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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Peng Zhang & Jiayu Shi & Maged N. Kamel Boulos, 2024. "Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’," Future Internet, MDPI, vol. 16(12), pages 1-21, December.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:462-:d:1539519
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    References listed on IDEAS

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    1. Sebastian Farquhar & Jannik Kossen & Lorenz Kuhn & Yarin Gal, 2024. "Detecting hallucinations in large language models using semantic entropy," Nature, Nature, vol. 630(8017), pages 625-630, June.
    2. Peng Zhang & Maged N. Kamel Boulos, 2023. "Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges," Future Internet, MDPI, vol. 15(9), pages 1-15, August.
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