Evaluating the Effectiveness of Generative AI for the Creation of Patient Education Materials on Coronary Heart Disease: A Comparative Study
Notice bibliographique
Résumé
BACKGROUND: Generative Artificial Intelligence (AI) has shown great potential in various fields, including healthcare. However, its application in developing patient education materials(PEMs), particularly those with coronary heart disease (CHD), remains underexplored. Traditional methods for creating these materials are time-consuming and lack personalization, which limits their effectiveness. OBJECTIVE: This study aims to explore the effectiveness of Generative AI tools(ChatGPT and DeepSeek) in generating PEMs for CHD patients and to compare them with materials developed by a professional medical team. METHODS: In February 2025, PEMs for CHD patients were developed using a framework designed by a professional medical team. Structured prompts were used to generate materials through two Generative AI models-ChatGPT-4o and DeepSeek R1. These AI-generated materials were compared with those created by the medical team in terms of development time, readability, understandability, actionability, and accuracy. RESULTS: The total time for manual preparation was 14 hours, while ChatGPT and DeepSeek consumed 0.62 and 0.78 hours, respectively. Regarding readability, the frequency of difficult words was more variable in Manually Written and ChatGPT materials, while DeepSeek showed more consistency. The proportion of simple sentences was highest in DeepSeek, followed by ChatGPT, with complete separation between Manually Written and ChatGPT (δ = 1). Content word frequency was highest in Manually Written, while ChatGPT had the lowest but most stable values. Personal pronouns were most frequent in Manually Written, with high variability, and least in DeepSeek, which was stable.All three groups had similar readability levels, reached Chinese elementary school-level readability for simple sentence proportion and personal pronouns, with high school-level difficulty words and content word frequency. The understandability and actionability scores were above 70, with ChatGPT being more stable in understandability, and DeepSeek in actionability. No significant differences were found between groups..In terms of accuracy, inter-group comparisons showed significant differences (H = 7.27, P = .03), but no significant differences in multiple comparisons. The direct comparison between ChatGPT and DeepSeek showed a negligible effect size (δ = .02), with no significant difference (Z = -.06, P = .96) . Four out of eight experts noted accuracy issues in the AI-generated materials. CONCLUSIONS: Generative AI significantly improved the efficiency of developing PEMs for CHD patients. The materials generated by ChatGPT-4o and DeepSeek R1 were comparable to the professionally written ones in terms of readability, understandability, and actionability. However, improvements in reducing difficult words and increasing content word frequency are needed to enhance readability. The accuracy of AI-generated materials still poses concerns, including potential AI "hallucinations" and requires review by healthcare professionals. Generative AI holds considerable potential in generating PEMs, and future research should assess its applicability and effectiveness in real-world patient and family contexts.
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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 | 0,021 | 0,088 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 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 ».