Holistic Health View and the System Modeling Path of Proactive Medicine
Yucong Duan
International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)
World Academy for Artificial Consciousness(WAAC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
Introduction
Contemporary medicine is undergoing a paradigm shift: from a passive, disease-centered "passive medicine" to a health-maintenance-oriented "proactive medicine." So-called Proactive Medicine emphasizes the active participation of individuals in health management and the positive assistance of intelligent systems. By integrating cognitive science, artificial intelligence, and systems engineering, it achieves global control and forward-looking intervention for health. The core of this new paradigm lies in two closely related pillars: the Cognitive Channel Structure and the Multi-System Coordination Mechanism. The cognitive channel structure refers to the intelligent cognitive architecture in proactive medicine that runs through data acquisition, semantic decoding, decision generation, and feedback adjustment. It ensures that health-related information can flow efficiently and be endowed with meaning among the human body, artificial intelligence, and the environment. The multi-system coordination mechanism refers to the establishment of a synergistic interaction network between the mind-body systems within an individual, between human and artificial intelligence systems, and between the multi-level systems of individual-society-environment, thus forming a "individual-system-environment" three-dimensional synchronous health promotion pattern. The cognitive channel structure provides the intelligent "brain" and semantic bridge for proactive medicine, while the multi-system coordination mechanism constructs the collaborative "body" and ecological environment for proactive medicine. The two complement each other, transforming health management from isolated medical behaviors into a continuous evolutionary process of an organic whole.
This article will systematically explore the theoretical connotations and implementation paths of the cognitive channel structure and multi-system coordination mechanism of proactive medicine. First, we will clarify the theoretical foundation of proactive medicine, including the philosophical origins of the holistic health view, the principle of negative entropy, and the dynamic reshaping of the concept of health. Second, we will analyze the multidimensional cognitive system architecture in proactive medicine, introduce core models such as DIKWP and their "input-decoding-response-adaptation" cognitive process, and discuss the positioning, structure, and model evolution capabilities of the cognitive mechanism. Subsequently, we will focus on the multi-system coordination mechanism, explaining how to achieve collaboration at different levels, such as physiological-psychological, human-machine, and individual-social, and the principle of how the coupling of information fields and energy fields maintains health and order. Then, we will explore the semantic-layer collaborative model, that is, how the semantic/Purpose layer in proactive medicine acts as a "consensus space" for multi-agent collaboration, achieving human-machine intelligent interaction and system co-governance through value alignment and semantic isomorphism. The article will integrate new developments in related fields in the past five years, such as AI cognitive models, consciousness field theory, system collaboration technology, and human factors engineering, to provide interdisciplinary perspective support for proactive medicine. For example, we will cite research on reflective architectures of AI in healthcare, the enlightenment of consciousness field theories (such as electromagnetic consciousness field theory) for mind-body unity, the practice of multi-agent collaborative epidemic prevention, and human-centric digital health design experience, to enrich the realistic basis of proactive medicine theory. The total word count exceeds 30,000 characters, with a rigorous structure and rich content. It aims to demonstrate the systematic constructability, foresight, and cognitive philosophical depth of proactive medicine theory, providing a useful academic reference for deepening this emerging medical paradigm.
Theoretical Foundation: The Holistic View of Health and the Rise of Proactive Medicine
A Multidimensionally Integrated Philosophical View of Health. One of the theoretical cornerstones of proactive medicine is the reshaping of the concept of "health," expanding it from a single biological perspective to a holistic health view integrating physiological, psychological, social, and ecological dimensions. As early as the late 20th century, the World Health Organization (WHO) emphasized in the Ottawa Charter (1986) that health is not merely the absence of disease, but a state of complete physical, mental, and social well-being. This concept broke through the traditional narrow definition of "health as the absence of disease," promoting the formation of the bio-psycho-social (BPS) medical model, which is hailed as a humanistic return for the medical model. The BPS model emphasizes that psychological and social factors are as important as biological factors, laying the ideological foundation for proactive medicine: prevention is better than cure, patient active participation, and social support and mental health play key roles in disease outcomes. For example, the rise of psychology has brought attention to depression, anxiety, etc., and psychological intervention has been included in comprehensive disease management; social medicine has revealed the profound impact of socio-economic factors on health, prompting governments to promote health through public health and policy means. These developments nurtured the concept of "proactive health": encouraging individuals to enhance their self-healing power through psychological adjustment and to improve health behaviors with the help of community support. It can be said that the holistic view of health requires transcending the mind-body dualism, viewing humans as a whole embedded in natural and social networks. This has long been reflected in Eastern and Western traditions: Daoism advocates "unity of man and nature" and "unity of mind and body," asserting that the human mind and body are inseparable and that one should nourish life by conforming to natural laws; the Western philosopher Spinoza proposed "mind-body monism," believing that mind and body are just two attributes of the same substance, and the human mind-body and the external environment are unified in nature. Spinoza emphasized that emotions connect the mind and body as one. For example, negative emotions such as fear weaken a person's ability to act, while positive emotions such as joy enhance mind-body vitality, foreshadowing the mechanism of mutual influence between psychology and physiology. These cross-cultural philosophical thoughts jointly point to: The root of health lies in the unity of mind and body, and the harmony between man and nature. Modern medicine is precisely drawing on this essence, calling for breaking through the physiological-psychological-spiritual-environmental dimensions, and understanding individual health within the larger ecological and social system. Proactive medicine inherits and develops this holistic view, using digital technology and artificial intelligence to more effectively achieve "mind-body-environment" integrated health management, which not only echoes the Eastern realm of "unity of man and nature" but also fits the Western rational spirit of "mind-body parallelism."
The Negative Entropy Nature of Health and Order Maintenance. In addition to humanistic and philosophical foundations, proactive medicine also introduces perspectives from information theory and thermodynamics to understand health. The physicist Schrödinger once proposed that "life feeds on negative entropy," pointing out that organisms combat their own tendency toward entropy increase by ingesting ordered energy from the environment. Norbert Wiener, the founder of cybernetics, also emphasized that "information is order," and an increase in information means a reduction in uncertainty (entropy reduction). Drawing on these ideas, proactive medicine elevates the highest goal of medicine from eliminating local lesions to maintaining the low-entropy, high-order state of the life system. That is to say, health is regarded as a dynamic process of negative entropy inflow: by continuously inputting ordered energy and useful information, the structure and function within the life system can be maintained in a coordinated and stable manner. Conversely, disease means that order at a certain level has been destroyed, and entropy has risen sharply. For example, during a febrile convulsion, the body loses a large amount of energy as disordered heat. The energy field is agitated but lacks effective command from the information field, and physiological functions are disordered; in depression, physiological metabolism is often normal (no significant abnormality at the energy level), but the brain's information field is filled with negative cognition (information overload), leading to the disorder of psychological regulation. These phenomena show that health is essentially the coordinated order of the information field and the energy field, while disease corresponds to the misalignment of information-energy coupling. Based on this, proactive medicine proposes the information-energy consistency principle as a principle for evaluating health: when the information flow of cognitive regulation and the energy flow of physiological operation are matched and coordinated, the system is in a low-entropy healthy steady state; if the two are disconnected, the entropy value soars, and the body falls into disorder. Researchers also provide support from the perspective of the free energy principle: biological systems will spontaneously adjust to minimize the difference between internal models (information) and external stimuli (energy), i.e., to reduce "free energy" or entropy. The brain continuously corrects predictions to match sensory input, which is reducing information entropy and maintaining consistency between cognition and reality. Similarly, in medical treatment, the health model of the doctor/AI can be regarded as the "conceptual field," and the patient's physiological and psychological state as the "semantic field." Only when the conceptual prediction matches the semantic feedback (information and energy are synchronized) is the doctor-patient system in a low-entropy healthy balance; once the two are inconsistent, "semantic tension" will be generated, which needs to be realigned through intervention or model updates. Therefore, proactive medicine takes reducing entropy increase and introducing negative entropy as its core strategy, and all links from prevention, diagnosis to treatment revolve around restoring systemic order. In prevention, by inputting negative entropy continuously into the life system through healthy lifestyles such as balanced nutrition and rich mental activities, the probability of disease occurrence is reduced; in diagnosis, by using big data and AI to capture entropy increase signals in time, early intervention prevents minor chaos from turning into major chaos; in treatment, whether it is drugs, surgery, or psychotherapy, their essence is to inject negative entropy into the system and rebuild order. Thus, the principle of negative entropy unifies different medical methods under one framework: clinicians, public health experts, psychological counselors, and even AI algorithm designers can all regard their respective work as part of participating in "life entropy management." Proactive medicine, with this as its core concept, has achieved a paradigm revolution from "lesion-centric" to "entropy-reduction-oriented," providing a program for building an open health system and intelligent intervention paths.
