Medical Consultation Case of Cold Complicated by Pharyngitis
通用人工智能AGI测评DIKWP实验室
Medical Consultation Case of Cold Complicated by Pharyngitis Leading to Bronchitis Based on the DIKWPArtificial Consciousness System
International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)
World Artificial Consciousness CIC(WAC)
World Conference on Artificial Consciousness(WCAC)
(Email: duanyucong@hotmail.com)
This case report details the complete clinical process of a patient who developed bronchitis following a common cold complicated by pharyngitis, including initial presentation, diagnosis, treatment, and follow-up. The patient's basic information, symptom progression, and detailed doctor-patient consultation dialogues are fully presented. According to Western medical diagnostic criteria, through symptom analysis, physical examination, and auxiliary laboratory tests, other possible diagnoses were systematically ruled out. The patient was ultimately diagnosed with acute bronchitis triggered by an upper respiratory tract infection and received targeted medication and therapeutic advice. During this process, a DIKWP artificial consciousness-assisted diagnosis and treatment system was introduced to assist the doctor's clinical work. This system constructs a closed-loop cognitive structure of "Data-Information-Knowledge-Wisdom-Purpose" in semantic space, facilitating semantic understanding and knowledge inference from doctor-patient dialogues, and optimizing the diagnostic process through semantic mathematical mechanisms of self-feedback such as subjective similarity, difference, and completeness. This report emphasizes how the DIKWP artificial consciousness system maps semantic space onto conceptual space via purpose-driven pathways (such as goal-oriented data collection and intent decision-making enhancement through wisdom), aiding physicians in semantic intent recognition and medical knowledge graph reasoning during consultations. Post-treatment follow-ups showed the patient's symptoms gradually subsided with satisfactory recovery. This case practically demonstrates the feasibility and potential value of integrating the DIKWP artificial consciousness system into clinical medical consultation.
Keywords: Common cold; Pharyngitis; Bronchitis; Case report; DIKWP artificial consciousness; Semantic understanding; Knowledge graph; Diagnostic reasoning
Acute bronchitis is a common infectious disease of the lower respiratory tract in clinical practice, usually caused by viral infection and often occurring secondary to upper respiratory infections such as the common cold or pharyngitis (Acute Bronchitis | Johns Hopkins Medicine). Typical patient presentations include persistent cough (with or without sputum), mild fever, throat discomfort, and mild breathing difficulty (Acute Bronchitis | Johns Hopkins Medicine). Most acute bronchitis cases are self-limiting; symptoms generally resolve within 1–3 weeks, although cough often persists for approximately 2–3 weeks and can occasionally last up to 4 weeks (Acute Bronchitis - StatPearls - NCBI Bookshelf). Differential diagnosis involves careful consideration to distinguish bronchitis from pneumonia: bronchitis patients usually lack high-grade fever and show no substantial infiltrates on lung imaging. However, the occurrence of severe fever or worsening dyspnea warrants vigilance for pneumonia (Acute Bronchitis - StatPearls - NCBI Bookshelf). In Western medicine, comprehensive history-taking, physical examination, and necessary auxiliary tests help reliably differentiate bronchitis from other conditions like pneumonia, guiding appropriate clinical treatment.
In recent years, artificial intelligence (AI) technologies have increasingly been applied in medicine. In diagnostic assistance, medical question-answering systems and the integration of large-scale pre-trained models with medical knowledge graphs have introduced new paradigms for clinical decision support. Such systems leverage the powerful semantic comprehension capabilities of large language models to convert patients' unstructured statements into structured medical information, combined with extensive medical knowledge bases from knowledge graphs, significantly enhancing diagnostic accuracy and providing medical professionals efficient and precise decision support. However, traditional AI predominantly emphasizes mapping from data to knowledge, with relatively limited capabilities in understanding medical "intent" and autonomous decision-making support. To further simulate and assist higher-level cognitive processes of physicians, Professor Duan Yucong proposed the DIKWP artificial consciousness model, which categorizes cognitive processes into five elements: Data, Information, Knowledge, Wisdom, and Purpose. The DIKWP model provides a novel perspective on understanding and simulating human consciousness, emphasizing the introduction of "Purpose" atop the classic "Data-Information-Knowledge-Wisdom (DIKW)" framework. Through dynamic interactions among these five elements, a closed-loop cognitive network structure emerges, closely resembling human physicians' cognitive processes in addressing complex medical issues. The five elements within this model interact dynamically rather than following a linear hierarchical relationship, forming an integrated "semantic closed-loop." This loop continually processes external inputs and simultaneously performs feedback adjustments on internal cognitive states, thereby generating holistic representations of internal conditions and external stimuli. This artificial consciousness framework has been utilized to develop intelligent diagnostic assistants, capable of real-time semantic understanding, intent reasoning, and self-correction optimization during physician consultations, thereby providing human-like intelligent support for clinical decision-making.
