Teaching agentic AI to generalize expert diagnostic reasoning in rare diseases
Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer. Large language models rank the correct disease first in only 35.4% of benchmark cases and often rely on learned phenotype-disease associations rather than reusable diagnostic reasoning strategies. We developed liteOdyssey through Policy Iteration with Human Feedback, a process in which model failures and expert corrections are iteratively consolidated into a clinician-gated, natural-language policy executed by a language model. Across 1,243 public benchmark cases spanning 722 rare diseases, liteOdyssey ranked the correct disease first in 59.3% of cases versus 26.5% without the policy, with compara...
| Authors | Minh-Ha Nguyen, Erica Gray, Bryce A. Schuler et al. |
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| Primary category | cs.AI |
| Published | 15 June 2026 |
| Last updated at source | 7 September 2026 |
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