Evaluating AI Research Quality in Myasthenia Gravis: A Longitudinal Study Using the CLARITY Framework (2020–2024)

Artificial intelligence (AI) is increasingly applied in clinical research to improve diagnostic accuracy, prognostic modeling, and disease monitoring, particularly in complex, data-constrained areas such as rare diseases. In this context, Myasthenia Gravis (MG)—a rare neuromuscular disorder—has seen a growing number of AI-driven studies aimed at addressing its diagnostic and clinical heterogeneity. However, the methodological quality and reporting standards of these studies remain largely unexamined. This study presents the first longitudinal evaluation of AI research in MG using the CLARITY AI framework—a modular tool designed to assess both structural rigor and macro-topical completeness. An analysis of 20 peer-reviewed studies published between 2020 and 2024 revealed that average total scores improved from 235.0 in 2021 to 256.15 in 2024, with notable gains in evaluation metrics (+2.33), data availability (+1.32), and study type and objectives (+1.55). A post hoc robustness check confirmed the stability of these temporal trends. Despite these improvements, critical deficiencies persist, particularly in usability testing, user engagement, and ethical reporting, where scores often remained below 1. These findings indicate that while technical sophistication is advancing, translational readiness continues to be limited by the underreporting of human-centered and ethical dimensions. This work provides actionable insights and establishes a benchmark for improving transparency, methodological rigor, and clinical relevance in AI applications for rare diseases.