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Open LLMs show promise in personalized healthcare prescriptions

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper explores the use of open foundation models in healthcare, specifically for personalized prescription generation. Researchers evaluated several open-weight large language models (LLMs) against a proprietary baseline, finding that the open models performed comparably or better in accuracy without retrieval augmentation. The study also investigated the impact of retrieval-augmented generation (RAG), noting improvements in accuracy but mixed results in subjective rater evaluations. The paper concludes by discussing the ethical implications and risks of deploying these advanced LLMs in healthcare. AI

IMPACT Open-source LLMs demonstrate competitive performance in specialized healthcare tasks, potentially increasing accessibility and transparency in medical AI applications.

RANK_REASON The cluster contains an academic paper detailing research findings on AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Open LLMs show promise in personalized healthcare prescriptions

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The cluster contains an academic paper detailing research findings on AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mahdi Alkaeed, Sofiat Abioye, Adnan Qayyum, Yosra Magdi Mekki, Ilhem Berrou, Mohamed Abdallah, Ala Al-Fuqaha, Muhammad Bilal, Junaid Qadir ·

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

    arXiv:2502.04356v2 Announce Type: replace-cross 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…

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