Evidence map›Paper›PMID 41417951›Full record

ArticleJMIR formative research2026

Assessment of Physician Preferences for Large Language Model-Generated Responses Across Geographic Regions and Clinical Experience Levels: Preliminary Survey Study.

James S Brooks, Paa-Kwesi Blankson, Peter Murphy Campbell, R Adams Cowley, Tsorng-Shyang Yang, Tijani Oseni, Anny Rodriguez, Muhammed Y Idris

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. Cited by 1 paper.

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0cells of the map it votes in
1citing 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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

8 authors.

James S BrooksMorehouse School of Medicine, Atlanta, GA, United States.ORCID 0009-0003-9642-8203
Paa-Kwesi BlanksonMorehouse School of Medicine, Atlanta, GA, United States.ORCID 0000-0003-3726-3501
Peter Murphy CampbellIcahn School of Medicine at Mount Sinai, New York, NY, United States.ORCID 0009-0002-7802-7628
R Adams CowleyDepartment of Orthopedic Surgery, MedStar Georgetown University Hospital, Georgetown, DC, United States.ORCID 0000-0003-0996-5223
Tsorng-Shyang YangDa'an District, 280 Ren'ai Road, Section 4, Cathay General Hospital, Taipei City, Taiwan.ORCID 0009-0008-4336-5954
Tijani OseniDepartment of Family Medicine, Edo University Iyamho, Edo State, Nigeria.ORCID 0000-0001-5301-1983
Anny RodriguezMorehouse School of Medicine, Atlanta, GA, United States.ORCID 0000-0001-5863-1129
Muhammed Y IdrisMorehouse School of Medicine, Atlanta, GA, United States.ORCID 0000-0002-3899-3728

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)-generated responses compared to those authored by human clinicians.

objectiveThis study examined physician evaluations of LLM-generated versus physician-authored responses to real-world patient questions, comparing preference patterns across geographic regions and years in clinical practice.

methodsWe conducted a cross-sectional online survey between March and May 2025 among licensed physicians recruited internationally. Participants reviewed anonymized medical responses from 2 LLMs (GPT-4.0 and Meta AI) and verified physicians to questions sourced from Reddit's r/AskDocs forum. Each participant ranked 3 responses per question (1=most preferred; 3=least preferred) according to accuracy and responsiveness. Mean ranks, pairwise win proportions, and full rank distributions were analyzed descriptively and stratified by geographic region and years in practice.

resultsOverall, LLM-generated responses were strongly preferred. GPT-4.0 achieved the best mean rank (1.63, SD 0.68; 95% CI 1.52-1.74), followed by Meta AI (1.83, SD 0.72; 95% CI 1.71-1.94), while verified physician-authored responses were least preferred (2.53, SD 0.76; 95% CI 2.40-2.65). In pairwise analyses, responses generated by GPT-4.0 won 78% (118/150) of the head-to-head comparisons versus physician-authored responses and 57% (86/150) versus Meta AI responses. Preference for GPT-4.0 was most pronounced in Africa (mean 1.59, SD 0.72), Asia (mean 1.91, SD 0.83), and North America (mean 1.55, SD 0.60), while Meta AI slightly led in Europe (mean 1.33, SD 0.57) and the Americas (mean 1.75). Across experience levels, physicians with less than 5 years in practice (28/52, 54%) ranked GPT-4.0 most favorably (mean 1.58, SD 0.63), followed by those with 10 to 15 years of experience (mean 1.56, SD 0.72). Even among physicians with more than 15 years in practice (9/52, 17%), AI-generated responses outperformed physician-authored responses (mean 1.75 vs 2.62). Across all subgroups, human-authored responses were ranked lowest.

conclusionsThis exploratory study demonstrates that physicians across diverse regions and experience levels generally prefer LLM-generated responses to human-authored ones. The consistency of this finding across continents and practice durations underscores growing professional acceptance of AI as a viable tool for patient communication. These results suggest that modern LLMs, particularly GPT-4.0, may provide clinically acceptable, contextually relevant, and user-trusted health information, with potential to augment physician workflows and patient education.

Indexed as

Artificial IntelligenceLanguagePhysiciansAdultCross-Sectional StudiesFemaleHumansLarge Language ModelsMaleMiddle AgedSurveys and QuestionnairesAIartificial intelligenceglobal healthhealth communicationlarge language modelphysician

Identifiers

PMID41417951
PMCPMC12892026

What OpenQuestion holds

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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.