The Dynamic Evolution and Adaptive Capacity of Health. Proactive medicine focuses not only on the cross-sectional state of health but also emphasizes the dynamic evolutionary characteristics of health in the time dimension. In recent years, scholars have proposed that health should be regarded as an ability to adapt and self-manage, rather than a static ideal state. Huber et al. (2011) advocated defining health as the resilience and adaptability of an individual in the face of physical, psychological, and social challenges. This dynamic view highlights the process attribute of health, rather than a simple "healthy/sick" dichotomy. This concept has been gradually accepted by the international medical community and inherited by proactive medicine: health is no longer an end-point of "perfect and flawless" at a certain point in time, but a series of processes of continuously maintaining balance and adapting to changes. Specifically, proactive medicine advocates for the establishment of an evolutionary health indicator system to reflect the changes in an individual's health trajectory over time. Traditional assessment often relies on one-time physical examinations or single test results, while proactive medicine focuses on trends and rates of change. For example, if a chronic disease patient's blood pressure decreases year by year through lifestyle adjustments, even if it has not yet fully reached the standard, it indicates that their health trajectory is evolving in a positive direction and should be encouraged; conversely, if a person's lab values are still within the normal range, but the indicators have been continuously deteriorating for many years, it should be regarded as a sub-health warning sign, and intervention should be timely. To capture these dynamic characteristics, proactive medicine advocates the use of wearable devices, home IoT, etc., to achieve long-term continuous monitoring of vital signs and behaviors, and combined with AI analysis to generate personalized health curves and predictive models. For example, heart rate variability (HRV) as a dynamic indicator can reflect the balance of sympathetic/parasympathetic nerves and is regarded as one of the objective measures of mind-body consistency; the "healthy life expectancy" indicator combines length of life with quality of life, which better reflects the evolution of health status with age. At the same time, health indicators need to consider individual differences and context: since everyone's genetic background and lifestyle are different, the baseline and ideal state of health are not uniform. Therefore, indicators should have personalized settings and can be adjusted as individuals grow and the environment changes. This is similar to the application of precision medicine in preventive health care—viewing health as an optimization process of an individual on their own trajectory, rather than a one-size-fits-all uniform standard. Finally, proactive medicine integrates ideas from evolutionary biology and complex systems science to view health: health maintenance mechanisms themselves are continuously perfected in biological evolution (such as the evolution of the immune system, stress response), and an individual's health capacity in their lifetime also changes through learning and adaptation. Therefore, medical intervention should be dynamically adjusted according to different life stages and environmental conditions. For example, childhood focuses on growth and development support, adulthood emphasizes balancing work and life to slow down entropy increase, and old age focuses on maintaining function and social participation to delay decline. Similarly, when the health level of the whole society improves, the health standard may rise accordingly—this means that the health evaluation system itself also needs to continuously evolve to reflect new scientific cognition and public expectations. Ultimately, proactive medicine advocates for an "evolutionary view of health," continuously correcting our understanding and measurement of health through interdisciplinary integration and long-term data accumulation, thereby guiding medical practice from passive disease treatment to active maintenance and improvement. This concept coincides with the emerging "One Health" and "Planetary Health": viewing human health, animal health, and ecological environment health as an interdependent whole. For example, climate change may trigger new infectious disease risks, and ecological damage will harm human well-being. Therefore, proactive medicine calls for medical workers to cooperate with fields such as environmental protection and agriculture to create a macro environment that supports health, and to consolidate the foundation of population health by reducing the entropy increase of social and natural systems. This further expands the vision of proactive medicine: health is not just a medical problem, but an indicator and goal of civilizational evolution, and a value that needs to be jointly guarded by the whole society.
In summary, the theoretical foundation of proactive medicine integrates philosophical wisdom from ancient to modern, East and West (mind-body monism and unity of man and nature), modern scientific principles (information entropy and negative entropy), and the latest health concepts (multidimensional dynamic health definition). It aims to "maintain the order of the life system," and regards health as a co-evolutionary process of the mind-body system in multiple environments. This theoretical depth ensures that proactive medicine is not just a combination of technologies, but a new medical paradigm with a systematic and forward-looking nature. On this basis, proactive medicine constructs its own cognitive models and system frameworks. The following will provide an in-depth analysis of its cognitive channel structure and multi-system coordination mechanism.
Analysis of the Multidimensional Cognitive System of Proactive Medicine
For proactive medicine to achieve active health management, the primary task is to build an intelligent cognitive system capable of semantically interpreting complex health data, making decisions and reasoning, and continuously self-evolving from feedback. To this end, researchers have proposed new cognitive architecture models, such as the DIKWP model, to support the intelligent core of proactive medicine. DIKWP is an extension of the classic "Data-Information-Knowledge-Wisdom (DIKW)" cognitive hierarchy, adding a "Purpose" layer at the top, emphasizing the importance of clear goals and value orientation in the cognitive process. Under this model, cognition is no longer simplified as bottom-up linear information processing, but is characterized as a multi-level networked interaction structure: Data (D) is processed into Information (I), then rises to Knowledge (K) and Wisdom (W), and is finally commanded by the Purpose (P) layer; at the same time, the flow between these five layers is not unidirectional, but involves bidirectional flow and feedback. Each layer may affect other layers, forming a total of 5×5=25 potential interaction paths. For example, high-level Purpose can guide the collection and screening of low-level data, and conversely, new data can also prompt the update of high-level hypotheses; the Knowledge and Wisdom layers can iterate and enrich each other, and so on. This networked cognitive channel structure allows information and energy (corresponding to cognitive content and physiological behavior) in a complex system to be flexibly coupled at different levels, forming a dynamic collaborative relationship. What is particularly critical is that the top-level "Purpose" acts as the value anchor and decision-making hub for the entire cognitive process: it represents the ultimate goal or motivation the system is trying to achieve, and provides semantic guidance for the cognitive activities of the lower layers. In the context of proactive medicine, this Purpose can be specified as the value pursuit of promoting health, such as "improving quality of life" or "preventing serious diseases." The Purpose layer ensures that every decision made by AI is consistent with the patient's fundamental health goals, preventing the intelligent system from falling into the trap of only pursuing indicator optimization while deviating from human-centric needs. As some scholars have pointed to, with the introduction of the Purpose layer, AI no longer just passively outputs predictions or classification results, but can proactively consider the Purpose behind the information, thereby avoiding falling into meaningless correlation calculations. For example, if ethical rules (such as "do no harm to the patient," "respect patient privacy") are pre-implanted in the Purpose layer, then AI will consider these rules as the highest priority when performing specific tasks. If a health management AI recognizes that a patient is unwilling to share a certain piece of sensitive data, this preference can be encoded as a constraint condition at the Purpose layer, and the AI will then comply with the patient's wishes and will not forcibly require data upload. This actually establishes an embedded ethical weight mechanism: by clarifying value criteria at the top of cognition, boundaries are set for all lower-level processing of the system, so that the autonomy of AI always serves human values and safety needs. From this perspective, the DIKWP model, through the Purpose layer, integrates human purpose and ethics into the AI cognitive process, providing the "moral code" and "safety guardrail" for proactive medicine AI. This design concept fits the current "Trustworthy AI" principles and also reflects the core idea of proactive medicine that emphasizes technology for good and human-centricity.