Based on a detailed clinical consultation involving a patient who developed bronchitis after a common cold complicated by pharyngitis, this paper presents a formal medical case report format. Besides traditional Western diagnostic analyses, we particularly emphasize the collaborative role of the DIKWP artificial consciousness system throughout each consultation stage, including data collection, semantic comprehension, knowledge reasoning, and intent-driven decision-making. Through this case, we explore practical methods and potential benefits of embedding artificial consciousness models into clinical medicine, aiming to offer valuable insights for the development of intelligent healthcare. The following sections first introduce the clinical case progression and treatment procedures, then elaborate on the DIKWP artificial consciousness system's operational mechanisms and contributions within this context, concluding with a summary of experience and prospects for future applications of artificial consciousness in medicine.
Case Presentation and Clinical Consultation Process
Patient Information and Chief Complaint
The patient is a 35-year-old male office worker. Chief Complaint: Persistent cough accompanied by sore throat for one week following a cold and pharyngitis, worsening over the past three days prior to presentation. The illness began shortly after recent exposure to cold temperatures, initially manifesting as common cold symptoms, followed by throat pain, and currently progressing to significant cough. He denies a history of chronic pulmonary diseases such as bronchial asthma, reports general good health, occasionally smokes (<5 cigarettes/day), and has no known drug allergies.
History of Present Illness (Symptom Progression)
According to the patient's account, the disease evolved as follows: Approximately eight days prior, the patient experienced nasal congestion, sneezing, and rhinorrhea after being exposed to cold, accompanied by mild fatigue and a low-grade fever of around 37.5°C. Two days later (about six days ago), he developed prominent throat pain, dryness, and odynophagia, suggestive of pharyngitis. The patient self-administered lozenges and herbal preparations for symptom relief. Three days ago, while throat pain began subsiding, he developed a paroxysmal cough initially dry, subsequently producing small amounts of white, viscous sputum. The cough notably worsened at night in the supine position, accompanied by a burning sensation behind the sternum without significant breathing difficulty. Self-measured temperature at this stage was approximately 37.8°C, not reaching high-grade fever. Due to progressively worsening cough disrupting rest, the patient sought medical attention five days after cough onset.
To fully assess the patient's condition, the physician conducted a detailed inquiry regarding symptom specifics and progression. The following is an excerpt from the consultation dialogue:
Doctor: "How did your discomfort initially start? Could you describe the entire course?"
Patient: "It began like a common cold about a week ago, stuffy nose, persistent runny nose, and frequent sneezing."Doctor: "Did you have a fever at that time?"Patient: "I had a mild fever, just slightly above 37°C, not very high. I drank water and rested but didn't take medication."Doctor: "When did your throat pain start? How severe was it?"Patient: "The throat pain started the second day after catching a cold. It was very dry and painful, even painful to swallow. I took herbal lozenges and some throat-soothing herbal tea, which gradually helped."Doctor: "When did the cough begin? Was it dry or productive?"Patient: "The cough started about three or four days ago, initially dry, then producing some white, sticky mucus. Sometimes the cough episodes were intense, especially when lying down at night."Doctor: "How was your temperature in the recent few days? Did it exceed 38°C?"Patient: "When my throat hurt, I measured once at 37.8°C. During these coughing days, I haven't measured again, but I don't think I had a fever, just feeling cold and weak."Doctor: "Did you experience chest pain or difficulty breathing during coughing episodes? Do you feel breathless after physical activity?"Patient: "I feel some chest discomfort like muscle soreness when coughing hard, which eases after rest. Walking is fine, no real shortness of breath. I do feel a burning sensation in my throat and chest when coughing."Doctor: "Have you had bronchitis or asthma in the past? Do you smoke regularly?"Patient: "I had bronchitis as a child, but it hasn't recurred in many years. Never had asthma. I occasionally smoke, but not heavily."Doctor: "Have people around you recently had similar symptoms, like flu or pneumonia?"Patient: "Several colleagues had coughs and colds recently, perhaps I caught it from them. No one mentioned pneumonia."Doctor: "Okay. Did you see a doctor or take antibiotics for the throat pain previously?"Patient: "No, I didn't see a doctor. I only took lozenges and no antibiotics."
