A Proof-of-Concept for Explainable Disease Diagnosis Using Large Language Models and Answer Set Programming
This work addresses the challenge of constructing knowledge bases for disease diagnosis, potentially benefiting medical professionals, but it is incremental as it builds on existing methods.
The paper tackles the problem of limited adoption of symbolic AI in healthcare by introducing McCoy, a framework that combines Large Language Models with Answer Set Programming to translate medical literature into code for disease diagnosis, achieving strong performance on small-scale tasks.
Accurate disease prediction is vital for timely intervention, effective treatment, and reducing medical complications. While symbolic AI has been applied in healthcare, its adoption remains limited due to the effort required for constructing high-quality knowledge bases. This work introduces McCoy, a framework that combines Large Language Models (LLMs) with Answer Set Programming (ASP) to overcome this barrier. McCoy orchestrates an LLM to translate medical literature into ASP code, combines it with patient data, and processes it using an ASP solver to arrive at the final diagnosis. This integration yields a robust, interpretable prediction framework that leverages the strengths of both paradigms. Preliminary results show McCoy has strong performance on small-scale disease diagnosis tasks.