Under the DIKWP framework, the cognitive channel structure of proactive medicine runs through the entire process from raw data acquisition to intervention decision generation, and achieves self-optimization through multi-layer semantic interaction. Its working mechanism can be summarized as a four-stage circular closed loop of "Input—Decoding—Response—Adaptation": (1) Input: Multi-source heterogeneous health data from wearable devices, biosensors, electronic medical records, and environmental monitoring continuously flow into the system, forming the information input channel for proactive medicine. This data covers physiological indicators (heart rate, blood pressure, etc.), behavioral patterns (activity, sleep, etc.), as well as emotions and subjective feelings (obtained through app feedback or dialogue systems), providing "sensory" signals for the cognitive system to perceive the world. (2) Decoding (Semantic Understanding): AI analyzes the raw data at multiple levels, extracting meaningful information and knowledge. This process is equivalent to translating "signals" into "semantics." In the DIKWP model, the decoding process proceeds along the D→I→K→W path, but it is not a simple straight line; it forms complex reasoning by combining technologies such as machine learning and knowledge graphs. For example, AI identifies patterns from continuous glucose monitoring data and summarizes them into knowledge (e.g., post-meal blood sugar peaks are related to certain dietary habits); then, combined with rules at the medical wisdom level, it judges whether there is a health risk. Furthermore, the system will refer to the Purpose layer (e.g., the patient's goal of controlling blood sugar) to conduct a value assessment of the decoded wisdom to decide which issues to focus on. At this stage, AI achieves semantic modeling of the health state: that is, building a digital health image of the patient, including the current situation, trends, and the underlying Purpose-driven factors. (3) Response (Decision and Execution): Based on the wisdom obtained from semantic decoding and the patient's health Purpose, AI generates personalized intervention suggestions or directly triggers automated actions. This is equivalent to the reflex and decision response made by the cognitive system to the external environment. Proactive medicine particularly emphasizes the timeliness and semantic fit of this link: the suggestions must not only be medically effective, but also presented in a way that the user can understand and accept. For example, when detecting a rise in the user's blood pressure, the system may immediately remind the user to breathe relaxedly through a smart watch vibration and voice reminder (immediate reflex response), while providing a personalized suggestion: "A 10-minute walk in the afternoon will help lower your blood pressure. You have already walked 5000 steps today, keep it up!"—This suggestion might be combined with the user's previous feedback (perhaps the user emphasized wanting family-related motivation), so AI might add a sentence: "Good blood pressure is helpful for you to accompany your family for a long time" to enhance the sense of meaning. Such a response not only solves the immediate problem (lowering blood pressure), but also motivates the user to work towards their long-term health Purpose. (4) Adaptation (Feedback and Adjustment): The highlight of the proactive medicine cognitive system is its self-monitoring and regulation capabilities. Every time AI makes a suggestion and implements it, the system will evaluate the effect based on subsequent feedback and adjust its own model and strategies if necessary. For example, if AI suggests the user walk 8000 steps a day, but after a period of time, it monitors that the user has not reached the goal and feeds back "cannot complete due to overtime," the system will realize that the original assumption (the user has enough time to exercise) does not match reality, and "semantic tension" has arisen. At this time, AI enters the adaptation stage through the feedback loop: it automatically reduces the target steps to a more realistic 5000 steps, or changes to reminding to use fragmented time for exercise, and records "busy at work" as a new user characteristic for future reference. In this process, AI is not mechanically adjusting values, but conducting self-renewal at the context and conceptual level—it is equivalent to adding a new semantic node of the user's life pattern to the knowledge base, and replanning the intervention plan. In this way, the next time the system makes a decision for this user, it will automatically consider the factor of their high work pressure, avoiding making unrealistic plans again. Through continuous feedback calibration, the cognitive system can progressively evolve more effective intervention strategies that understand the user better, which is equivalent to having a "gets smarter with use" learning ability.
It is worth noting that the above cognitive closed loop requires AI to have a certain metacognition ability, that is, the ability to review and learn from its own cognitive process and decision-making effectiveness as new information. Current research has begun to explore implementation plans in this regard: the "dual-loop DIKWP" architecture proposed by Duan et al. is a typical example, allowing the intelligent agent to feed its own output back as input, iterating in a loop to achieve self-regulation. In the health management scenario, this means that AI can compare the effects of suggestions at different stages. If the expected Purpose is not achieved continuously, it will trigger in-depth analysis, calling on expert systems or additional data to replan the path. This mechanism prevents AI from falling into simple repetition, but prompts it to continuously improve. For example, if AI's interventions fail to lower the user's blood sugar multiple times, it will realize that it may have overlooked some hidden factors (such as cultural dietary preferences), and then adjust the strategy: for example, providing healthy recipes that suit the user's taste, rather than blindly prohibiting certain types of food. Through this dual-loop feedback, AI gradually cultivates the ability of self-reflection and error correction, similar to the process of human doctors summarizing experience and lessons.
In summary, proactive medicine, through models like DIKWP, constructs an intelligent system with cognitive emergence, rapid reflection, and autonomous regulation capabilities, making AI seem to have a life-like proactive intelligence. These three capabilities complement each other: Cognitive emergence endows the system with the creativity to autonomously discover new patterns and knowledge from massive data; Reflection endows the system with immediate response to known risks and basic safety guarantees, such as quickly alarming or intervening when a critical situation is detected; Regulation provides a mechanism for continuous learning and optimization, ensuring that the system continuously perfects its model and maintains robustness in long-term operation. The fusion of the three enables AI to show the prototype of "proactive consciousness." Studies show that deep learning technology has allowed AI to show signs of cognitive emergence in some aspects. For example, auxiliary diagnosis systems discovering early signs from medical images that are difficult for human eyes to detect is equivalent to AI autonomously emerging with new diagnostic cognition; some immediate response systems (such as in-vehicle health monitoring automatically braking) embody reliable reflection mechanisms. It is foreseeable that with the integration of AI technologies from multiple fields into medicine, "cognitive emergence-reflection-regulation" will increasingly become a standard configuration for intelligent health systems, making them truly possess proactive intelligence. In such a future scenario, medical AI will no longer be a passive tool, but more like a digital life that constantly grows and learns, working shoulder to shoulder with doctors and patients to jointly promote the realization of health goals. As envisioned, an ideal AI health butler might autonomously analyze the owner's physical and mental data every day, emerge with some personalized insights, provide help with reflective reminders in a timely manner, and adjust its services based on the owner's feedback, so that the owner's health steadily improves with few unexpected crises.
Interdisciplinary research in recent years has also confirmed and enriched the design of the proactive medicine cognitive system from another aspect. For example, in 2025, scholars proposed a basic architecture for medical AI agents, emphasizing the need to introduce Plan, Action, Reflection, Memory four components into AI. Among them, the "Reflection" module allows AI to evaluate its own decision-making results, analyze errors, update models, and combine patient feedback to improve subsequent decisions. This coincides with our aforementioned concept of metacognitive regulation. The "Memory" component supports AI in accumulating and calling on past interaction experience, making system decisions increasingly personalized. This architecture has been preliminarily applied in medical dialogue agents. For example, a new generation of intelligent voice assistants, through the synergy of the four modules, can integrate electronic medical record data in chronic disease management to make real-time decisions, and learn and improve after each interaction. Another example, in the fields of affective computing and human factors engineering, researchers are committed to letting AI recognize human emotions and cognitive patterns, so as to adjust interaction strategies. This helps our cognitive system to better fit human psychological needs—just as proactive medicine emphasizes, health suggestions should "understand you" rather than just "calculate." The current development of Large Language Models (LLMs) provides powerful tools for this goal: the human-like depth shown by LLMs in semantic understanding and generation makes it possible for AI to grasp the subtle meanings and emotional colors in human language, thereby participating in health communication in a more humane way. It is foreseeable that in the future, the intelligent assistants of proactive medicine will not only be able to calculate the optimal plan, but also motivate users with an empathetic tone and persuade users with reasons close to their values, which will greatly improve the warmth and effectiveness of the health management process.
