Comparison of ChatGPT and Internet Research for Clinical Research and Decision-Making in Occupational Medicine: Randomized Controlled Trial
Notice bibliographique
Résumé
Background: Artificial intelligence is becoming a part of daily life and the medical field. Generative artificial intelligence models, such as GPT-4 and ChatGPT, are experiencing a surge in popularity due to their enhanced performance and reliability. However, the application of these models in specialized domains, such as occupational medicine, remains largely unexplored. Objective: This study aims to assess the potential suitability of a generative large language model, such as ChatGPT, as a support tool for medical research and even clinical decisions in occupational medicine in Germany. Methods: In this randomized controlled study, the usability of ChatGPT for medical research and clinical decision-making was investigated using a web application developed for this purpose. Eligibility criteria were being a physician or medical student. Participants (N=56) were asked to work on 3 cases of occupational lung diseases and answer case-related questions. They were allocated via coin weighted for proportions of physicians in each group into 2 groups. One group researched the cases using an integrated chat application similar to ChatGPT based on the latest GPT-4-Turbo model, while the other used their usual research methods, such as Google, Amboss, or DocCheck. The primary outcome was case performance based on correct answers, while secondary outcomes included changes in specific question accuracy and self-assessed occupational medicine expertise before and after case processing. Group assignment was not traditionally blinded, as the chat window indicated membership; participants only knew the study examined web-based research, not group specifics. Results: Participants of the ChatGPT group (n=27) showed better performance in specific research, for example, for potentially hazardous substances or activities (eg, case 1: ChatGPT group 2.5 hazardous substances that cause pleural changes versus 1.8 in a group with own research; P=.01; Cohen r=-0.38), and led to an increase in self-assessment with regard to specialist knowledge (from 3.9 to 3.4 in the ChatGPT group vs from 3.5 to 3.4 in the own research group; German school grades between 1=very good and 6=unsatisfactory; P=.047). However, clinical decisions, for example, whether an occupational disease report should be filed, were more often made correctly as a result of the participant's own research (n=29; eg, case 1: Should an occupational disease report be filed? Yes for 7 participants in the ChatGPT group vs 14 in their own research group; P=.007; odds ratio 6.00, 95% CI 1.54-23.36). Conclusions: ChatGPT can be a useful tool for targeted medical research, even for rather specific questions in occupational medicine regarding occupational diseases. However, clinical decisions should currently only be supported and not made by the large language model. Future systems should be critically assessed, even if the initial results are promising.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,005 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».