ReviewDigital health
Theoretical appraisal of explanatory paradigms for artificial intelligence usage by medical doctors.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.