Theoretical appraisal of explanatory paradigms for artificial intelligence usage by medical doctors

The integration of artificial intelligence (AI) into medical practice has generated both enthusiasm and hesitation among physicians, yet no comprehensive theoretical appraisal has systematically evaluated the paradigms used to explain this phenomenon. This study aims to critically assess existing theories, conceptual models, and frameworks to determine their capacity to explain AI acceptance and resistance among medical doctors.

A structured critical examination was conducted using the developed T2P2 criteria, which categorizes 12 evaluation dimensions into theoretical, technological, professional, and personal groups. A structured paradigm search across Scopus and Web of Science research articles identified 28 peer-reviewed studies that applied 21 distinct paradigms to examine AI usage by physicians. Each paradigm was then evaluated using a trichotomous rating scale alongside interpretive analysis to assess both quantitative alignment and conceptual coherence.

Among the evaluated paradigms, the Social Cognitive Theory, Self-Determination Theory, and Dual Factor Model consistently met the T2P2 criteria, demonstrating strong explanatory sufficiency across internal psychological mechanisms and external structural factors. Other paradigms, while useful in specific contexts, often exhibited insufficiency in intelligent systems relevance, complimentary duality, professional functionality, specialization applicability, pedagogical usability, and healthcare context specificity.

This study clarifies the theoretical robustness of existing paradigms in explaining AI usage among medical doctors, highlighting the need for integrative paradigms that account for both cognitive-motivational and socio-technical factors. The findings inform IS researchers, clinicians, and policymakers on selecting appropriate explanatory paradigms for responsible AI integration in healthcare.