RareVA: Adaptive Agentic Verification for Rare Disease Candidate Prioritization from Facial Images under Data Scarcity

Rare disease candidate prioritization from facial images is particularly challenging under extreme data scarcity, where genetic testing and structured clinical annotations may be unavailable and visually similar disorders can exhibit overlapping craniofacial phenotypes. Existing approaches typically formulate this setting as either direct visual classification or end-to-end vision-language reasoning, providing limited mechanisms for explicitly verifying why one candidate disease should be favored over competing alternatives. We propose **RareVA**, an image-first system that formulates rare disease candidate prioritization as candidate-conditioned disease verification. Rather than directly selecting a disease from the visual prediction space, RareVA first identifies a small set of plausible candidates, extracts image-derived facial phenotypes through a vision-language model, and verifies each candidate through agentic reasoning over evidence retrieved from a disease-phenotype knowledge graph. To improve computational efficiency, RareVA further introduces Adaptive Candidate Selection (ACS), which dynamically allocates the number of candidates requiring verification according to case difficulty. We evaluate RareVA on RDFace and further assess generalization on the independent GMDB benchmark. RareVA improves Top-1 accuracy from 25.66% to 34.51% on RDFace and from 7.56% to 12.25% on GMDB. ACS further reduces average output-token cost by 17.15% relative to the best-performing fixed-budget configuration while achieving comparable observed accuracy. These results support candidate-conditioned verification as a formulation for evidence-grounded rare disease candidate prioritization from facial images under severe data scarcity.