Evidence map›Paper›PMID 42462057›Full record

ArticleJMIR formative research2026

Investigating the Potential Effects of Medical AI Systems on Physician Autonomy: Pretest of a Semistructured Qualitative Interview Guide.

John Grosser, Jule Sauerbach, Rainer Borgstedt, Sebastian Rehberg, Juliane Düvel

Abstract read
In one paragraph

Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

John GrosserDepartment of Health Economics and Healthcare Management, School of Public Health, Bielefeld University, Universitätsstraße 25, Bielefeld, North Rhine-Westphalia, 33615, Germany, 49 521-106-86319.ORCID 0000-0003-3890-5596
Jule SauerbachSchool of Public Health, Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany.ORCID 0009-0002-3906-6747
Rainer BorgstedtDepartment of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Campus Bielefeld-Bethel, University Hospital Bielefeld, Bielefeld, North Rhine-Westphalia, Germany.ORCID 0000-0002-2870-745X
Sebastian RehbergDepartment of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Campus Bielefeld-Bethel, University Hospital Bielefeld, Bielefeld, North Rhine-Westphalia, Germany.ORCID 0000-0002-5835-7083
Juliane DüvelCentre for ePublic Health Research, School of Public Health, Bielefeld University, Bielefeld, North Rhine-Westphalia, Germany.ORCID 0000-0001-5700-4852

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: AI is an increasingly prominent feature of contemporary health care, with medical AI systems beginning to support diagnostic and therapeutic processes in many clinical domains. Alongside the anticipated benefits of these technologies, their introduction also raises broader questions about how clinical work and professional roles may change. In particular, medical AI systems may affect physician autonomy, a key factor influencing the acceptance and long-term implementation of new medical technologies. Objective: The aim of this study was to develop and pretest a semistructured interview guide concerning the potential effects of medical AI systems on physician autonomy. Methods: The interview guide was theoretically grounded in a 7-component model of physician autonomy proposed by Schulz and Harrison. Semistructured qualitative interviews were conducted with a sample of 7 hospital physicians. Interview recordings were transcribed and analyzed using a hybrid inductive-deductive thematic approach: themes were first identified inductively from participant responses and subsequently mapped onto the 7-component model of physician autonomy proposed by Schulz and Harrison. Data were analyzed to assess both the potential effects of medical AI systems on physician autonomy and the methodological adequacy of the interview guide. Results: Most participants did not express strong concerns about losing clinical autonomy through the introduction of AI systems. However, several autonomy-related risks were identified, including potential deskilling, automation bias, limited system explainability, and increasing economic or cost-related pressures. Participants emphasized that AI should serve as a supportive tool rather than a substitute for physician judgment. All physicians agreed that AI systems should not replace clinicians as primary clinical decision-makers. Conclusions: Medical AI was largely viewed as compatible with physician autonomy; however, participants highlighted important risks that warrant attention in future research and system design. Our preliminary findings suggest that autonomy-related concerns extend beyond the direct loss of decision-making authority and include broader professional, cognitive, and organizational dimensions. However, our inductively identified themes and subthemes did not fully reflect all components of physician autonomy, indicating the need for further refinement of how to assess physician autonomy in qualitative research.

Indexed as

Artificial IntelligencePhysiciansProfessional AutonomyAdultFemaleHumansInterviews as TopicMaleMiddle AgedQualitative Researchartificial intelligenceclinical decision support systemphysicianspretestprofessional autonomyqualitative research

Identifiers

PMID42462057
PMCPMC13374796

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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.