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Record W4416578240 · doi:10.2196/82487

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

2025· article· en· W4416578240 on OpenAlexvenueno aff
J. S. Brooks, Paa‐Kwesi Blankson, Peter Campbell, R. Adams Cowley, Tye Young Yang, Tijani Idris Ahmad Oseni, Anny Rodriguez, Muhammed Y. Idris

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsnot available
Fundersnot available
KeywordsConsistency (knowledge bases)Exploratory researchWorkflowSurvey researchMEDLINESurvey data collectionPatient experiencePreference

Abstract

fetched live from OpenAlex

BACKGROUND: Large 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: Overall, 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. CONCLUSIONS: This 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.529
GPT teacher head0.659
Teacher spread0.130 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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