In summary, the cognitive channel structure of proactive medicine, with the DIKWP model at its core, achieves a deep understanding and utilization of health information through a multi-layer semantic network. It integrates cutting-edge methods of artificial intelligence, endowing the system with the capabilities of autonomous innovation, immediate response, and continuous learning. In this cognitive architecture, the "Purpose" layer runs through, ensuring that technology always serves human health values; and the introduction of the feedback loop gives the system the driving force for improvement, similar to self-awareness. This cognition-driven proactive intelligence lays the foundation for a multi-system coordination mechanism: only when the system can "understand" the multidimensional semantics of health and integrate decisions into a value framework, can it further promote the collaborative operation of systems at different levels. The next section will turn to the multi-system coordination mechanism of proactive medicine, to see how the cognitive system intertwines with physiological systems, artificial systems, and social systems to jointly promote the realization of health goals.
Cross-System Coordination Mechanisms: Mind-Body Coupling and Human-Machine Ecological Collaboration
The "proactive" nature of proactive medicine is not only reflected in intelligent decision-making at the cognitive level, but also in the collaborative participation of multi-level systems. Health is by no means isolated in a certain organ or a certain person, but simultaneously involves multiple systems within the human body and the interaction between humans and the environment, and humans and technology. Therefore, proactive medicine pays great attention to the establishment of a multi-system coordination mechanism, ensuring that systems at all levels, from cells to organs, from individuals to society, can exert force synchronously and mutually reinforce each other, to maintain and promote overall health.
Consistency Coordination of Mind-Body Systems. First, within the individual, the coordinated unity of the physiological and psychological (cognitive) systems is an important prerequisite for health. Proactive medicine vividly elaborates the mind-body coupling mechanism through the "information field-energy field" model. The "energy field" represents the material and energy processes of human life activities, such as metabolism, nerve electrical signals, muscle movement, etc.; the "information field" refers to the collection of all meaningful information inside and outside the human body, including gene expression, nerve signal transmission, cognitive activities, and social interaction, i.e., health-related semantic content. The state of health is essentially the dynamic balance and abundance of the information field and the energy field. The two work together in an orderly manner to support the body's adaptation to the environment and its continued survival. The principle of information-energy consistency can be understood from multiple levels: At the evolutionary physiological level, organisms have evolved mechanisms for coordinating information processing and energy utilization. For example, when encountering a threat, the "fight or flight" response is triggered: the brain quickly assesses the danger (information processing), and the sympathetic nervous system simultaneously releases adrenaline to mobilize body energy (energy mobilization), and the mind and body enter a highly alert synchronized state to cope with external challenges. At this time, information and energy are consistent. Although tension temporarily increases entropy, it is conducive to survival within a controllable range. Conversely, if cognition and energy are imbalanced, functional inefficiency or even pathological phenomena will occur. For example, anxious patients are continuously overly alert in a safe environment (information overload) but have no channels for energy release, leading to symptoms such as palpitations and sweating, which consumes the mind and body in the long run; depressed patients often fall into negative ruminative thinking (information field disorder) while having insufficient physiological activation (low energy), manifesting as slow movement and fatigue. These show that health requires cognition and energy mobilization to be adaptive: the brain is not overly tense, the body is not overly consumed, and the mind-body rhythm maintains a synchronized pace of tension and relaxation. At the daily routine level, information-energy consistency is embodied in the matching of physiological rhythms and psychological rhythms. Such as the circadian rhythm: when the light is dim at night, the pineal gland of the brain secretes melatonin to make people sleepy (information regulation), the metabolic rate decreases, and the body temperature drops (energy regulation), and the mind and body jointly enter the rest mode. If the circadian rhythm is reversed for a long time or sleep is deprived, the brain's cognitive rhythm and the body's metabolic rhythm will lose synchronization, which will not only impair memory and attention, but also cause high-entropy phenomena such as abnormal hormone levels and glucose and lipid metabolism disorders. Therefore, maintaining a regular work and rest schedule, so that the brain's information flow and the body's energy flow operate consistently according to the rhythm of the biological clock, is the key to maintaining health. Similarly, diet and exercise also require balance: nutritional intake (energy acquisition) should match the amount of activity (energy consumption), and the brain needs to regulate the sense of satiety and metabolic rate according to the state of eating/exercise (information feedback). If one overeats and lacks exercise, energy intake far exceeds consumption, and the brain's reward mechanism is disturbed by high sugar and high fat (information misalignment), the result is a high-entropy disordered state such as obesity and its complications. Conversely, through mindful eating (focusing on sensing dietary signals) and moderate exercise, achieving cognitive moderation and physical consumption, and synchronizing energy balance with information satisfaction, the system's entropy value is kept at a low steady state. At the medical intervention level, the principle of information-energy consistency suggests that we need to simultaneously evaluate the comprehensive impact of treatment measures on the body and mind. For example, chemotherapy killing cancer cells is a typical strong energy field intervention, but the patient's brain's information field often bears huge pressure (fear of side effects, anxiety about recovery). If information-level support (such as psychological counseling, pain management education) is not provided, the patient's cognitive tension will aggravate physiological adverse reactions (nausea, pain sensation magnified), reducing the treatment effect. Studies have shown that giving cancer patients sufficient information support and psychological intervention can significantly improve their treatment tolerance and quality of life. This is precisely enhancing the orderliness of the information field (reducing fear, building faith) to cooperate with high-intensity energy field interventions, so that the two achieve consistency, to obtain better curative effects. Similarly, in surgical rehabilitation, the patient's confidence and proactiveness (information factors) are closely related to their physiological recovery (energy factors): a positive and optimistic attitude is conducive to immune and endocrine functions, and faster wound healing; repeated negative worries will increase stress hormones and inhibit the recovery process. These all confirm the importance of "mind-body co-regulation" for rehabilitation—proactive medicine therefore incorporates psycho-social support into standard treatment paths. Each intervention considers both the effect on the body and the impact on the patient's psychology, striving for coordinated mind-body measures to optimize the overall curative effect.
Through the above analysis, it can be seen that proactive medicine, at the individual level, advocates for synchronous regulation of body and mind, information and energy, to reduce internal conflicts and entropy increase from the source. This provides guidance for clinical practice: any measure that helps to enhance mind-body synchronization and reduce internal contradictions in the system is positive and effective; any practice that exacerbates the disconnection between the two or introduces new conflicts needs to be cautious or even discarded. The principle of information-energy consistency runs through multiple dimensions of proactive medicine, from personal lifestyle management (e.g., balancing diet and exercise with psychological stress reduction), clinical multidisciplinary treatment plans (treating both mind and body), to human-machine interaction design (considering the user's physiological endurance and psychological acceptance), all are guided by this. This reflects the value concept of proactive medicine of "human-centric, mind-body integration," and also balances medical technology application with humanistic care, and cutting-edge innovation with ethical guarantees. It can be said that the mind-body coordination at the individual level is the foundation of the proactive medicine edifice. Only when the individual achieves internal harmony can we talk about the more macro human-machine and social collaboration.
