arXiv：cs.AI· Mahdi Alkaeed, Sofiat Abioye, Adnan Qayyum, Yosra Magdi Mekki, Ilhem Berrou, Mohamed Abdallah, Ala Al-Fuqaha, Muhammad Bilal, Junaid Qadir·· 5 小时前
医疗领域的开放基础模型：GenAI 驱动个性化处方面临的挑战、悖论与机遇
Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription
AI 导读
一篇综述系统梳理了面向临床任务的开放 LLM 与 AI 基础模型，并在个性化处方任务上实测开放权重模型与专有模型。在 100 个合成患者档案上，无检索增强时开放权重模型整体适宜性准确率达 0.810–0.850，高于专有基线的 0.780，但置信区间重叠；加入 RAG 后所有模型均提升，排名差距缩至 0.04 以内。
正文
Abstract:In response to the success of proprietary Large Language Models (LLMs), 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. Open models have substantial potential to improve healthcare applications. In this paper, we provide a comprehensive survey and a taxonomy of state-of-the-art open LLMs and AIFMs for clinical tasks. In addition, we empirically evaluate open-weight LLMs on a personalized-prescription task and compare them with a proprietary model with and without Retrieval-Augmented Generation (RAG). Across 100 synthetic patient profiles, the open-weight models matched or exceeded the proprietary baseline on overall suitability accuracy without retrieval augmentation (0.810--0.850 versus 0.780), although the confidence intervals overlap; with retrieval augmentation, every model improved, and the ranking closed to within 0.04. Gains from RAG were field-dependent rather than uniform. To highlight the clinical relevance of LLM-driven personalized prescriptions, we also conduct a subjective evaluation in which two raters, blinded to model and condition, scored responses independently of each other. Their ratings did not follow the accuracy gains: retrieval raised overall suitability accuracy for every model while lowering the two-rater means for two of the four on all five dimensions. Finally, we outline the ethical implications and potential risks associated with the misuse of advanced LLMs and AIFMs, highlighting the importance of responsible and transparent deployment in healthcare contexts. \textit{Data and code used in the paper will be released upon publication.
| Comments: | 21 pages, 4 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2502.04356 [cs.CL] |
| (or arXiv:2502.04356v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2502.04356 arXiv-issued DOI via DataCite |
Submission history
From: Adnan Qayyum [view email]
[v1]
Tue, 4 Feb 2025 19:16:56 UTC (8,740 KB)
[v2]
Mon, 12 Oct 2026 10:31:22 UTC (3,201 KB)
来源：arXiv：cs.AI · arxiv.org