LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis

TL;DR

This paper introduces a privacy-preserving, fine-tuned LLaMA-v3-based platform for medical document analysis that improves differential diagnosis accuracy and provides explainable results, supporting clinical decision-making.

Contribution

It presents a novel approach using low-rank adaptation to fine-tune LLaMA-v3 for differential diagnosis, utilizing the DDXPlus dataset and ensuring explainability and privacy in medical AI.

Findings

Superior performance in pathology prediction and differential diagnosis.

Effective explainability techniques enhance trust and transparency.

Outperforms existing models in accuracy and practical utility.

Abstract

Medical document analysis plays a crucial role in extracting essential clinical insights from unstructured healthcare records, supporting critical tasks such as differential diagnosis. Determining the most probable condition among overlapping symptoms requires precise evaluation and deep medical expertise. While recent advancements in large language models (LLMs) have significantly enhanced performance in medical document analysis, privacy concerns related to sensitive patient data limit the use of online LLMs services in clinical settings. To address these challenges, we propose a trustworthy medical document analysis platform that fine-tunes a LLaMA-v3 using low-rank adaptation, specifically optimized for differential diagnosis tasks. Our approach utilizes DDXPlus, the largest benchmark dataset for differential diagnosis, and demonstrates superior performance in pathology prediction and variable-length differential diagnosis compared to existing methods. The developed web-based platform allows users to submit their own unstructured medical documents and receive accurate, explainable diagnostic results. By incorporating advanced explainability techniques, the system ensures transparent and reliable predictions, fostering user trust and confidence. Extensive evaluations confirm that the proposed method surpasses current state-of-the-art models in predictive accuracy while offering practical utility in clinical settings. This work addresses the urgent need for reliable, explainable, and privacy-preserving artificial intelligence solutions, representing a significant advancement in intelligent medical document analysis for real-world healthcare applications. The code can be found at \href{https://github.com/leitro/Differential-Diagnosis-LoRA}{https://github.com/leitro/Differential-Diagnosis-LoRA}.