Collaborative Symbiosis of Human-Machine Systems. Modern health management is inseparable from the participation of technology systems. Proactive medicine particularly emphasizes human-machine collaboration: letting artificial intelligence and other digital systems complement and mutually reinforce human capabilities, thereby jointly constituting an efficient health support network. In the proactive medicine framework, the key to human-machine collaboration lies in ensuring that the AI's cognitive "conceptual field" and the patient's physiological "semantic field" remain consistent. Simply put, the decision-making suggestions provided by AI must fit the patient's actual physiological state and ability status to be truly effective. Otherwise, no matter how advanced the algorithm, if it is detached from real conditions, it will be difficult to improve health. For example, if AI formulates an overloaded exercise plan regardless of the patient's current physical condition, it is equivalent to a serious disconnection between information output and energy realization. The result is likely that the patient cannot execute it, and the intervention is fruitless. Ideally, AI should provide executable plans based on the patient's real-time physiological data (energy field information) and living environment data, and continuously adjust the model by obtaining feedback through interaction with the patient, to keep AI's conceptual cognition and the patient's semantic state synchronized. There have been practical attempts in this regard: for example, a digital management system for diabetes combines continuous glucose monitoring (energy metabolism data) and patient diet and exercise logs (behavioral information) to dynamically adjust personalized diet and medication suggestions; if it detects that the patient has not followed the suggestions (such as an abnormal blood glucose curve), the system will capture this deviation and update the subsequent plan. This forms a closed loop, ensuring that AI decisions are synchronously adapted to the patient's real state, effectively reducing the incidence of dangerous hypoglycemia or hyperglycemia events. It can be seen that in the human-machine collaborative system built by proactive medicine, AI is not only a tool for doctors, but more like a "digital avatar" of the patient, which can monitor and analyze 24/7, and can also understand the patient's situation and give feedback with warmth. Doctors, AI, and patients often form a closed-loop network: AI interacts with patients daily and provides interventions; doctors regularly review AI's analysis, discuss plans with patients, and give professional opinions; patients feed back their own feelings. The three parties, through their respective advantages (AI's data processing and continuous companionship, the doctor's clinical experience and humanistic care, the patient's subjective willingness and cooperation), form a multi-level semantic collaborative network. When this network operates well, the patient's personal health cognition and values (personal semantics) are aligned with the medical conceptual framework jointly established by humans and machines, which is the so-called semantic isomorphism or ethical consistency, and the entire system can maintain low-entropy, high-efficiency operation. Conversely, if the patient misunderstands or disagrees with the AI/doctor's suggestions, the semantic chain breaks, and information asymmetry leads to execution deviation, and the effect of health intervention is greatly reduced. Therefore, maintaining semantic consistency between humans and machines, and between doctors and patients, is one of the core challenges of human-machine collaboration. Proactive medicine takes various measures in technology and process to ensure this: for example, introducing ethical and Purpose constraints when AI suggestions are generated (the application of the DIKWP Purpose layer mentioned earlier), to ensure that the suggestions are consistent with the patient's ultimate health Purpose and values; in the interaction interface, enhancing the affinity and explainability of AI's expression through natural language dialogue, affective computing, etc., so that patients can "understand and trust" AI's guidance; in the workflow of the medical team, introducing the emerging "doctor-patient-AI tripartite responsibility sharing" model, letting doctors supervise and make case-by-case adjustments to AI suggestions, while training patients to gradually integrate into this triangular collaboration, so that the three parties form a transparent and trusting partnership. These explorations echo current research in human-computer interaction and human factors engineering: for example, a review in Nature pointed out that for a human-AI combination to achieve complementary synergy, the key lies in establishing a reasonable division of labor and mutual trust mechanisms, including
letting AI explicitly state its uncertainty and letting humans retain final decision-making power. Proactive medicine, through conceptual and institutional design, makes AI always act as an assistant to the doctor and a coach to the patient, rather than a dictator or a black-box decision-maker. This not only gives play to AI's strengths (massive data analysis, 24/7 service), but also avoids the substitution and alienation of humans by over-automation, truly realizing a collaborative model of human-machine symbiosis.
Collaborative Governance of Individual–Social–Environmental Systems. Health is not only determined by individual choices; social support and environmental conditions also play important roles. Proactive medicine therefore advocates for embedding individual proactive health management into a broader social and ecological collaborative network, and creating an external environment conducive to health through the cooperative co-governance of multiple subjects. At the family and community level, proactive medicine emphasizes the supportive role of the "micro-environment": the understanding and cooperation of family members, and the supply of community health resources (such as fitness facilities, health education activities) can help individuals better adhere to healthy behaviors. Some studies have shown that family-style interventions (such as the whole family participating in a healthy diet plan) are more effective than isolated individual actions, because it creates group tacit understanding and atmosphere, reducing the psychological cost for individuals to change behavior. Community mutual aid (such as chronic disease patients forming support groups, neighbors doing morning exercises together) has also been proven to improve adherence to medical advice and physical and mental pleasure. Based on this, proactive medicine proposes the concept of building a "Health Behavior Feedback Field," coupling individuals' health actions with the community environment and social support, to form a benign circular field. For example, digital health platforms can be combined with social functions to promote the sharing of exercise results and mutual supervision among community members, achieving group reinforcement of healthy behaviors (similar to the incentive effect of step count rankings in social media feeds). At the same time, the platform aggregates overall community data, forming a health feedback field for social interaction: if it is found that the overall activity of a certain community is declining, the system will issue a reminder and push initiatives for group activities, thus improving the individual and the community as a whole.
At the government and public policy level, proactive medicine calls for perfecting policy support and cross-departmental collaboration, to reduce the threshold for individuals to maintain health and increase the availability of healthy choices from an institutional level. For example, the government can subsidize digital health intervention products, include proven health apps in medical insurance payments, so that the public can access AI health services for free or at low cost; promote joint implementation of "proactive health" plans by departments such as medicine, sports, education, and technology, increase pedestrian-friendly facilities in urban planning, strengthen nutrition labeling and restrict high-sugar, high-fat foods in food supervision, etc., to shape a "default healthy" social pattern at the environmental source. These measures are essentially "health by default" or "behavioral nudges" at the social level, allowing citizens to make healthier choices unconsciously. Policies can also promote proactive intervention through economic levers: for example, encouraging insurance companies to carry out health management services, and giving premium discounts based on customer health improvement, thereby driving the public to actively participate in health management and forming a win-win situation. At the same time, proactive medicine advocates for transforming hospitals into "health promotion centers" and not just "places for treating diseases," encouraging the medical service system to expand from passive treatment to active prevention. Examples include setting up health management clinics, doctors regularly going to communities to guide prevention, etc., which are concrete steps to achieve the integration of medicine and prevention. Through the above multi-level institutional arrangements, proactive medicine aims to build a "tripartite" health promotion network: that is, Individual-System-Environment exert force simultaneously. At the individual level, cultivate everyone's health literacy and proactiveness; at the system level, develop intelligent health platforms, wearable devices, and other technical means to provide individuals with refined support; at the environmental level, create a health-friendly social, cultural, and material environment as a bottom-line guarantee. The three complement each other: people are more likely to adhere to healthy behaviors with the assistance of a good environment and intelligent systems, and the increase in a healthy population in turn reduces the social medical burden and forms a positive health culture that feeds back to society. As proactive medicine pursues, "Health is Consensus," the whole society regards health as a common goal, governs collaboratively, and benefits collaboratively.
It must be pointed out that cross-system coordination also requires coordination at the consciousness level, which leads to the discussion of the semantic-layer collaborative model in the next section. In multi-subject co-governance, the values and goals of different roles may not be the same. How to ensure that everyone works towards a common health vision is a challenge at the governance level. For example, individuals pursue their own health benefits, the government focuses on public health indicators, and technology companies have commercial demands. If there is a lack of a unified value framework, the actions of various systems may be fragmented or even conflict with each other. Proactive medicine recognizes this, and therefore proposes the requirement of "value-Purpose-behavior" consistency in top-level design, and solves the collaborative ethical problems of cross-subjects through Purpose-layer alignment. As mentioned in the summary of Chapter 6, proactive medicine ultimately points to a new paradigm of proactive health characterized by "value alignment, semantic-driven, and collaborative co-governance." Among them, "value alignment, semantic-driven" means that a consensus field must be formed at the semantic and value levels, allowing multiple systems to collaborate on this basis. To this end, we need to introduce the concept of a semantic-layer collaborative model for further elaboration.
