AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025340067
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ORIGINAL RESEARCH ARTICLE

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

Malik Sallam1,2* ,  Johan Snygg3,4,5 ,  Mazin Aljabiri5,6 ,  Edward Cody7 ,  Reem Allateef7 ,  Chadia Beaini5,8 ,  Mohammed Sallam5,9,10,11,12
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1 Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, University of Jordan, Amman, Jordan
2 Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, Jordan
3 Department of Management, Mediclinic City Hospital, Dubai, United Arab Emirates
4 Department of Anesthesia and Intensive Care, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
5 Department of General Medicine, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
6 Department of Gastroenterology, Mediclinic Parkview Hospital, Dubai, United Arab Emirates
7 Department of Family Medicine, Mediclinic Parkview Hospital, Dubai, United Arab Emirates
8 Department of Nephrology/Dialysis, Mediclinic Welcare Hospital, Dubai, United Arab Emirates
9 Department of Pharmacy, Mediclinic Parkview Hospital, Dubai, United Arab Emirates
10 Department of Management, Mediclinic Parkview Hospital, Dubai, United Arab Emirates
11 Department of Management, School of Business, International American University, Los Angeles, California, United States of America
12 Department of Pharmacology and Therapeutics, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
AIH, 025340067 https://doi.org/10.36922/AIH025340067
Received: 20 August 2025 | Revised: 9 September 2025 | Accepted: 18 September 2025 | Published online: 15 October 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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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.

Graphical abstract
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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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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