(The physician repeatedly verified and confirmed all critical symptom points without omissions.)
The patient’s medical history clearly illustrates an initial upper respiratory infection (common cold) progressing to acute pharyngitis, subsequently descending to lower respiratory symptoms consistent with tracheobronchitis. Such progression from common cold/pharyngitis to bronchitis is frequently encountered clinically (Acute Bronchitis | Johns Hopkins Medicine). The patient's cough has persisted approximately five days, consistent with the clinical characteristics of acute bronchitis (Acute Bronchitis - StatPearls - NCBI Bookshelf). Accompanying symptoms, including low-to-moderate fever and the onset of sputum production following throat pain resolution, also support the likelihood of viral progression into the bronchi (Acute Bronchitis - StatPearls - NCBI Bookshelf). Absence of high fever, significant chest pain, and breathing difficulty, coupled with prominent upper respiratory symptoms during illness progression, favors acute bronchitis diagnosis. The physician remained vigilant during consultation for warning signs suggestive of pneumonia (such as high fever, intense chest pain, respiratory distress), none of which were exhibited by the patient.
Physical Examination and Laboratory/Imaging Findings
Physical Examination:
The patient appeared generally stable. Self-reported temperature was 37.6°C, respiratory rate 18 breaths/min, pulse rate 86 bpm, blood pressure 120/78 mmHg, and oxygen saturation 97% on room air at rest.
Throat: Mild pharyngeal mucosal congestion, slightly swollen without purulent discharge on tonsils; no cervical lymphadenopathy or tenderness.
Lungs: Slightly coarse breath sounds bilaterally; scattered mild expiratory wheezes heard over both lower lungs, partially improving with deep breathing. No moist rales or localized fixed crackles. Normal resonance on percussion, normal vocal resonance.
Heart: Regular rhythm at 86 bpm, no murmurs.
Abdomen and Extremities: No abnormalities or edema noted.
Physical findings indicated persistent mild upper airway inflammation and mild bronchospasm or mucus-related wheezing, without consolidation signs.
Auxiliary Examinations:
To clarify diagnosis and exclude pneumonia, laboratory and imaging studies were conducted:
Blood Count: WBC 9.8 ×10^9/L (normal: 4–10 ×10^9/L), Neutrophils 72% (↑), Lymphocytes 20% (↓), CRP 18 mg/L (mildly elevated; normal <10 mg/L), indicating mild inflammatory response predominantly neutrophilic, possibly late-stage viral infection or mild secondary bacterial involvement.
Rapid Antigen Throat Swab: Negative, ruling out major bacterial pathogens (such as Group A Streptococcus), supporting viral etiology of prior pharyngitis.
Chest X-ray: Bilateral lung fields clear, without infiltrates or consolidations. Slight hilar enlargement and increased bronchovascular markings noted. Normal cardiac silhouette and diaphragm contours. Radiological conclusion indicated bronchitis (peribronchial inflammatory changes), effectively ruling out lobar pneumonia and aligning with bronchitis imaging characteristics (Acute Bronchitis - StatPearls - NCBI Bookshelf).
Additionally, if the cough persists, pulmonary function testing is planned for subsequent evaluations of potential reversible airway obstruction (to exclude occult asthma); not performed at this initial visit. Overall, auxiliary examinations support an acute bronchitis diagnosis, excluding pneumonia and bacterial pharyngitis requiring specific targeted treatments.
