Evaluating the Quality and Understandability of Radiology Report Summaries Generated by ChatGPT: Survey Study
Notice bibliographique
Résumé
Background: Radiology reports convey critical medical information to health care providers and patients. Unfortunately, they are often difficult for patients to comprehend, causing confusion and anxiety, thereby limiting patient engagement in health care decision-making. Large language models (LLMs) like ChatGPT (OpenAI) can create simplified, patient-friendly report summaries to increase accessibility, albeit with errors. Objective: We evaluated the accuracy and clarity of ChatGPT-generated summaries compared to original radiologist-assessed radiology reports, assessed patients' understanding and satisfaction with the summaries compared to the original reports, and compared the readability of the original reports and summaries using validated readability metrics. Methods: We anonymized 30 radiology reports created by neuroradiologists at our institution (6 brain magnetic resonance imaging, 6 brain computed tomography, 6 head and neck computed tomography angiography, 6 neck computed tomography, and 6 spine computed tomography). These anonymized reports were processed by ChatGPT to produce patient-centric summaries. Four board-certified neuroradiologists evaluated the ChatGPT-generated summaries on quality and accuracy compared to the original reports, and 4 patient volunteers separately evaluated the reports and summaries on perceived understandability and satisfaction. Readability was assessed using word count and validated readability scales. Results: After reading the summary, patient confidence in understanding (98%, 116/118 vs 26%, 31/118) and satisfaction regarding the level of jargon/terminology (91%, 107/118 vs 8%, 9/118) and time taken to understand the content (97%, 115/118 vs 23%, 27/118) substantially improved. Ninety-two percent (108/118) of responses indicated the summary clarified patients' questions about the report, and 98% (116/118) of responses indicated patients would use the summary if available, with 67% (79/118) of responses indicating they would want access to both the report and summary, while 26% (31/118) of responses indicated only wanting the summary. Eighty-three percent (100/120) of radiologist responses indicated the summary represented the original report "extremely well" or "very well," with only 5% (6/120) of responses indicating it did so "slightly well" or "not well at all." Five percent (6/120) of responses indicated there was missing relevant medical information in the summary, 12% (14/120) reported instances of overemphasis of nonsignificant findings, and 18% (22/120) reported instances of underemphasis of significant findings. No fabricated findings were identified. Overall, 83% (99/120) of responses indicated that the summary would definitely/probably not lead patients to incorrect conclusions about the original report, with 10% (12/120) of responses indicating the summaries may do so. Conclusions: ChatGPT-generated summaries could significantly improve perceived comprehension and satisfaction while accurately reflecting most key information from original radiology reports. Instances of minor omissions and under-/overemphasis were noted in some summaries, underscoring the need for ongoing validation and oversight. Overall, these artificial intelligence-generated, patient-centric summaries hold promise for enhancing patient-centered communication in radiology.
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 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».