Towards Explainable Conversational AI for Early Diagnosis with Large Language Models
This addresses the problem of delayed treatment and poor outcomes for patients by providing a more interactive and explainable diagnostic tool, though it is incremental as it builds on existing LLM and explainable AI techniques.
The research tackled inefficient and non-transparent AI diagnostics in healthcare by developing an LLM-based diagnostic chatbot, which achieved 90% accuracy and 100% top-3 accuracy in tests against traditional models.
Healthcare systems around the world are grappling with issues like inefficient diagnostics, rising costs, and limited access to specialists. These problems often lead to delays in treatment and poor health outcomes. Most current AI and deep learning diagnostic systems are not very interactive or transparent, making them less effective in real-world, patient-centered environments. This research introduces a diagnostic chatbot powered by a Large Language Model (LLM), using GPT-4o, Retrieval-Augmented Generation, and explainable AI techniques. The chatbot engages patients in a dynamic conversation, helping to extract and normalize symptoms while prioritizing potential diagnoses through similarity matching and adaptive questioning. With Chain-of-Thought prompting, the system also offers more transparent reasoning behind its diagnoses. When tested against traditional machine learning models like Naive Bayes, Logistic Regression, SVM, Random Forest, and KNN, the LLM-based system delivered impressive results, achieving an accuracy of 90% and Top-3 accuracy of 100%. These findings offer a promising outlook for more transparent, interactive, and clinically relevant AI in healthcare.