Diagnosis and Differential Analysis
Based on a detailed medical history, physical examination, and auxiliary test results, the physician performed a systematic analysis and reasoning process:
Preliminary Diagnosis:
Acute Bronchitis (viral bronchitis) – The patient had a typical history of upper respiratory tract infection (common cold and pharyngitis) followed by bronchitis symptoms. The cough persisted for more than five days, gradually becoming productive, with thick but non-purulent sputum, indicative of inflammatory reactions in the bronchial mucosa due to viral infection (Acute Bronchitis - StatPearls - NCBI Bookshelf). Moderate temperature elevation, mildly elevated white blood cell count within normal limits, and slightly elevated CRP levels further support viral-induced airway inflammation rather than significant bacterial infection. Lung auscultation revealed wheezing without moist rales, and chest X-ray showed no infiltrates or consolidation, aligning with bronchitis rather than pneumonia. Thus, acute bronchitis is the most consistent diagnosis.
Differential Diagnosis Considerations:
Pneumonia:
The absence of high fever, minimal sputum without significant purulence, lack of moist rales on lung auscultation, and clear chest X-ray without evidence of consolidation effectively excludes pneumonia (Acute Bronchitis - StatPearls - NCBI Bookshelf). Should symptoms such as fever >38.5°C, purulent sputum, or radiographic infiltrates appear, pneumonia would need reconsideration.
Pertussis (Whooping Cough):
Although the patient experienced paroxysmal coughing, no classic inspiratory "whoop" was present, and the course of illness was short without clear exposure history. Thus, pertussis is unlikely at this stage. If cough persists beyond several weeks or characteristic symptoms arise, laboratory tests for pertussis would be reconsidered.
Bronchial Asthma Attack:
Although mild wheezing was observed, the patient has no history of asthma. Symptoms were clearly infection-related and improved with recovery, aligning more closely with post-infectious cough rather than cough-variant asthma. A bronchial provocation test was not performed initially, but if cough recurs or persists, further evaluation might be necessary.
Acute Tracheobronchitis vs. Acute Pharyngitis:
The patient clearly progressed from pharyngitis to tracheobronchitis. Currently, symptoms predominantly involve the lower respiratory tract; hence, bronchitis is the primary diagnosis, with pharyngitis considered secondary.
Others:
Conditions such as acute exacerbation of chronic obstructive pulmonary disease (COPD) (no COPD history), allergic bronchitis (no significant allergen exposure history) were not supported by the patient's profile. Given the patient's young, healthy status without chronic pulmonary conditions, these differentials are effectively excluded.
Final Diagnostic Conclusion:
Acute bronchitis following viral upper respiratory infection (common cold and acute pharyngitis) with mild bronchospasm (wheezing on auscultation) but no evidence of pneumonia.
Treatment Course and Follow-Up
Based on the above diagnosis, the physician adopted primarily symptomatic and supportive treatment aimed at alleviating symptoms, resolving airway inflammation, and preventing progression or complications. Treatment details were as follows:
Encouraged adequate rest, sufficient sleep, and increased fluid intake.
Recommended maintaining warm and humid indoor air, using frequent steam inhalation to moisten airways and ease cough and thick sputum.
Advised avoidance of cold exposure and vigorous exercise, recommending a one-week home rest period.
Advised smoking cessation during illness to reduce airway irritation.
Expectorants and Antitussives:
Compound Glycyrrhiza mixture (10 ml, orally, three times daily) to facilitate mucus clearance. Dextromethorphan syrup could be used at night to alleviate severe coughing interfering with sleep, but daytime management focused on expectoration rather than strong cough suppression, facilitating mucus clearance.
Bronchodilators:
Considering mild wheezing observed during examination, Salbutamol (Ventolin) aerosol inhalation (2 puffs every 6 hours) was prescribed to alleviate bronchospasm and chest tightness. The patient reported no significant respiratory distress, and auscultatory wheezing notably improved post-administration.
