aceDiagnosing Rare Genetic Diseases From Facial Images Using Visual Prompting With Multimodal Large Language Models: Diagnostic Accuracy Study
Background: Rare genetic diseases often present with recognizable facial phenotypes. Whether multimodal large language models (MLLMs) can prioritize the diagnosis of rare genetic diseases from facial images and whether image enhancement or visual prompting can improve this process remain unclear. Objective: This study aimed to evaluate the diagnostic accuracy of MLLMs for rare genetic diseases using facial images and to assess whether super-resolution enhancement or red-circle visual prompting improved performan…
Three open-weight multimodal AI models identified rare genetic diseases from facial images poorly, and on the rarest diseases a random baseline of 8.15% beat all three models on raw images.
Results apply only to the evaluated experimental settings and three models; the study lacked physician comparisons and prospective multicenter data.
Evidence
- Peer-reviewedJMIR Medical Informatics2026-11-02
How should this claim be treated?
Truvace Impact Record TRV-2026-2339, v1: “Diagnosing Rare Genetic Diseases From Facial Images Using Visual Prompting With Multimodal Large Language Models: Diagnostic Accuracy Study.” Truvace, 2026-11-03. /record/TRV-2026-2339 (accessed at citation time). sha256 6237cf3f95c9d4f0…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2339 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.