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Artificial Intelligence (AI) is reshaping medical practice and education by unifying the knowledge of medical specialties and subspecialties into a single, intelligent system. This system functions not only as an integrated knowledge base but also as an active cognitive collaborator. By bridging knowledge gaps beyond the capacity of any individual clinician, AI could enable physicians to function as AI-augmented generalists who can perform certain tasks that require specialist training. These generalists may help mitigate care fragmentation, but realising their potential will require significant changes in medical education.
Medicine as a knowledge enterprise
Medicine has always been a knowledge-driven field, requiring a deep understanding of human anatomy, physiology, pathology, pharmacology, etc, to guide decision-making. Diagnosis, for example, relies on synthesising clinical data and applying expert knowledge to ensure accurate diagnoses and treatment. This vast knowledge repository constantly evolves, with new discoveries and advancements continually reshaping the field.
The Industrial Revolution laid the foundation for modern medicine by formalising education and introducing systematic approaches to diagnostics and treatment, such as standardised medical curricula, hospital-based training and laboratory science.1 During the 20th century, there was a rapid expansion in medical knowledge, driven by breakthroughs in microbiology, pharmacology and medical imaging.2 Notwithstanding, these developments highlighted the cognitive limitations of human practitioners, who struggled to master increasingly complex knowledge bases. In the 21st century, electronic health records and genomics have further amplified the volume and complexity of medical knowledge.3
The explosion of medical knowledge made specialisation inevitable. Physicians were forced to focus on narrower fields to master increasingly complex conditions and treatments. This specialisation advanced precision in diagnostics and care but fragmented the healthcare system. Patients with multisystem conditions often face delays and communication barriers in care due to a lack of integration among specialists. This fragmentation highlighted the need for a more unified, collaborative approach to medicine—one that artificial intelligence (AI) can help physicians achieve.
Medicine reintegrated: the rise of AI-augmented generalists
AI offers unprecedented opportunities to bridge knowledge gaps in fragmented specialties—integrating disparate knowledge into unified AI systems. AI is enabling a return to integrated medicine by empowering specialists to leverage these unified AI systems and act as AI-augmented generalists. Unlike generalists from previous eras, who relied solely on their memory and limited resources, AI-augmented generalists operate within a distributed cognition framework that combines human expertise with AI’s vast knowledge base and reasoning abilities.4 These large AI systems encompass the knowledge of specialties and subspecialties in a single, unified system—a feat impossible for the individual human mind. By integrating this knowledge, AI has the potential to assist physicians in performing timely and accurate diagnoses and improve care by reducing some of the missed diagnoses and delayed care due to care fragmentation.
For example, internal medicine includes diverse subspecialties, such as cardiology, gastroenterology, infectious diseases, rheumatology, haematology, etc. Historically, general internists have managed foundational knowledge across all these areas; however, they were often limited in effectively addressing highly specialised conditions. Although the rise of subspecialists addressed this gap, it led to fragmented care.
Consider a patient presenting with overlapping symptoms, including persistent fatigue, shortness of breath, swollen joints and unexplained weight loss. Traditionally, diagnosing and treating such a patient could require input from a cardiologist to evaluate potential heart failure, a rheumatologist to assess autoimmune disorders and an endocrinologist to rule out metabolic conditions. In this scenario, an AI-augmented generalist could leverage AI tools to access integrated knowledge from these subspecialties. The AI system might identify patterns connecting autoimmune inflammation with cardiac dysfunction and metabolic abnormalities, assisting the physician with a holistic diagnosis. Such an approach could potentially reduce misdiagnosis and the need for multiple referrals and accelerate treatment initiation, thereby improving patient outcomes.
Redesigning medical education for the AI era
Medical education must undergo a fundamental transformation to prepare physicians for effective collaboration with advanced AI systems. These systems are no longer passive knowledge bases but active cognitive collaborators—capable of reasoning, pattern recognition and insight generation across specialties and subspecialties. This change necessitates a shift away from traditional, memorisation-focused acquisition of knowledge towards curricula that emphasise understanding, applying and using knowledge in tandem with AI.
To navigate this new landscape, physicians must acquire a range of new competencies. These include: AI system proficiency—understanding how AI tools function, what their outputs mean and where their limits lie; collaborative problem-solving—integrating AI-generated insights into clinical decision-making; contextual adaptation—tailoring those insights to the nuances of individual patients; and robust ethical reasoning—ensuring responsible and patient-centred use of AI tools.
Medical education must prepare physicians to identify and mitigate AI-related risks, including recognising biases from non-representative data and understanding transparency challenges inherent in AI systems. Training should address critical privacy and security concerns, equipping physicians with strategies to handle sensitive patient data securely5 and navigate regulatory frameworks such as Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR) and the European AI Act. Additionally, curricula must clarify legal and liability issues around human-AI collaborative decisions, defining accountability and professional responsibilities clearly. Ultimately, the effective integration of AI in healthcare requires educating clinicians who can collaborate confidently with AI while prioritising safety, equity, ethics and trust.
Conclusion
This article focuses on medicine primarily as a knowledge enterprise that supports clinical practice and education. The emergence of integrated AI systems, encompassing knowledge across multiple specialties, enables AI-augmented generalists to perform clinical tasks traditionally requiring specialised training. However, fully harnessing these potential demands careful management of AI-specific challenges, including biases, transparency issues, privacy, security, regulatory compliance, ethical accountability and legal implications. Consequently, medical education must evolve from knowledge acquisition via rote memorisation toward fostering skills essential for effective and responsible human-AI collaboration. Ultimately, preparing physicians to proficiently and ethically partner with advanced AI systems will be critical in navigating the increasing complexities of modern healthcare. As AI development is still ongoing, continued research is essential to understand and address these evolving challenges.
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Acknowledgments
This work was supported by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health, through UTHealth-CCTS, grant number 1 U54 TR002804-01.
Footnotes
X @jiajiezhang100
Contributors The author, JZ, contributed the entire contents of this article. ChatGPT-4o and Grammarly were used to review and revise editorial details such as grammar, paragraphing and other editorial tasks. The contents of this article were entirely conceived and generated by the author.
Funding This study was funded by the National Center for Advancing Translational Sciences (1 U54 TR002804-01).
Competing interests None declared.
Provenance and peer review Commissioned; externally peer reviewed.