Anti-inflammatory and Other Treatments:
Given mild systemic inflammation and probable viral etiology, antibiotics were not routinely used (Acute Bronchitis - StatPearls - NCBI Bookshelf). The physician explained to the patient that acute bronchitis is typically viral, rendering antibiotics ineffective and potentially harmful due to resistance and side effects. The patient expressed understanding. Rapid throat swab testing was negative, ruling out bacterial pharyngitis, thus further justifying antibiotic avoidance. Saline gargle solution was prescribed for symptomatic throat relief. The physician advised monitoring closely for signs suggesting secondary bacterial infection (such as purulent sputum or recurring high fever), instructing prompt reassessment if such symptoms appeared. Symptomatic medications (e.g., acetaminophen 500 mg every 6 hours) were recommended if fever or headache occurred. Herbal lozenges were advised for throat irritation relief as needed.
The physician explained the self-limiting nature of acute bronchitis, emphasizing that coughing commonly lasts about two weeks even after resolution of inflammation (Acute Bronchitis - StatPearls - NCBI Bookshelf). Instructions for follow-up included immediate medical consultation if persistent high fever, worsening respiratory distress, or significant chest pain occurred; otherwise, adherence to medication, increased fluid intake, adequate rest, and patience during recovery were advised.
One week post-treatment, a telephone follow-up indicated notable improvement. The patient reported reduced daytime coughing, improved nighttime sleep, decreased mucus viscosity and volume, resolved throat discomfort, normal temperature, and no new symptoms. Bronchodilator inhalation had ceased three days prior without recurrence of wheezing. No recurrence of fever, chest pain, or alarming symptoms was noted. Continuation of symptomatic treatment to complete a two-week course was advised, gradually tapering medication afterward. A follow-up two weeks post-treatment showed complete cough resolution, symptom-free status, and a return to normal daily activities. Recommendations for preventive measures against cold exposure during winter and consideration of long-term smoking cessation were reiterated. The patient expressed satisfaction with clinical services.
Thus concluded a successful clinical course from symptom onset to full recovery, spanning approximately three weeks, aligning with expected outcomes. Antibiotics were not utilized throughout the treatment, consistent with current recommendations for acute bronchitis predominantly viral in nature (Acute Bronchitis - StatPearls - NCBI Bookshelf). Additionally, the integrated artificial intelligence-assisted diagnostic system employed during clinical interactions contributed meaningfully to the consultation and decision-making processes, which will be specifically elaborated upon in subsequent sections.
Application and Analysis of the DIKWP Artificial Consciousness System in the Consultation Process
Throughout the clinical case described previously, an intelligent diagnostic support system based on the DIKWP artificial consciousness model participated actively. Acting as a digital assistant, this system simulated the cognitive processes of physicians through semantic analysis of doctor-patient dialogues and clinical data, providing decision-making support. The following sections elaborate on how the DIKWP system was specifically applied to this case, demonstrating the realization of its closed-loop cognitive structure composed of "Data–Information–Knowledge–Wisdom–Purpose" (DIKWP).
Constructing the DIKWP Semantic Closed-Loop (Data–Information–Knowledge–Wisdom–Purpose)
The DIKWP system first interprets patient statements and consultation dialogues as raw Data inputs. For instance, individual patient-reported symptoms—such as a cough lasting 5 days, sticky white sputum, previous throat pain, and mild fever—were captured as discrete data points. Through semantic parsing, these data points were structured into meaningful clinical Information, clearly labeled for easy analysis, e.g., "Cough–Duration: 5 days," "Sputum–Nature: Sticky-white," "Max temperature: 37.8°C."
Next, the system employed its embedded medical Knowledge graph to link this structured information with existing medical concepts, enabling inference and pattern matching. For example, by comparing the patient’s clinical information with established respiratory disease profiles, the system recognized a high semantic alignment with "acute bronchitis," and a notably lower alignment with "pneumonia" (due to missing critical features like high fever or intense chest pain).
At the Wisdom level, the system integrated contextual reasoning and medical guidelines to make higher-order judgments. For example, given that the patient was young and otherwise healthy, the system's wisdom dictated adherence to guidelines advocating avoidance of unnecessary antibiotic use. Also, recognizing the sequential progression from upper to lower respiratory symptoms, the wisdom layer logically inferred viral etiology and prolonged viral inflammation, guiding clinical decisions accordingly.
