Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription

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. Data and code used in the paper will be released upon publication.