Schuitmaker L, Drogt J, Benders M, Jongsma K. Physicians' required competencies in AI-assisted clinical settings: a systematic review.
Br Med Bull 2025;
153:ldae025. [PMID:
39821209 PMCID:
PMC11738171 DOI:
10.1093/bmb/ldae025]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2024] [Revised: 12/12/2024] [Indexed: 01/19/2025]
Abstract
BACKGROUND
Utilizing Artificial Intelligence (AI) in clinical settings may offer significant benefits. A roadblock to the responsible implementation of medical AI is the remaining uncertainty regarding requirements for AI users at the bedside. An overview of the academic literature on human requirements for the adequate use of AI in clinical settings is therefore of significant value.
SOURCES OF DATA
A systematic review of the potential implications of medical AI for the required competencies of physicians as mentioned in the academic literature.
AREAS OF AGREEMENT
Our findings emphasize the importance of physicians' critical human skills, alongside the growing demand for technical and digital competencies.
AREAS OF CONTROVERSY
Concrete guidance on physicians' required competencies in AI-assisted clinical settings remains ambiguous and requires further clarification and specification. Dissensus remains over whether physicians are adequately equipped to use and monitor AI in clinical settings in terms of competencies, skills and expertise, issues of ownership regarding normative guidance, and training of physicians' skills.
GROWING POINTS
Our review offers a basis for subsequent further research and normative analysis on the responsible use of AI in clinical settings.
AREAS TIMELY FOR DEVELOPING RESEARCH
Future research should clearly outline (i) how physicians must be(come) competent in working with AI in clinical settings, (ii) who or what should take ownership of embedding these competencies in a normative and regulatory framework, (iii) investigate conditions for achieving a reasonable amount of trust in AI, and (iv) assess the connection between trust and efficiency in patient care.
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