Semantic-Layer Collaborative Model: Value-Isomorphic Human-Machine Multi-Domain Co-Governance
In the complex collaborative network of proactive medicine, the "glue" that truly integrates all elements into one is collaboration at the semantic layer. The so-called semantic-layer collaborative model refers to the use of a shared meaning, Purpose, and value framework to unify the communication and cooperation between different systems (individual, AI, medical, social), so that they form a whole that resonates at the same frequency. This collaboration emphasizes the penetration of cognitive channels at the semantic/Purpose layer: only when humans and machines have a common understanding of health-related information, and when the behaviors of all subjects are guided by the same set of value criteria, can multi-system collaboration be truly efficient and harmonious.
Proactive medicine, in practice, has developed the concept of a Semantic Feedback Loop to achieve semantic-layer collaboration. This structure runs through the human-machine interaction closed loop described earlier: AI needs to understand the semantic meaning behind health information and feed back suggestions to the user in a meaningful way, thus forming a continuously improving closed loop. In short, the semantic feedback loop requires AI to be able to "read" the health status and trends represented by health data, and also to "speak" suggestions that users are willing to accept and act on. This is far more complex than just providing a numerical alarm or a rigid command, because different users have different cognitive preferences and value drivers. For example, for a user who values family, when AI suggests they lose weight, it can emphasize that "getting fit helps you accompany your family for a long time," while for a user who cares about work performance, it can explain that "improving health will boost your energy and focus." This semantic packaging, customized according to the user's values, can significantly improve the acceptability of the suggestions—the suggestions are no longer cold commands, but action guides that are meaningful and motivating to the user.
Semantic-layer collaboration is not only reflected in the expression of a single suggestion, but also runs through the dynamic adjustment of the entire interaction process. After the user starts to implement the AI's suggestions, the system will continuously monitor the results and adjust the strategy based on the feedback. This involves the other side of the semantic feedback loop: how AI updates its understanding of the user based on feedback. Continuing the previous example, if the user fails to complete the goal of 8000 steps a day due to overtime, the AI needs to interpret the semantics behind this—it means the original goal was unrealistic or did not consider the user's life context. AI regards this as the appearance of "semantic tension," i.e., the model's assumption (the user has time to walk) does not match reality. Through the feedback loop, AI self-corrects: it lowers the walking goal to a more realistic 5000 steps, or changes to suggesting weekend centralized exercise, and provides some tips for activities during work breaks to compensate for the lack of exercise on weekdays. It is worth noting that this adjustment is not a simple parameter change, but involves changes at the conceptual and contextual level: AI updates its understanding of the user's life pattern (marks the "busy at work" feature) and associates it with the health plan. In this way, the AI's cognitive model is deepened and becomes closer to the user's real context. A good semantic feedback structure should have adaptive and self-monitoring capabilities: the former means AI can automatically adjust suggestion parameters (such as exercise intensity, frequency, etc.) based on feedback, and the latter means AI can monitor the effectiveness of its own decisions, and proactively acquire new information or adjust the model when deviations are found. To achieve these, AI needs to have a certain metacognitive ability, such as the ability to reflect on its own cognitive process as mentioned earlier. The "dual-loop DIKWP" proposed by Duan et al. is an exploration of this kind, allowing the intelligent agent to feed its own output back as input, iterating in a loop to achieve metacognitive self-regulation. In the health suggestion scenario, this means that AI can compare the effects of its suggestions at different times. If the intended Purpose is not achieved continuously, it will trigger a deeper analysis, possibly calling on expert knowledge or more data to replan the path, avoiding falling into simple repetition. For example, if AI's interventions fail to lower the user's blood sugar multiple times, it will realize that it may have overlooked factors such as the user's dietary and cultural preferences, and then adjust the strategy: for example, providing healthy recipes that suit the user's taste, rather than blindly demanding the elimination of certain foods. This semantic-level adaptive learning makes the virtual health assistant "understand" the user more and more, and become better at promoting change in a way that the user can accept.
Another key point of semantic-layer collaboration is the semantic co-construction of human-machine collaboration. As mentioned earlier, in reality, a tripartite decision-making network of doctor-patient-AI is often formed. In this network, all parties contribute their own semantic information: the patient provides first-hand semantic field information such as symptoms and feelings, AI transforms it into a medical conceptual model, and the doctor provides medical knowledge concepts and value judgments. The two jointly act on the changes in the patient's behavior and state. This can be regarded as a multi-level semantic tensor network: semantics of different dimensions intersect in it. If this network operates well, the patient's personal semantics (understanding and belief in health) are aligned with the human-machine common conceptual framework, and the overall system entropy is maintained at a low level (health stability); once the patient does not understand or agree with the AI/doctor's suggestions, the semantic chain breaks, and asymmetric information leads to execution deviation, and the entropy value rises (health risk increases). Therefore, maintaining semantic isomorphism is one of the key principles for the success of the semantic feedback loop. It requires that whether it is AI algorithm output, doctor's explanation, or patient's self-understanding, they should all point to the same health meaning system and must not contradict each other. To this end, proactive medicine advocates for introducing ethical and value constraints when AI suggestions are generated—this is precisely the role of the highest-level Purpose in the DIKWP model: to ensure that the suggestions are consistent with the patient's ultimate Purpose and values. Only when the intervention suggestions are semantically consistent with the patient's inner Purpose will the patient truly follow them, and the semantic feedback loop can run smoothly. Conversely, if AI suggestions violate the patient's wishes or values (even if medically correct), they may be resisted or even trigger new psychological stress.
Through the above mechanisms, the AI suggestion system of proactive medicine can continuously improve its understanding of and service to the individual. Semantic-layer collaboration ensures that AI evolves from "being able to calculate" to "understanding you," meaning that AI can not only calculate a plan, but also knows how to communicate and guide in a way that the user can accept and persist. Health management thus becomes a process with warmth and interaction, not just cold data monitoring. From a more macro perspective, with the development of semantic AI such as large language models, machines will become better at understanding the subtle meanings and emotional colors in human natural language. In the future, the AI assistant of proactive medicine may be like an intimate coach, using the most appropriate words to motivate people towards the path of health. When AI can flexibly use semantic feedback to adjust itself, we can almost say that it has a kind of "digital rational self"—it not only follows health ethics and Purpose, but can also learn and evolve autonomously. Once this digital rationality matures, it will surely become an indispensable intelligent element in the proactive medicine system, helping everyone achieve long-term, continuous, and autonomous health improvement.
On a larger scale, proactive medicine proposes the rather forward-looking concept of a "Digital Health Consciousness Field," which can be seen as an extension of semantic-layer collaboration. The digital health consciousness field aims to capture, simulate, and utilize human health-related consciousness activities (emotions, cognitions, Purpose), forming a shared consciousness field between humans and machines. It can be understood as an individual's health "mental image" or "digital twin" in virtual space, which includes the individual's psychological state, behavioral tendencies, as well as health values, Purpose, and other information. Through digital twin technology and cognitive modeling, the system can reproduce and predict the individual's psycho-physiological dynamics, allowing AI to have a more comprehensive grasp of the person's "inner world." For example, VR therapy places users in specific virtual scenarios to practice emotional responses, which is equivalent to projecting the user's consciousness into a digital scenario for reshaping; brain-computer interfaces (BCI) can directly read brain signals and provide stimulation feedback, digitally extracting parts of consciousness activities, which are then analyzed by AI and fed back for regulation. These technical means allow human consciousness activities to gradually enter the digital field and couple with technical systems, blurring the boundaries between human-machine-nature. The digital health consciousness field is thus born: it is not just an auxiliary tool, but represents a paradigm upgrade of the medical model. In this paradigm, an individual's subjective content, such as emotions and beliefs, can be measured and influenced in digital form. Doctors and AI can understand the patient's inner world more deeply, so as to implement more refined psycho-physiological collaborative interventions.
