迈向具有大语言模型的可解释对话AI用于早期诊断
Towards Explainable Conversational AI for Early Diagnosis with Large Language Models
AI总结：
本研究提出基于大语言模型的可解释对话AI，用于提高早期诊断的透明性和互动性，实验显示其在诊断准确性上优于传统机器学习模型。
摘要：
全球医疗系统正面临效率低下诊断、成本上升和专家资源有限等问题。这些问题往往导致治疗延误和不良健康结果。目前大多数AI和深度学习诊断系统不具有交互性和透明性，使其在以患者为中心的现实环境中效果不佳。本研究介绍了一种由大语言模型（LLM）驱动的诊断聊天机器人，使用GPT-4o、检索增强生成和可解释AI技术。该聊天机器人与患者进行动态对话，帮助提取和标准化症状，通过相似性匹配和自适应提问优先考虑潜在诊断。通过链式思维提示，系统还提供了诊断背后的透明推理。当与传统机器学习模型如朴素贝叶斯、逻辑回归、支持向量机、随机森林和KNN相比时，基于LLM的系统表现出色，准确率达到90%，前三名准确率达到100%。这些发现为更透明、互动和临床相关的医疗AI提供了前景。
英文摘要：
Healthcare systems around the world are grappling with issues such as inefficient diagnostics, rising costs, and limited access to specialists. These challenges often contribute to delays in treatment and poorer health outcomes. Most existing AI and deep learning based health assessment systems offer limited interactivity and transparency, reducing their usefulness for user-centered health support. This research introduces a conversational chatbot powered by a Large Language Model (LLM), using GPT-4o, Retrieval-Augmented Generation, and explainable AI techniques. The chatbot engages users in a dynamic conversation to extract and normalize symptoms while identifying and ranking potential health conditions through similarity matching and adaptive questioning. Using Chain-of-Thought prompting, the system also provides more transparent explanations of its reasoning process. When evaluated against traditional machine learning models, including Naive Bayes, Logistic Regression, SVM, Random Forest, and KNN using both TF-IDF and CountVectorizer feature extraction, the proposed LLM-based system achieved a Top-1 accuracy of 90% and a Top-3 accuracy of 100%. The system was additionally evaluated through a cross-sectional expert evaluation involving 17 physicians across all 14 conditions, with the results indicating generally favorable assessments of conversational quality, early diagnostic plausibility, and safety-related criteria. These findings demonstrate the potential of explainable conversational AI as a health and well-being support tool for early symptom assessment. However, the proposed system is not intended for clinical diagnosis or clinical decision-making, and further validation would be required before any use in healthcare practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
