Abstract Artificial intelligence is transforming healthcare beyond efficiency gains, presenting an opportunity to fundamentally reorganize the clinical workforce. This paper proposes the “Generalist-Specialist” model, arguing that AI’s capacity to scale specialist-level knowledge challenges the historical “cognitive necessity” for narrow specialty definitions. By democratizing clinical expertise, AI-augmented clinicians could manage the full constellation of patients’ chronic and complex conditions within broader, disease-based domains (e.g., cardiometabolic, infectious and inflammatory) rather than organ-specific specialties. This shift promises fewer handoffs, better coordination, and unlocked specialty capacity for patients who need it most. Economically, consolidating care under fewer clinicians makes value-based and bundled payments more feasible, though under fee-for-service, without countervailing payment reform, it risks increasing total utilization. Realizing this vision requires evolving medical education to incorporate AI, reforming malpractice standards to accept AI-guided evidence-based care, modernizing credentialing frameworks, and strategically repositioning Academic Medical Centers toward ultra-complex care or seamless generalist-specialist hubs. The greatest impact of AI in healthcare may not be doing the same things more efficiently, but enabling an entirely new class of clinicians organized around disease biology and patient need. Realizing this potential will require navigating formidable non-technical barriers, including incumbent interests, legacy payment models, and patient safety and liability standards.
Kocher et al. (2026) studied this question.