General Practitioners' Attitudes Toward Artificial Intelligence-Enabled Systems : Interview Study

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Buck, Christoph ; Doctor, Eileen ; Hennrich, Jasmin ; Jöhnk, Jan ; Eymann, Torsten:
General Practitioners' Attitudes Toward Artificial Intelligence-Enabled Systems : Interview Study.
In: Journal of Medical Internet Research. Bd. 24 (2022) Heft 1 . - No. e28916.
ISSN 1438-8871
DOI der Verlagsversion: https://doi.org/10.2196/28916

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Abstract

Background: General practitioners (GPs) take care of a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care. Objective: This study seeks to understand GPs’ attitudes towards AI-enabled systems in medical diagnosis. Methods: We interviewed 18 GPs from Germany between March and May 2020 to identify determinants of GPs’ attitudes towards AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants. Results: We merged the open codes into 21 concepts and finally into five categories: (1) concerns, (2) expectations, (3) environmental influences, (4) individual characteristics, and (5) minimum requirements of AI-enabled systems. Concerns include all doubts and fears of the interviewees regarding AI-enabled systems. Expectations reflect GPs’ thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences include influences resulting from an evolving working environment, key stakeholders’ perspectives and opinions, the available IT hardware and software resources, and the media environment. Individual characteristics are determinants that describe a physician as a person, including character traits, demographic specifics, and knowledge. Besides, the interviews also revealed minimum requirements of AI-enabled systems, which are preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships between these categories, which we conflate in our proposed model. Conclusions: This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing socio-technical systems and the literature on attitude towards technology and practice by fostering the understanding of GPs and their attitude on AI-enabled systems. Our findings provide relevant information to technology developers and policymakers, and stakeholder institutions of GP care.