数字诊断：大型语言模型在识别常见疾病症状方面的潜力
Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnesses
AI总结：
本文评估GPT-4、Gemini和GPT-3.5基于症状识别常见疾病的诊断能力，发现三者各具优势，并强调临床应用需重视隐私、合规与伦理偏见。
摘要：
近期，GPT-4、Gemini和GPT-3.5等LLM（大型语言模型）的快速发展为医学和医疗保健，尤其是数字诊断，带来了变革性机遇。本研究通过解读用户症状并确定与常见疾病高度吻合的诊断，评估了每个模型的诊断能力，并展示了这些模型各自如何能够显著提高诊断准确性和效率。通过一系列基于医学数据库中症状的诊断提示，GPT-4凭借其在医学数据方面深入而完整的训练历史，展现出更高的诊断准确性。与此同时，Gemini作为疾病分诊中的关键工具表现出高精度，证明了其在医生尝试进行高风险诊断时成为可靠模型的潜力。GPT-3.5虽然略微不够先进，但仍是医学诊断的良好工具。本研究强调，需要更加谨慎和重视地研究LLM在医疗保健和临床实践中的应用，确保任何使用LLM的系统都能促进患者隐私，并遵守HIPAA合规等健康信息隐私法律，同时关注在复杂医疗环境中影响不同个体的社会后果。本研究标志着未来一项更宏大工作的开端：研究将伦理关切赋予LLM从人类偏见中学习这一任务的多种方式，如何能够发掘在复杂医疗场景中应用AI的新途径。
英文摘要：
The recent swift development of LLMs like GPT-4, Gemini, and GPT-3.5 offers a transformative opportunity in medicine and healthcare, especially in digital diagnostics. This study evaluates each model diagnostic abilities by interpreting a user symptoms and determining diagnoses that fit well with common illnesses, and it demonstrates how each of these models could significantly increase diagnostic accuracy and efficiency. Through a series of diagnostic prompts based on symptoms from medical databases, GPT-4 demonstrates higher diagnostic accuracy from its deep and complete history of training on medical data. Meanwhile, Gemini performs with high precision as a critical tool in disease triage, demonstrating its potential to be a reliable model when physicians are trying to make high-risk diagnoses. GPT-3.5, though slightly less advanced, is a good tool for medical diagnostics. This study highlights the need to study LLMs for healthcare and clinical practices with more care and attention, ensuring that any system utilizing LLMs promotes patient privacy and complies with health information privacy laws such as HIPAA compliance, as well as the social consequences that affect the varied individuals in complex healthcare contexts. This study marks the start of a larger future effort to study the various ways in which assigning ethical concerns to LLMs task of learning from human biases could unearth new ways to apply AI in complex medical settings.
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。
