Application of Large Language Models in Data Analysis and Medical Education for Assisted Reproductive Technology: Comparative Study
Notice bibliographique
Résumé
Background: Recent studies have demonstrated that large language models exhibit exceptional performance in medical examinations. However, there is a lack of reports assessing their capabilities in specific domains or their application in practical data analysis using code interpreters. Furthermore, comparative analyses across different large language models have not been extensively conducted. Objective: The purpose of this study was to evaluate whether advanced artificial intelligence (AI) models can analyze data from template-based input and demonstrate basic knowledge of reproductive medicine. Four AI models (GPT-4, GPT-4o, Claude 3.5 Sonnet, and Gemini Pro 1.5) were evaluated for their data analytical capabilities through numerical calculations and graph rendering. Their knowledge of infertility treatment was assessed using 10 examination questions developed by experts. Methods: First, we uploaded data to the AI models and furnished instruction templates using the chat interface. The study investigated whether the AI models could perform pregnancy rate analysis and graph rendering, based on blastocyst grades according to Gardner criteria. Second, we assessed model diagnostic capabilities based on specialized knowledge. This evaluation used 10 questions derived from the Japanese Fertility Specialist Examination and the Embryologist Certification Exam, along with chromosome imaging. These materials were curated under the supervision of certified embryologists and fertility specialists. All procedures were repeated 10 times per AI model. Results: GPT-4o achieved grade A output (defined as achieving the objective with a single output attempt) in 9 out of 10 trials, outperforming GPT-4, which achieved grade A in 7 out of 10. The average processing times for data analysis were 26.8 (SD 3.7) seconds for GPT-4o and 36.7 (SD 3) seconds for GPT-4, whereas Claude failed in all 10 attempts. Gemini achieved an average processing time of 23 (SD 3) seconds and received grade A in 6 out of 10 trials, though occasional manual corrections were needed. Embryologists required an average of 358.3 (SD 9.7) seconds for the same tasks. In the knowledge-based assessment, GPT-4o, Claude, and Gemini achieved perfect scores (9/9) on multiple-choice questions, while GPT-4 showed a 60% (6/10) success rate on 1 question. None of the AI models could reliably diagnose chromosomal abnormalities from karyotype images, with the highest image diagnostic accuracy being 70% (7/10) for Claude and Gemini. Conclusions: This rapid processing demonstrates the potential for these AI models to significantly expedite data-intensive tasks in clinical settings. This performance underscores their potential utility as educational tools or decision support systems in reproductive medicine. However, none of the models were able to accurately interpret and diagnose using medical images.
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 | 0,013 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».