Abstract Chaudhary and Kumar’s review, “Rare diseases: a comprehensive literature review and future directions,” provides a timely synthesis of epidemiology, diagnostics, therapeutics, and policy challenges across rare diseases, highlighting genomics, artificial intelligence (AI), and orphan drug development as key drivers of progress. Building on this foundation, this commentary proposes a complementary, operational perspective focused on translating narrative insights into an equity-first learning health system (LHS) for rare diseases. Rather than replacing established epidemiological and registry frameworks, three pragmatic extensions are discussed: complementing prevalence and incidence estimates with patient-centred burden-of-disease metrics; evolving isolated registries toward interoperable and incrementally federated data ecosystems that support learning across health systems; and reframing AI research from proof-of-concept studies toward clinically validated tools with measurable decision-level impact. Emphasis is placed on feasibility, transparency, and global inclusivity, particularly for under-represented regions. By aligning innovation with real-world outcomes and equity considerations, this commentary aims to stimulate discussion on how emerging data and AI capabilities can be more effectively integrated into rare disease research, policy, and clinical practice.
M Vijayasimha (Wed,) studied this question.