医疗健康领域的开放基础模型：GenAI驱动的个性化处方所面临的挑战、悖论与机遇
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription
AI总结：
本文综述了开源医疗健康LLMs和AIFMs的现状并提出分类法，通过个性化处方案例研究证明开放模型结合RAG等grounding技术可达到与专有模型相当的性能，同时讨论了伦理风险与负责任实施的必要性。
摘要：
鉴于OpenAI的GPT-4等专有大型语言模型（LLMs）取得的成功，人们越来越关注开发开放、非专有的LLMs和AI基础模型（AIFMs），以用于学术、科学和非商业应用中的透明使用。尽管开放模型无法匹敌其专有对应模型所具备的精细化功能，但它们仍拥有革新医疗健康应用的巨大潜力。在本文中，我们考察了开源LLMs和AIFMs在开发医疗健康应用方面的前景，并做出了两项关键贡献。首先，我们对当前最先进的开源医疗健康LLMs和AIFMs进行了全面综述，并提出了这些开放AIFMs的分类法，对其在各种医疗健康任务中的效用进行了归类。其次，为了评估开放LLMs在医疗健康中的通用应用，我们提出了一个关于个性化处方的案例研究。该任务尤为重要，因为它在提供量身定制、针对特定患者的药物治疗方面发挥着关键作用，能够极大改善治疗结果。此外，我们比较了开源模型与专有模型在有无检索增强生成（RAG）设置下的性能。我们的研究结果表明，尽管开放LLMs不够精细，但在与RAG等 grounding 技术相结合时，其性能可以达到与专有模型相当的水平。此外，为了突出LLMs赋能的个性化处方的临床意义，我们通过一位临床专家进行了主观评估。我们还详细阐述了与滥用强大LLMs和AIFMs相关的伦理考量和潜在风险，强调了在医疗健康领域谨慎且负责任地实施这些模型的必要性。
英文摘要：
In response to the success of proprietary Large Language Models (LLMs) such as OpenAI's GPT-4, there is a growing interest in developing open, non-proprietary LLMs and AI foundation models (AIFMs) for transparent use in academic, scientific, and non-commercial applications. Despite their inability to match the refined functionalities of their proprietary counterparts, open models hold immense potential to revolutionize healthcare applications. In this paper, we examine the prospects of open-source LLMs and AIFMs for developing healthcare applications and make two key contributions. Firstly, we present a comprehensive survey of the current state-of-the-art open-source healthcare LLMs and AIFMs and introduce a taxonomy of these open AIFMs, categorizing their utility across various healthcare tasks. Secondly, to evaluate the general-purpose applications of open LLMs in healthcare, we present a case study on personalized prescriptions. This task is particularly significant due to its critical role in delivering tailored, patient-specific medications that can greatly improve treatment outcomes. In addition, we compare the performance of open-source models with proprietary models in settings with and without Retrieval-Augmented Generation (RAG). Our findings suggest that, although less refined, open LLMs can achieve performance comparable to proprietary models when paired with grounding techniques such as RAG. Furthermore, to highlight the clinical significance of LLMs-empowered personalized prescriptions, we perform subjective assessment through an expert clinician. We also elaborate on ethical considerations and potential risks associated with the misuse of powerful LLMs and AIFMs, highlighting the need for a cautious and responsible implementation in healthcare.
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。
