Mapping medical specialty vulnerability to superintelligent AI: A competency-guided generative AI foresight framework

Abstract

The evolution of artificial intelligence (AI) raises questions about the future roles of physicians. This study aimed to propose an exploratory foresight model for stratifying risk across medical specialties, using board-defined competencies and generative AI (genAI) evaluation as the assessment tool. We developed a heuristic framework, the Machine automat-ability, Diagnostic Ambiguity, Legal/ethical complexity, Interpersonal intensity, Knowledge codifiability, Evidence in data, Difficulty of procedures (MALIKED) score, to capture dimensions of displacement vulnerability for 27 board-recognized specialties. To minimize individual bias, ratings were generated by three genAI models (ChatGPT, DeepSeek, and Gemini). Data-centric fields—Clinical Pathology (30.3/35), Anatomic/Clinical Pathology (29.3/35), and both Anatomic Pathology and Radiology (28.0/35 each)—clustered in the highest-vulnerability tier. In contrast, procedurally intensive or patient-interaction-heavy specialties—including Psychiatry (11.0/35), Neurosurgery (11.7/35), Obstetrics/Gynecology (13.0/35), General Surgery (13.0/35), Pediatrics (14.3/35), Emergency Medicine (14.3/35), and Family Medicine (14.3/35)—formed the lowest-vulnerability tier. Between these extremes, mixed-mode specialties, such as Internal Medicine (17.0/35) and Neurology (17.0/35), along with Ophthalmology (19.3/35) and Anesthesiology (21.3/35), occupied an intermediate zone. Displacement risk was driven by knowledge codifiability and data-centricity, while procedural complexity and interpersonal interaction intensity exerted protective effects. This exploratory foresight framework suggests that the risk of displacement by advanced or potentially superintelligent AI is unevenly distributed across medical specialties. While data-driven fields appear most exposed, no specialty is categorically insulated, as multimodal AI and robotics continue to evolve. The MALIKED framework is not predictive but intended as a structured lens for debate, education, and workforce planning regarding the long-term implications of AI in medicine.

Keywords

Generative pre-trained transformer / Clinical competence / Automation / Workforce / Decision support systems

Funding

None.

Conflict of interest

The authors declare that they have no competing interests.

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