Finally, the system arrived at the Purpose (Intent) layer, formulating specific actionable recommendations tailored to the scenario: diagnosing the case as acute bronchitis, recommending a chest X-ray to exclude pneumonia, and focusing primarily on supportive, symptomatic treatment without antibiotics. When new data (such as the results of the chest X-ray) entered the system, the entire closed-loop would re-engage, adjusting interpretations at each level until reaching a coherent and optimized decision.
It is noteworthy that this closed-loop is not linear but rather a networked structure with bi-directional feedback. The five elements of the DIKWP model interact dynamically through up to 25 interaction modules, allowing high-level intent to guide low-level data collection, and vice versa—new low-level data can refine higher-level judgments. For example, the physician’s intention to "exclude pneumonia" actively guided the system's choice to recommend imaging data acquisition (chest X-ray), and when new data confirmed the absence of pneumonia, the system refined its knowledge and wisdom-based decisions, thus optimizing treatment intentions.
Self-Feedback Optimization Using Semantic Mathematics: "Subjective Identity, Difference, and Completeness"
The DIKWP artificial consciousness system employs a semantic mathematical mechanism called "Subjective Identity, Difference, and Completeness," or "Subjective Same-Different-Complete (SDC)," rooted in the consciousness "BUG" theory. Briefly, as new information is processed, the system proactively evaluates:
Identity (Same): Does new data align with existing cognition?
Difference (Different): Does new information contradict or deviate from current understanding?
Completeness (Complete): Is the existing data set comprehensive enough to support confident decisions?
In the present case, the DIKWP system’s initial diagnostic hypothesis was "viral bronchitis." As the consultation unfolded, the system continuously tested the hypothesis:
Information such as "absence of high fever" and "progression from dry to productive cough" matched expectations ("Identity").
Mild chest pain reported by the patient aligned with typical muscle soreness from coughing and therefore reinforced the initial hypothesis ("Identity").
However, noting the patient's mild fever approaching 38°C triggered a subtle "Difference," as higher fever could indicate potential pneumonia. Though minor, this discrepancy alerted the system to potential cognitive uncertainty ("Bug").
To resolve this uncertainty, the system proactively requested additional diagnostic data (a chest X-ray). This process demonstrated "using difference to achieve completeness," where identified discrepancies prompted further investigation to enhance informational completeness. Once imaging confirmed the absence of pneumonia, this difference was resolved, restoring cognitive consistency and completeness.
The DIKWP system thus utilizes cognitive "Bugs" (uncertainties or contradictions) productively, prompting higher cognitive levels (Wisdom and Purpose layers) to engage more deeply to resolve such issues. In this case, a mild fever prompted deeper wisdom-based reasoning ("fever may indicate a serious infection") and reinforced the intent ("must exclude pneumonia"), ultimately guiding a diagnostic test decision. By continuously evaluating Identity, Difference, and Completeness, the DIKWP system maintains robustness in reasoning, avoiding both the neglect of abnormal cues and the overinterpretation of minor deviations. The final closure of the diagnostic loop occurs when all key uncertainties are resolved and the system's cognition achieves completeness.
Intent-Driven Pathway Modeling from Semantic to Conceptual Space
The DIKWP artificial consciousness system effectively transforms complex semantic information into coherent medical concepts and diagnostic reasoning pathways through intent-driven modeling. Intent-driven refers to higher-level goals within the system actively guiding lower-level information processing, creating a bridge between semantic (natural language) and conceptual (medical knowledge) spaces.
In the current clinical scenario, physician intent included: "Determining diagnosis (bronchitis versus pneumonia)" and "Developing an optimal treatment strategy." Correspondingly, the DIKWP system organized a series of subtasks:
Purpose→Data: To fulfill the intent of "excluding pneumonia," the system initiated acquisition of relevant objective data (e.g., temperature measurements, imaging). Similarly, to ensure medication safety, it prompted questions about drug allergies or chronic diseases.
Wisdom→Information: Wisdom contains clinical guidelines and collective medical experiences. Understanding that antibiotics are unnecessary for viral bronchitis guided information selection at lower layers, favoring data reinforcing supportive treatments over antibiotic necessity, thus ensuring coherent clinical reasoning.
