Evidence map›Paper›PMID 40080059›Full record

ReviewJMIR AI2025

Studying the Potential Effects of Artificial Intelligence on Physician Autonomy: Scoping Review.

John Grosser, Juliane Düvel, Lena Hasemann, Emilia Schneider, Wolfgang Greiner

Abstract readReview
In one paragraph

Review in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
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 Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany.ORCID https://orcid.org/0000-0003-3890-5596
Juliane DüvelCentre for Electronic Public Health Research (CePHR), School of Public Health, Bielefeld University, Bielefeld, Germany.ORCID https://orcid.org/0000-0001-5700-4852
Lena HasemannDepartment of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany.ORCID https://orcid.org/0000-0001-5609-0971
Emilia SchneiderDepartment of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany.ORCID https://orcid.org/0009-0005-3994-701X
Wolfgang GreinerDepartment of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Bielefeld, Germany.ORCID https://orcid.org/0000-0001-9552-6969

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPhysician autonomy has been found to play a role in physician acceptance and adoption of artificial intelligence (AI) in medicine. However, there is still no consensus in the literature on how to define and assess physician autonomy. Furthermore, there is a lack of research focusing specifically on the potential effects of AI on physician autonomy.

objectiveThis scoping review addresses the following research questions: (1) How do qualitative studies conceptualize and assess physician autonomy? (2) Which aspects of physician autonomy are addressed by these studies? (3) What are the potential benefits and harms of AI for physician autonomy identified by these studies?

methodsWe performed a scoping review of qualitative studies on AI and physician autonomy published before November 6, 2023, by searching MEDLINE and Web of Science. To answer research question 1, we determined whether the included studies explicitly include physician autonomy as a research focus and whether their interview, survey, and focus group questions explicitly name or implicitly include aspects of physician autonomy. To answer research question 2, we extracted the qualitative results of the studies, categorizing them into the 7 components of physician autonomy introduced by Schulz and Harrison. We then inductively formed subcomponents based on the results of the included studies in each component. To answer research question 3, we summarized the potentially harmful and beneficial effects of AI on physician autonomy in each of the inductively formed subcomponents.

resultsThe search yielded 369 studies after duplicates were removed. Of these, 27 studies remained after titles and abstracts were screened. After full texts were screened, we included a total of 7 qualitative studies. Most studies did not explicitly name physician autonomy as a research focus or explicitly address physician autonomy in their interview, survey, and focus group questions. No studies addressed a complete set of components of physician autonomy; while 3 components were addressed by all included studies, 2 components were addressed by none. We identified a total of 11 subcomponents for the 5 components of physician autonomy that were addressed by at least 1 study. For most of these subcomponents, studies reported both potential harms and potential benefits of AI for physician autonomy.

conclusionsLittle research to date has explicitly addressed the potential effects of AI on physician autonomy and existing results on these potential effects are mixed. Further qualitative and quantitative research is needed that focuses explicitly on physician autonomy and addresses all relevant components of physician autonomy.

Indexed as

acceptanceadoptionAIAI ethicsartificial intelligenceautonomy, professional autonomyCDSSclinical decision support systemsethicsethics of artificial intelligencephysicianphysician autonomyscoping review

Identifiers

PMID40080059
PMCPMC11950692

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.