Challenges of patient-facing generative artificial intelligence in hypertension care: A cross-platform evaluation of the quality, readability, and actionability of LLM-Generated patient education materials.

Objective: To assess the quality, readability, and actionability of hypertension patient education materials generated by six selected patient-facing large language model (LLM) platforms, and to characterize cross-platform heterogeneity to guide the optimization of AI-generated patient education materials. Methods: Six publicly accessible or commonly accessible platforms were evaluated using standardized prompts to generate patient education materials. Understandability and actionability were assessed using the Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P). Information quality was evaluated using the expanded Ensuring Quality Information for Patients scale (EQIP-36), and overall quality was rated using the Global Quality Score (GQS). Readability was compared using seven metrics, including the Flesch Reading Ease Score (FRES) and the Flesch-Kincaid Grade Level (FKGL). Results: Overall educational quality and understandability were generally favorable, but substantial cross-platform heterogeneity was observed. Readability remained challenging, with low FRES values and grade-level indices generally exceeding commonly recommended thresholds for patient education materials. Qwen3-Max-Thinking-Preview achieved the highest PEMAT-P total score (77.00), followed by ChatGPT 5.2-Thinking (72.22). For EQIP-36, Qwen3-Max-Thinking-Preview scored highest (49.28), followed by DeepSeek-R1 (45.48). DeepSeek-R1 generated the most readable materials among the evaluated platforms, with a median FRES of approximately 42.14 and FKGL of approximately 10.10, whereas Qwen3-Max-Thinking-Preview showed lower readability, with a median FRES of approximately 16.82 and FKGL of approximately 14.38. Kimi K2 showed high PEMAT-P understandability (76.90) but low actionability (10.00). Post hoc analyses showed that Qwen3-Max-Thinking-Preview significantly outperformed ERNIE Bot 4.5 Turbo, Doubao, and Kimi K2 on PEMAT-P total score, and outperformed Kimi K2 and ERNIE Bot 4.5 Turbo on EQIP-36. DeepSeek-R1 also outperformed Kimi K2 and ERNIE Bot 4.5 Turbo on EQIP-36. Across content domains, actionability was significantly higher for Daily Care and Prevention than for Basic Understanding of the Disease and Complications, Psychological and Social Aspects. Conclusions: Generative AI shows promise for hypertension patient education, particularly in improving understandability. However, actionability remains a major limitation of current outputs, highlighting the need for platform-aware optimization strategies that explicitly strengthen step-by-step and action-oriented guidance.