Knowledge→Data: The Knowledge layer contains medical theory and statistical patterns. Awareness of rules like "high fever (>38.5°C) typically indicates pneumonia" guided the system’s attention to specific data points (patient’s measured temperature, CRP values). Similarly, the knowledge of "cough lasting more than two weeks necessitates pertussis evaluation" prevented premature testing due to short symptom duration.
Purpose→Information and Wisdom→Purpose (Intent): Intent directly influenced selection of relevant patient statements (symptoms and signs), filtering out irrelevant conversational data. Wisdom continuously audited and refined these intents, ensuring ethically and medically sound decisions (e.g., resisting patient pressure for unnecessary antibiotic prescriptions).
Through these multi-layered pathways, the DIKWP system achieves seamless mapping from semantic to conceptual spaces. Natural language dialogues (semantic) become systematically interpreted into clinically meaningful concepts and relationships (conceptual), guided by higher-level intent. High-level intent provides direction; low-level data supplies factual grounding; middle-level knowledge establishes connections. This dynamic interaction permits both top-down planning and bottom-up verification, enabling the AI system to deeply "understand" the patient interaction, integrate scattered data into meaningful clinical insights, and translate abstract clinical intentions into tangible diagnostic and therapeutic actions.
Semantic Intent Recognition and Medical Knowledge Graph Reasoning Supporting Clinical Decision-making
During actual clinical interactions, the DIKWP artificial consciousness system established effective collaboration with physicians, prominently demonstrating its capabilities in two key areas: semantic intent recognition and medical knowledge reasoning.
Semantic Intent Recognition
Doctor-patient dialogues often contain abundant implicit information and contextual intent. Leveraging its semantic understanding module, the DIKWP system continuously “listened” to patient statements, capturing critical symptom keywords and their temporal relationships. Concurrently, it interpreted physicians' questioning intent to identify the necessary information for subsequent clinical reasoning. For example, when the doctor asked, "When did the cough start? Was it dry or productive?", the system identified that the doctor's immediate intent was to obtain a timeline and the nature of the cough. Accordingly, the system not only structured the patient's response ("cough lasting five days, initially dry then productive") into precise, structured information but also immediately relayed it to the knowledge graph module to refine diagnostic assessments.
Additionally, when the patient mentioned that "cough worsens when lying flat at night," the patient did not explicitly express concern. However, based on semantic understanding and clinical experience, the system inferred an underlying patient concern about the severity of the symptom. Consequently, when generating suggestions for the doctor's subsequent interactions, the system proposed reassuring the patient with an explanation such as: "The increased cough at night is due to accumulation of secretions and doesn’t necessarily indicate worsening disease severity." This exemplifies the system’s capability for interpreting implicit patient concerns and supporting physician communication strategies. Throughout the consultation, the DIKWP system effectively served as a "second brain," prompting the physician to explore critical symptoms systematically and accurately interpreting patient descriptions, thus minimizing misunderstandings between doctor and patient (Duan, 2024).
Medical Knowledge Graph Reasoning
The DIKWP system is linked to an extensive medical knowledge graph comprising interconnected entities such as diseases, symptoms, diagnostic tests, and treatments. As patient data was continuously input, relevant nodes and relationships within the knowledge graph were activated for reasoning and deduction. In this clinical case, the system initially generated several diagnostic hypotheses—acute bronchitis, pneumonia, and post-pharyngitis cough—and calculated their semantic matching scores. After diagnostic tests ruled out pneumonia, the system pruned pneumonia-related pathways from the graph, thus increasing confidence in the acute bronchitis pathway. Simultaneously, it retrieved best-practice guidelines from the knowledge graph, emphasizing treatment principles such as "antibiotics not recommended (viral etiology)" and "supportive symptomatic treatment as primary strategy" (StatPearls, 2023). These recommendations directly validated the physician's treatment decisions.
Moreover, during prescription formulation, the system cross-checked medication safety against patient allergy history, drug interactions, and recommended dosages according to clinical guidelines. This knowledge graph-based reasoning function operated similarly to having a clinical pharmacist and a literature assistant available, making prescriptions safer and more evidence-based. Furthermore, the system also facilitated patient education, quickly retrieving authoritative explanations for clinical decisions (such as reasons for avoiding antibiotics), thereby enhancing patient understanding and compliance (StatPearls, 2023).
