Jekyll2026-09-04T15:28:29+00:00https://index.biohackrxiv.org//feed/by_tag/MHA26.xmlPreprints for BioHackathonsGitHub Useryour-email@domain.com2026-09-04T00:00:00+00:002026-09-04T00:00:00+00:00https://index.biohackrxiv.org//2026/09/04/e5g6sGenome and exome sequencing have transformed rare disease diagnosis, yet converting large variant sets into evidence-backed interpretations remains labor-intensive and fragmented. Across Asia, population genomic resources and specialist expertise are expanding, but prioritization, expert review, and reuse of reviewed knowledge are often separated across institutions and countries. We argue that the most useful near-term role of artificial intelligence (AI) is not autonomous variant classification, but reducing the friction between distributed evidence and distributed expert judgment. Building on collaborative platform development by participants from institutions in Japan, Singapore, the Philippines, and Thailand, we propose a common workflow connecting variant prioritization and evidence organization, structured expert review, and reviewed-knowledge sharing. AI can support multilingual phenotype structuring, population-aware candidate prioritization, literature and evidence retrieval, and reuse of previous expert-reviewed records, while final evidence assessment remains expert-governed. Country-operated platforms can preserve local governance and population context while exchanging standardized evidence and interpretations across Asia and contributing appropriate records to global knowledge resources. Initial implementations for variant prioritization and an expert review workspace provide a practical foundation for this model. This Perspective outlines where AI can add value, where human judgment must remain decisive, and how a regionally connected expert-panel network could be built.

]]>Toyofumi Fujiwara