Mobile Medical Education Apps as Digital Health Information Resources in China and the United States: Longitudinal App Store Metadata Analysis and Topic Modeling of User Review Narratives

BACKGROUND Mobile learning apps are increasingly used in medical education, but cross-national evidence on their longitudinal development and rating-based satisfaction patterns remains limited. OBJECTIVE This study aimed to examine the longitudinal evolution of medical education apps in China and the United States and identify app-related factors associated with positive and negative rating deviations among app store reviewers. METHODS We conducted a longitudinal app store analysis of 467 medical education apps and 232,598 user reviews from major Chinese and US app stores. Joinpoint regression was used to characterize temporal trends in app development. Latent Dirichlet allocation (LDA) topic modeling, with large language model–assisted topic labeling, was used to identify themes in user reviews. Tobit regression, Wald tests, and Kano model classification were used to examine asymmetric associations with positive and negative rating deviations and to classify review topics into functional attributes. RESULTS Among the 232,598 analyzed reviews, 219,105 were rated 4 stars or higher, corresponding to an overall rating-based satisfaction proxy rate of 94.20%. This rating-based satisfaction proxy rate was lower in China than in the United States (128,018/139,416, 91.82% vs 91,087/93,182, 97.75%). Within the 2025-observable analytic cohort, cumulative observable app counts increased significantly in both countries, with average annual percentage changes of 41.69% in China and 41.17% in the United States. The joinpoint analysis identified temporal inflection points within this observable cohort around 2017 and 2021-2022. User concerns also shifted over time. In China, the dominant review theme changed from “Clinical practicality” in 2009-2017 (2735/7385 reviews, 37.03%) to “Practice test function” in 2018-2025 (53,929/132,031 reviews, 40.85%). In the United States, user attention shifted from “Functional availability” in 2009-2017 (20,736/26,118 reviews, 79.39%) to “Cost” in 2018-2025 (27,401/67,064 reviews, 40.86%). Tobit regression and Kano classification showed that factors associated with positive and negative rating deviations differed by country and developmental stage, with essential and charm factors varying across temporal phases. CONCLUSIONS This longitudinal app store analysis revealed distinct but partially converging patterns of observable app development and rating-based satisfaction among app store reviewers in China and the United States. These findings suggest that future medical education apps should prioritize reliable content, technical stability, affordable exam preparation, and personalized learning support. They may also inform the design of future AI-supported medical education platforms while requiring further validation with learning process and outcome data.