In summary, within this clinical case, the DIKWP artificial consciousness system fulfilled multiple roles: it acted as an information interpreter, decision advisor, and validator. By providing semantic understanding and knowledge-based reasoning, it significantly strengthened diagnostic accuracy and physician confidence. Research has shown that integrating the semantic comprehension capabilities of large language models with structured medical knowledge graphs substantially enhances diagnostic accuracy and efficiency (Medical Knowledge Graph Applications, 2025). This clinical case exemplified precisely this finding: the DIKWP system, through deep semantic analysis, comprehended the patient's symptoms and the physician’s diagnostic intents, rapidly retrieving and reasoning from vast medical knowledge resources to transform abstract guidelines into actionable clinical decision support. Throughout the consultation process, the artificial consciousness system closely collaborated with the physician—allowing the physician to focus on empathetic patient interactions and final clinical judgments, while the AI handled complex information processing and knowledge assistance, making clinical care more comprehensive and efficient. As such AI technologies mature, future clinical practices will enable physicians to more confidently handle complex scenarios, supported by powerful semantic computation and knowledge reasoning engines.
This case report presented a comprehensive clinical process of acute bronchitis triggered by a common cold and pharyngitis, showcasing the innovative application of the DIKWP artificial consciousness system. Through meticulous medical history taking, careful physical examination, and necessary supplementary testing, accurate diagnosis and effective treatment were achieved, leading to successful patient recovery. Crucially, the DIKWP artificial consciousness system was actively involved throughout the entire consultation, executing closed-loop semantic analysis from data to intent, significantly aiding physicians in diagnostic reasoning and decision-making.
Utilizing semantic mathematical mechanisms, the system evaluated information consistency, differences, and completeness, promptly identifying and resolving cognitive gaps, thereby demonstrating a human-like reflective capacity. Through intent-driven multi-layered pathways, the system transformed high-level clinical intentions into specific data acquisition tasks and knowledge retrievals, translating patient narratives into structured medical conceptual networks to assist complex diagnostic reasoning. The deep integration of the medical knowledge graph enabled rapid matching of patient data with extensive medical information, increasing diagnostic accuracy and ensuring medication safety (Medical Knowledge Graph Applications, 2025). This physician-AI collaboration made the consultation thorough and efficient, blending subjective clinical judgment with objective AI-driven insights, ensuring compliance with best practices and individual patient needs.
This report highlighted the immense potential of applying artificial intelligence—particularly artificial consciousness models—in clinical practice. The DIKWP system, modeling physician cognitive architecture and thought processes, offered a feasible pathway toward patient-centered intelligent healthcare. Through this case, we observed that artificial consciousness systems can seamlessly integrate into routine clinical workflows, collaborating effectively with medical professionals, alleviating physicians' cognitive burdens from tedious information processing tasks, and enabling them to focus more on empathetic patient interactions. Such AI assistance also minimizes diagnostic errors, promotes guideline adherence (e.g., appropriate antibiotic use), enhances medical care quality, and improves patient satisfaction. While broader clinical application faces challenges like data privacy and responsibility allocation, technological and regulatory advancements will progressively address these issues. Ultimately, the DIKWP artificial consciousness model represents an innovative leap for medical AI—from mere imitation of human knowledge to modeling human consciousness itself. Looking forward, intelligent diagnostic assistants incorporating artificial consciousness will likely play critical roles in more complex clinical scenarios, including critical care decision-making, personalized treatment design, and multidisciplinary consultations. Human-AI collaborative medical care will soon become the new norm, making medicine smarter, more empathetic, and ultimately benefiting patients.
Duan Y. et al. DIKWP Artificial Consciousness: Theory, Design, and Simulation Implementation. 2024.
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玩透DeepSeek：认知解构+技术解析+实践落地
人工意识概论：以DIKWP模型剖析智能差异，借“BUG”理论揭示意识局限
人工智能通识 2025新版 段玉聪 朱绵茂 编著 党建读物出版社
邮箱|duanyucong@hotmail.com
