Evidence map›Paper›PMID 40857713›Full record

ArticleJournal of medical Internet research2025

Physicians' Attitudes Toward Artificial Intelligence in Medicine: Mixed Methods Survey and Interview Study.

Helen Heinrichs, Alexander Kies, Saskia K Nagel, Fabian Kiessling

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

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

  1. Pooled it
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  11. Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026
    Article
  12. Artificial intelligence in clinical genetics: Current practice and attitudes among the clinical genetics workforce.Genetics in medicine : official journal of the American College of Medical Genetics · 2026
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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

4 authors.

Helen HeinrichsInstitute for Experimental Molecular Imaging, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University), Aachen, Germany.ORCID https://orcid.org/0000-0002-9727-1979
Alexander KiesService and Technology Marketing, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University), Aachen, Germany.ORCID https://orcid.org/0000-0002-0300-8142
Saskia K NagelApplied Ethics, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University), Aachen, Germany.ORCID https://orcid.org/0000-0001-9657-5121
Fabian KiesslingInstitute for Experimental Molecular Imaging, Rheinisch-Westfälische Technische Hochschule Aachen (RWTH Aachen University), Aachen, Germany.ORCID https://orcid.org/0000-0002-7341-0399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) has the potential to transform clinical practice and diagnostics. Amid workforce shortages, AI-based applications assist in decision-making, patient monitoring, and administrative tasks. However, despite enthusiasm, integration into clinical practice remains limited because of concerns about usability, ethical implications, and physicians' acceptance. Understanding physicians' attitudes and engaging them in AI development may foster acceptance and adoption.

objectiveThis study aimed to comprehensively assess physicians' attitudes toward AI in medicine.

methodsWe conducted a mixed methods study combining a web-based survey and qualitative interviews. The survey explored physicians' perspectives on the advantages and disadvantages of AI, its role in decision-making, and impact on physician-patient communication. Attitudes were measured using a 5-point Likert scale, covering cognitive and affective dimensions. An exploratory factor analysis (EFA) identified underlying attitudinal factors, while the Mann-Whitney U and the Kruskal-Wallis tests examined differences in attitudes based on physicians' age, discipline, AI familiarity, and other variables. Overall, 13 physicians, independent of the survey sample, participated in semistructured interviews, which were analyzed using inductive coding and thematic analysis.

resultsThe survey yielded 498 responses. EFA revealed two factors: (1) AI enthusiasm and acceptance (Cronbach α=0.83) and (2) AI skepticism and apprehension (Cronbach α=0.77). Physicians reported high AI enthusiasm (median 4, IQR 3.57-4.29) and lower skepticism (median 3.62, IQR 3.20-4.20; reverse coded, with higher scores indicating reduced skepticism). Greater AI familiarity, use in daily life or professionally, and involvement in research were strongly associated with greater enthusiasm and lower skepticism. Physicians involved in AI-related research reported significantly higher enthusiasm (mean rank: AI research=111.52; no AI research=54.32; P<.001) and lower skepticism (mean rank: AI research=108.27; no AI research=70.45; P=.01). Those using AI professionally or intending to do so similarly expressed high enthusiasm (mean rank: professional use=253.88; no use=196.17; P=.001) and lower skepticism (mean rank: plan to use=275.93; no use=218.86; P=.001). Greater familiarity with AI tools was also strongly associated with higher enthusiasm (mean rank: very familiar=323.55; not familiar=169.86; P<.001) and lower skepticism (mean rank: very familiar=296.90; not familiar=186.23; P=.008). Chief physicians (mean rank 277.32) were significantly less skeptical than residents (mean rank 210.60; P=.01); however, age and discipline did not influence attitudes. The qualitative analysis identified six themes shaping physicians' attitudes: (1) status quo, (2) AI dependency and negligence, (3) role changes and needs, (4) AI transparency and decision-making, (5) the physician-patient relationship, and (6) a framework for responsible AI integration. These findings led to several key propositions considered critical for AI adoption.

conclusionsAI in medicine is viewed positively, with attitudes shaped more by experience and engagement than by demographic factors. While concerns persist, they diminish with increased familiarity and professional use. These findings highlight the need for targeted education, hands-on training, and standardized implementation strategies to enhance AI engagement and facilitate adoption.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelPhysiciansAdultFemaleHumansInterviews as TopicMaleMiddle AgedSurveys and QuestionnairesAIAI adoptionartificial intelligenceattitudeshealth caremedical AImedical diagnosticsmedical ethicsmixed methodsqualitative interviewsurvey

Identifiers

PMID40857713
PMCPMC12421205

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.