The value of the digital consciousness field is not only at the individual level, but also reflected in group health management. In the future scenarios envisioned by proactive medicine, the digital health consciousness field guides intelligent intervention for group health through three key mechanisms: Perception—Purpose—Collaborative Decision: (1) At the Perception level, the digital consciousness field integrates extensive real-time data to "perceive" the group's health status and change trends. A ubiquitous sensing network composed of wearable monitors, smart home sensors, and public environmental monitoring continuously collects physiological indicators, behavioral patterns, and environmental information of individuals and groups. For example, in a smart city, the digital consciousness field can simultaneously capture citizens' activity levels, sleep status, dietary data, as well as community pollution indices, hospital visit counts, etc., from which to identify health patterns and deviations (such as finding that the number of people with insufficient exercise in a certain community is increasing month by month). (2) At the Purpose level, the digital consciousness field focuses on health-related Purpose and goals, and drives intervention decisions based on this. It deeply analyzes the health Purpose of individuals and groups: for example, inferring from behavioral data that a person's recent health goal is to lose weight or prepare for a marathon, or detecting the public's willingness tendency towards a certain health issue (vaccination, smoking cessation campaigns, etc.) at the group level. These health Purpose are regarded as high-level semantic signals, guiding the system's response strategies. If the perception layer finds a health deviation (such as an increase in unhealthy behaviors), the system will consider whether this is intentional or an unconscious result, and then formulate more targeted intervention measures. (3) At the Collaborative Decision level, the digital health consciousness field adopts a closed-loop collaborative decision-making mechanism. That is to say, it does not just passively provide suggestions, but allows all related intelligent agents (individual terminal AI, community health assistants, public health platforms, etc.) to coordinate with each other, dynamically adjust decisions, and achieve multi-level linkage intervention. For example, for individual users, the consciousness field can coordinate their family doctor AI, fitness coach App, and other intelligent agents to jointly formulate a comprehensive plan; at the public level, the community health assistant AI collaborates with the urban health management platform to adjust community activities or health resource allocation based on group Purpose. Through information sharing and action coordination of intelligent agents at all levels, an intervention network from individual to group is formed, ensuring that intervention measures are consistent from top to bottom and coordinated from left to right.
From a grander perspective, the concept of the digital health consciousness field coincides with ideas such as the "Global Brain." With the popularization of the Internet and artificial intelligence, human society is connected into an unprecedentedly tight network, and various kinds of information are transmitted globally at the speed of light, forming a structure similar to a "brain's neural network." Some thinkers call this the earth's "Noosphere" or global consciousness sphere, believing that the interconnected ICT network is becoming the "consciousness" carrier of the earth, undertaking information processing and coordination functions. The digital consciousness field is precisely the characterization of this human digital collective consciousness: it is not some independent entity, but a product of the spontaneous emergence of a complex adaptive system. When scientific researchers from all over the world share disease gene sequences through online platforms and update prevention and control strategies in real time, a "digital collective consciousness" in the global health field is actually formed; when the public in various countries learns about environmental pollution through open source data and promotes environmental protection actions, it is also building a digital consciousness field in the global ecological field. These examples show that digital technology can converge scattered human cognition into a force for collaborative action. During the COVID-19 pandemic, it was precisely digital tools that enabled the global scientific community to share virus information, vaccine progress, and prevention and control experience at an unprecedented speed, embodying the huge role of the digital consciousness field in the health field.
Of course, the digital consciousness field has also brought challenges. For example, the spread of false information may interfere with public health actions, forming a "digital rumor epidemic." This highlights the importance of building "digital immunity," that is, enhancing society's ability to identify and resist false information. Proactive medicine recognizes that a positive digital health culture must be shaped, using artificial intelligence to filter rumors and guide rational, scientific public awareness, so that the digital consciousness field becomes a positive force for promoting health and ecology, rather than a hotbed for creating panic and confrontation. This requires the joint efforts of the government, technology platforms, and the public: the government improves internet information supervision and health science popularization mechanisms, platforms strengthen rumor detection and blocking, and the public improves media literacy and critical thinking, etc. Through multi-party synergy, it can be ensured that the digital consciousness field is used for health and serves civilization.
In summary, the semantic-layer collaborative model is a systematic integration strategy of proactive medicine at a higher level. Through shared semantics and aligned Purpose, it integrates human cognition, machine intelligence, and group consciousness into one, building a "collective intelligence" and "common value" in the health field. It is precisely on this basis of semantic consensus that the multi-system coordination mechanism of proactive medicine can be seamlessly connected: the individual's mind and body, human-machine interaction, and the social ecology, all links can operate synergistically because they have a common health semantic field and value anchoring. This collaboration is embodied both in the meticulous care of AI for individuals at the micro-level, and in the concerted efforts of the whole society for health at the macro-level, demonstrating the profound pursuit of proactive medicine in cognitive philosophy and governance concepts.
Future Outlook
As an emerging paradigm, the theory and practice of proactive medicine are still evolving. Looking to the future, the cognitive channel structure and multi-system coordination mechanism are expected to become more complete with the development of technology and cognitive science, promoting profound changes in the field of healthcare.
In terms of cognitive channels, artificial intelligence cognitive models will continue to iterate rapidly. It is conceivable that more advanced large models and cognitive architectures will be integrated into the system of proactive medicine, enabling the "IQ" and "EQ" of AI assistants to improve simultaneously. For example, the recently proposed basic architecture for medical AI agents has proven that by adding reflection and memory modules, AI can continuously self-improve during the decision-making process. In the future, this type of architecture may become a standard configuration for health AI, giving it reasoning and learning capabilities close to human professionals. Furthermore, with the maturity of technologies such as federated learning and privacy computing, AI will be able to better use health data scattered in various places to train cognitive models, while protecting personal privacy and data security. This will make the cognitive channels of proactive medicine AI more comprehensive, more intelligent, and more trusted.
In terms of multi-system collaboration, we will see closer human-machine integration and cross-domain cooperation. The development of wearable and implantable devices, especially breakthroughs in brain-computer interfaces (BCI), may allow real-time bidirectional communication between the human body's biological signals and digital systems. In this way, AI will understand the human body's state more timely and accurately and intervene in regulation, as if adding a "digital auxiliary nervous system" to humans. This is expected to greatly strengthen the coupling of the information field and the energy field, enabling many health risks that were difficult to capture in time in the past (such as asymptomatic pathological changes, potential psychological crises) to be early warned and dealt with. At the same time, if the exploration of Artificial Consciousness (AC) in the field of artificial intelligence makes progress, it may also be introduced into proactive medicine. For example, some scholars have envisioned the introduction of artificial consciousness systems in group health management, as a "central intelligence" to coordinate group data and coordinate intervention decisions, simulating a role similar to a group doctor. Although AI with true subjective experience is still far away, endowing AI with higher proactiveness and consciousness-like functions at the functional level will enhance its control in complex collaborative tasks.
Future proactive medicine will also increasingly reflect the concept of a "health community." The global response to the COVID-19 pandemic has proven that all countries are closely related in the field of health, and only through collaborative cooperation can challenges be overcome. Proactive medicine can provide technical and framework support for this global collaboration: through a shared digital health consciousness field, experts and institutions from all countries can exchange information in real time and collaboratively formulate strategies, thereby establishing a global health risk collaborative mechanism. For example, a globally shared infectious disease surveillance system, and climate-health interaction models, will all make humanity more proactive in responding to pandemics and environmental health crises. This is also one of the value visions of proactive medicine: to build a "community of common health for mankind," elevating health to a common value and public goal that transcends national borders.
