医疗健康洞察中 Drug-GPT 与 ChatGPT 大语言模型的比较分析：评估患者和 HCP 语境下的准确性与相关性
Comparative Analysis of Drug-GPT and ChatGPT LLMs for Healthcare Insights: Evaluating Accuracy and Relevance in Patient and HCP Contexts
AI总结：
本研究在医疗问答场景下比较了 Drug-GPT 3、Drug-GPT 4 和 ChatGPT，发现基于患者和 HCP 社交媒体精选数据集的 Drug-GPT 模型比特通用 ChatGPT 提供更具针对性和深度的见解。
摘要：
本研究在问答（Q&A）环境下对三种生成式预训练 Transformer（GPT）解决方案进行了比较分析：Drug-GPT 3、Drug-GPT 4 和 ChatGPT，背景为医疗保健应用。目的是确定哪个模型能针对特应性皮炎（AD）患者体验以及医疗保健专业人员（HCP）关于糖尿病的讨论提示，提供最准确和相关的信息。结果表明，虽然三个模型都能生成相关且准确的回答，但由患者和 HCP 社交媒体及留言板帖子精选数据集支持的 Drug-GPT 3 和 Drug-GPT 4 提供了更有针对性和深度的见解。作为更通用的模型，ChatGPT 生成更广泛和通用的回答，这对寻求对主题高层次理解的读者可能很有价值，但可能缺乏专业 Drug-GPT 模型生成答案中的深度和个人见解。此比较分析强调了在评估医疗保健应用中生成信息的有用性时，考虑语言模型的视角、知识深度和时效性的重要性。
英文摘要：
This study presents a comparative analysis of three Generative Pre-trained Transformer (GPT) solutions in a question and answer (Q&A) setting: Drug-GPT 3, Drug-GPT 4, and ChatGPT, in the context of healthcare applications. The objective is to determine which model delivers the most accurate and relevant information in response to prompts related to patient experiences with atopic dermatitis (AD) and healthcare professional (HCP) discussions about diabetes. The results demonstrate that while all three models are capable of generating relevant and accurate responses, Drug-GPT 3 and Drug-GPT 4, which are supported by curated datasets of patient and HCP social media and message board posts, provide more targeted and in-depth insights. ChatGPT, a more general-purpose model, generates broader and more general responses, which may be valuable for readers seeking a high-level understanding of the topics but may lack the depth and personal insights found in the answers generated by the specialized Drug-GPT models. This comparative analysis highlights the importance of considering the language model's perspective, depth of knowledge, and currency when evaluating the usefulness of generated information in healthcare applications.
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。
