ArticleFrontiers in digital health2026
Validation of an AI-powered mobile application for personalizing medical note explanations: a mixed-methods evaluation.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Nearly half of adults struggle to understand written health information, making medical communication a persistent barrier to effective care. While artificial intelligence has potential to improve health communication, few patient-facing tools have undergone systematic validation for personalized medical explanation. Methods: Patiently AI is a mobile application designed to clarify clinician-authored medical notes using large language models with audience-specific adaptations (child, teenager, adult, carer) and tone variations (friendly, informative, reassuring). A three-phase mixed-methods evaluation was conducted: (1) computational readability analysis of 210 AI-generated explanations using established metrics; (2) expert review by 15 healthcare professionals assessing medical accuracy, safety, and communication quality; and (3) a patient survey of 54 participants evaluating preferences, comprehension, and acceptance. Results: AI-generated explanations demonstrated consistent improvements in readability, with mean Flesch-Kincaid Grade Level decreasing by 2.96 levels (10.57-7.61), Flesch Reading Ease increasing by 31.9 points (37.7-69.6), and Gunning Fog Index decreasing by 4.09 points (14.5-10.4); all improvements were statistically significant (all Conclusions: This mixed-methods evaluation suggests that a deliberately constrained, language-focused AI system can improve the accessibility of medical notes while preserving clinical accuracy and safety. Patiently AI demonstrates a scalable approach to supporting health literacy and patient engagement without extending into clinical interpretation.
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