Evidence map›Paper›PMID 39352738›Full record

ArticleJournal of medical Internet research2024

"Doctor ChatGPT, Can You Help Me?" The Patient's Perspective: Cross-Sectional Study.

Jonas Armbruster, Florian Bussmann, Catharina Rothhaas, Nadine Titze, Paul Alfred Grützner, Holger Freischmidt

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 2 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, 2 syntheses or guidelines pooled it.

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  8. Diagnostic accuracy and citation integrity of four large language models on otolaryngology vignettes.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 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

6 authors.

Jonas ArmbrusterDepartment of Trauma and Orthopedic Surgery, BG Klinik Ludwigshafen, Ludwigshafen am Rhein, Germany.ORCID 0009-0007-2486-665X
Florian BussmannDepartment of Trauma and Orthopedic Surgery, BG Klinik Ludwigshafen, Ludwigshafen am Rhein, Germany.ORCID 0009-0007-1494-0895
Catharina RothhaasDepartment of Trauma and Orthopedic Surgery, BG Klinik Ludwigshafen, Ludwigshafen am Rhein, Germany.ORCID 0009-0003-7555-8447
Nadine TitzeDepartment of Trauma and Orthopedic Surgery, BG Klinik Ludwigshafen, Ludwigshafen am Rhein, Germany.ORCID 0009-0007-9472-4921
Paul Alfred GrütznerDepartment of Trauma and Orthopedic Surgery, BG Klinik Ludwigshafen, Ludwigshafen am Rhein, Germany.ORCID 0000-0002-0730-4782
Holger FreischmidtDepartment of Trauma and Orthopedic Surgery, BG Klinik Ludwigshafen, Ludwigshafen am Rhein, Germany.ORCID 0000-0001-7309-478X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence and the language models derived from it, such as ChatGPT, offer immense possibilities, particularly in the field of medicine. It is already evident that ChatGPT can provide adequate and, in some cases, expert-level responses to health-related queries and advice for patients. However, it is currently unknown how patients perceive these capabilities, whether they can derive benefit from them, and whether potential risks, such as harmful suggestions, are detected by patients.

objectiveThis study aims to clarify whether patients can get useful and safe health care advice from an artificial intelligence chatbot assistant.

methodsThis cross-sectional study was conducted using 100 publicly available health-related questions from 5 medical specialties (trauma, general surgery, otolaryngology, pediatrics, and internal medicine) from a web-based platform for patients. Responses generated by ChatGPT-4.0 and by an expert panel (EP) of experienced physicians from the aforementioned web-based platform were packed into 10 sets consisting of 10 questions each. The blinded evaluation was carried out by patients regarding empathy and usefulness (assessed through the question: "Would this answer have helped you?") on a scale from 1 to 5. As a control, evaluation was also performed by 3 physicians in each respective medical specialty, who were additionally asked about the potential harm of the response and its correctness.

resultsIn total, 200 sets of questions were submitted by 64 patients (mean 45.7, SD 15.9 years; 29/64, 45.3% male), resulting in 2000 evaluated answers of ChatGPT and the EP each. ChatGPT scored higher in terms of empathy (4.18 vs 2.7; P<.001) and usefulness (4.04 vs 2.98; P<.001). Subanalysis revealed a small bias in terms of levels of empathy given by women in comparison with men (4.46 vs 4.14; P=.049). Ratings of ChatGPT were high regardless of the participant's age. The same highly significant results were observed in the evaluation of the respective specialist physicians. ChatGPT outperformed significantly in correctness (4.51 vs 3.55; P<.001). Specialists rated the usefulness (3.93 vs 4.59) and correctness (4.62 vs 3.84) significantly lower in potentially harmful responses from ChatGPT (P<.001). This was not the case among patients.

conclusionsThe results indicate that ChatGPT is capable of supporting patients in health-related queries better than physicians, at least in terms of written advice through a web-based platform. In this study, ChatGPT's responses had a lower percentage of potentially harmful advice than the web-based EP. However, it is crucial to note that this finding is based on a specific study design and may not generalize to all health care settings. Alarmingly, patients are not able to independently recognize these potential dangers.

Indexed as

Physician-Patient RelationsAdultArtificial IntelligenceCross-Sectional StudiesEmpathyFemaleHumansInternetMaleMiddle AgedPhysiciansSurveys and QuestionnairesAIartificial intelligencechatbotchatbotsChatGPTempathylarge language modelsLLMpatient educationpatient informationpatient perceptions

Identifiers

PMID39352738
PMCPMC11480680

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.