Version 1
: Received: 2 September 2024 / Approved: 3 September 2024 / Online: 4 September 2024 (11:04:57 CEST)
How to cite:
Zhang, P.; Shi, J.; Kamel Boulos, M. N. Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’. Preprints2024, 2024090311. https://doi.org/10.20944/preprints202409.0311.v1
Zhang, P.; Shi, J.; Kamel Boulos, M. N. Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’. Preprints 2024, 2024090311. https://doi.org/10.20944/preprints202409.0311.v1
Zhang, P.; Shi, J.; Kamel Boulos, M. N. Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’. Preprints2024, 2024090311. https://doi.org/10.20944/preprints202409.0311.v1
APA Style
Zhang, P., Shi, J., & Kamel Boulos, M. N. (2024). Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’. Preprints. https://doi.org/10.20944/preprints202409.0311.v1
Chicago/Turabian Style
Zhang, P., Jiayu Shi and Maged N. Kamel Boulos. 2024 "Generative AI in Medicine and Healthcare: Moving Beyond the ‘Peak of Inflated Expectations’" Preprints. https://doi.org/10.20944/preprints202409.0311.v1
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
Subject
Medicine and Pharmacology, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