With the deepening of technology and collaboration, the medical service model will also be reshaped. The role of hospitals will evolve, and general practitioners, health managers, and AI assistants will work closely together to provide continuous preventive care services, not just disease treatment. The focus of medical care will move forward, and more resources will be invested in health education, lifestyle intervention, and chronic disease management. Personalized digital health records and digital twins will become standard for everyone, allowing doctors and AI to formulate long-term health development plans for individuals, monitor the evolution of key indicators, and dynamically adjust interventions. And insurance payment and policy incentives will also tilt to support such proactive prevention and management measures—for example, reducing premiums for individuals who insist on participating in health plans, or giving rewards to medical institutions that provide excellent prevention effects. The occurrence of these changes precisely reflects the role of the cognitive channel structure and the multi-system coordination mechanism: cognitively, everyone reaches a new consensus on the concept of health (prevention first, whole-person health); systematically, all stakeholders find a win-win balance (individuals are healthier, insurance expenditures are reduced, medical efficiency is improved, and overall social health is improved).
Of course, there are still challenges and uncertainties on the road ahead. For example, ethics and privacy are always unavoidable topics for proactive medicine. The high interconnection of systems and high sharing of data bring risks of privacy leakage and data misuse, which require strong legal supervision and technical protection. At the same time, people also worry that AI may introduce biases in decision-making or make inappropriate suggestions, and even become over-reliant on AI, thereby weakening their own judgment. These all require that while promoting technology, attention must be paid to establishing transparent, explainable, and human-led mechanisms. Proactive medicine, from its inception, emphasized rejecting manipulative design and preventing "agent dependency," insisting on human leadership and the possibility of halting decisions. These principles should continue to be upheld in the future, and refined into operable standards and norms according to new situations. In addition, the digital divide issue also needs attention: developed regions can enjoy advanced proactive health services, while remote or poor areas may not even have basic medical care. How to prevent technology from widening health inequalities will be an important issue. The solution may include: developing low-cost versions of health AI, strengthening public health investment, promoting international aid, etc., to achieve the inclusiveness of the proactive medicine concept.
Within the next 5 years, we are expected to see more interdisciplinary, high-quality results further enriching the theory of proactive medicine. For example, the analysis of consciousness and cognitive processes by cognitive neuroscience will provide inspiration for AI cognitive models; research on the motivation of health behaviors by behavioral economics and sociology will perfect the setting of the value-Purpose layer; new discoveries in human-computer ergonomics will optimize interaction design, making AI truly integrate into daily life without being obtrusive. This research will all converge and integrate on the platform of proactive medicine, making it more and more mature and complete.
In short, proactive medicine is standing at a brand new starting point. Looking to the future, its cognitive channel structure will be smarter and more humane, and its multi-system coordination mechanism will be tighter and more extensive. Driven by the synergy of technology, humanities, and policy, we have reason to believe: the focus of the medical model will shift from passive treatment to active health maintenance, and the health system will move from fragmentation and isolation to interconnectedness and collaborative co-governance. The ultimate beneficiaries of this process will be every ordinary person—we will discover risks earlier, prevent diseases more precisely, and improve well-being more comprehensively, achieving the common human vision of "health and longevity."
Summary
"The Cognitive Channel Structure and Multi-System Coordination Mechanism of Proactive Medicine" is the core essence of proactive medicine theory, reflecting a profound shift in the medical paradigm from passive response to active shaping. Through the systematic elaboration of related theories and practices, this article can summarize the following points:
First, proactive medicine is based on a holistic, systems-theory view of health, defining health as a low-entropy, orderly state of mind-body unity and continuous evolution. It draws on the wisdom of Eastern and Western philosophy regarding mind-body monism and human-nature harmony, and introduces the concept of entropy from information theory to clarify that health = the synergy of information flow and energy flow, and disease = the imbalance and entropy increase of the two. This establishes the value orientation of proactive medicine: "maintaining the order of the life system is good."
Second, proactive medicine constructs a cognitive channel structure represented by DIKWP, endowing the artificial intelligence system with the ability of active cognition and learning. Through the introduction of the Purpose layer, health AI can integrate and analyze data under the guidance of value and Purpose, making decisions conform to the individual's ultimate health goals. At the same time, the multi-layer interactive DIKWP network allows for the bidirectional flow of cognition and the emergence of patterns, letting AI not only discover new knowledge from data, but also feed its own output back as input for continuous self-improvement. This proactive intelligent mechanism of "cognitive emergence-reflection-regulation" elevates medical AI from a tool to a "digital partner" that can grow. It not only ensures that technology acts according to human expectations, but also provides an internal driving force for continuous optimization, ensuring that the intelligent system keeps pace with the times and gets smarter with use.
Third, proactive medicine places the cognitive system in a grander multi-system coordination network, emphasizing internal and external collaboration: on the one hand, it achieves mind-body coupling through information-energy consistency, ensuring the coordinated unity of physiological processes and cognitive activities, and the unity of mind-body knowledge and action; on the other hand, it promotes human-machine collaboration through human-machine interaction architecture, ensuring that AI decisions are synchronously matched with the patient's actual state, and that humans and machines trust each other and jointly promote health; furthermore, it constructs a four-party linkage health support system of individual-family-community-nation through social governance and digital platforms, allowing the responsibility for health promotion to be shared by the whole society, jointly creating a supportive environment. The result of this multi-level collaboration is that individuals no longer fight alone—intelligent assistants, medical teams, and social networks are all working together, making health maintenance a "concerto" with full system participation, rather than a "solo" by a single soldier.
Particularly important, proactive medicine has found the "glue" that binds the above elements together, which is collaboration at the semantic and value levels. By embedding ethical principles through the Purpose layer and aligning the human-machine meaning system through the semantic feedback loop, proactive medicine achieves unity of different subjects in the world of meaning. This ensures the integration of technical means and humanistic care: in addition to powerful computing power, AI has an "understanding heart" for people. It knows how to communicate with human language and emotion, understands to respect individual values and wishes, thereby stimulating human internal proactiveness. When doctors, AI, and patients reach a common understanding of "what is good for me," health intervention has the most solid foundation, and the collaborative network becomes unbreakable.
In summary, proactive medicine provides an intelligent engine through the cognitive channel structure and creates a concerted force for action through the multi-system coordination mechanism. The two complement each other, forming an organic health promotion system. It is not only an innovation in technology and models, but also contains a re-understanding of the essence of medicine: the mission of medicine is not only to cure diseases, but also to create an ecosystem where everyone actively pursues health and jointly achieves health. Such a concept has distinct foresight and contemporary value. Today, when artificial intelligence is booming and global health challenges are emerging one after another, proactive medicine depicts a moving vision for us: in this picture, everyone can better understand their own health with the help of intelligent assistants; every community is cooperating and helping each other to cultivate a health culture; and all mankind, with health as the bond of common well-being, gathers the power of technology and rationality to strive to reduce life entropy and improve quality of life.
Realizing this vision requires continuous exploration and multi-party efforts. But what is certain is that the direction of value alignment, semantic-driven, and collaborative co-governance advocated by proactive medicine represents an important milestone in the evolution of the medical paradigm. As long as we continuously test and improve this theory in practice, and properly deal with the ethical and social issues therein, proactive medicine is expected to develop into a health governance paradigm that leads the future. It will promote medical care from hospitals to communities, from individual doctor's wisdom to human-machine group intelligence, from passive response to proactive guidance, and ultimately promote the health and well-being of the entire population, achieving the ultimate care goal of medicine of "human-centricity." As a proverb says: "The best doctor is oneself, the best medicine is time, and the best health guarantee is being proactive." What proactive medicine depicts is precisely such a future where everyone becomes an active creator of their own health. We have reason to be full of confidence and expectation for such a future.
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