A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians
Notice bibliographique
Résumé
Abstract Background The rapid advancement of generative artificial intelligence (AI) has led to the wide dissemination of models with exceptional understanding and generation of human language. Their integration into healthcare has shown potential for improving medical diagnostics, yet a comprehensive diagnostic performance evaluation of generative AI models and the comparison of their diagnostic performance with that of physicians has not been extensively explored. Methods In this systematic review and meta-analysis, a comprehensive search of Medline, Scopus, Web of Science, Cochrane Central, and MedRxiv was conducted for studies published from June 2018 through December 2023, focusing on those that validate generative AI models for diagnostic tasks. The risk of bias was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Meta-regression was performed to summarize the performance of the models and to compare the accuracy of the models with that of physicians. Results The search resulted in 54 studies being included in the meta-analysis. Nine generative AI models were evaluated across 17 medical specialties. The quality assessment indicated a high risk of bias in the majority of studies, primarily due to small sample sizes. The overall accuracy for generative AI models across 54 studies was 56.9% (95% confidence interval [CI]: 51.0–62.7%). The meta-analysis demonstrated that, on average, physicians exceeded the accuracy of the models (difference in accuracy: 14.4% [95% CI: 4.9–23.8%], p-value =0.004). However, both Prometheus (Bing) and GPT-4 showed slightly better performance compared to non-experts (-2.3% [95% CI: -27.0–22.4%], p-value = 0.848 and -0.32% [95% CI: -14.4–13.7%], p-value = 0.962), but slightly underperformed when compared to experts (10.9% [95% CI: -13.1–35.0%], p-value = 0.356 and 12.9% [95% CI: 0.15–25.7%], p-value = 0.048). The sub-analysis revealed significantly improved accuracy in the fields of Gynecology, Pediatrics, Orthopedic surgery, Plastic surgery, and Otolaryngology, while showing reduced accuracy for Neurology, Psychiatry, Rheumatology, and Endocrinology compared to that of General Medicine. No significant heterogeneity was observed based on the risk of bias. Conclusions Generative AI exhibits promising diagnostic capabilities, with accuracy varying significantly by model and medical specialty. Although they have not reached the reliability of expert physicians, the findings suggest that generative AI models have the potential to enhance healthcare delivery and medical education, provided they are integrated with caution and their limitations are well-understood. Key Points Question: What is the diagnostic accuracy of generative AI models and how does this accuracy compare to that of physicians? Findings: This meta-analysis found that generative AI models have a pooled accuracy of 56.9% (95% confidence interval: 51.0–62.7%). The accuracy of expert physicians exceeds that of AI in all specialties, however, some generative AI models are comparable to non-expert physicians. Meaning: The diagnostic performance of generative AI models suggests that they do not match the level of experienced physicians but that they may have potential applications in healthcare delivery and medical education.
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 | 0,015 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,010 | 0,022 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».