Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’

Peng Zhang, Jiayu Shi and Maged N. Kamel Boulos ()
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Peng Zhang: Department of Computer Science and Data Science Institute, Vanderbilt University, Nashville, TN 37240, USA
Jiayu Shi: Department of Computer Science and Data Science Institute, Vanderbilt University, Nashville, TN 37240, USA
Maged N. Kamel Boulos: School of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal

Future Internet, 2024, vol. 16, issue 12, 1-21

Abstract: The rapid development of specific-purpose Large Language Models (LLMs), such as Med-PaLM, MEDITRON-70B, and Med-Gemini, has significantly impacted healthcare, offering unprecedented capabilities in clinical decision support, diagnostics, and personalized health monitoring. This paper reviews the advancements in medicine-specific LLMs, the integration of Retrieval-Augmented Generation (RAG) and prompt engineering, and their applications in improving diagnostic accuracy and educational utility. Despite the potential, these technologies present challenges, including bias, hallucinations, and the need for robust safety protocols. The paper also discusses the regulatory and ethical considerations necessary for integrating these models into mainstream healthcare. By examining current studies and developments, this paper aims to provide a comprehensive overview of the state of LLMs in medicine and highlight the future directions for research and application. The study concludes that while LLMs hold immense potential, their safe and effective integration into clinical practice requires rigorous testing, ongoing evaluation, and continuous collaboration among stakeholders.

Keywords: generative AI; large language models; AI chatbots; ChatGPT; artificial intelligence; retrieval-augmented generation; medicine; healthcare; human health; AI regulation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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