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Large Language Model Prompting Techniques for Advancement in Clinical Medicine.

Authors :
Shah, Krish
Xu, Andrew Y.
Sharma, Yatharth
Daher, Mohammed
McDonald, Christopher
Diebo, Bassel G.
Daniels, Alan H.
Source :
Journal of Clinical Medicine. Sep2024, Vol. 13 Issue 17, p5101. 12p.
Publication Year :
2024

Abstract

Large Language Models (LLMs have the potential to revolutionize clinical medicine by enhancing healthcare access, diagnosis, surgical planning, and education. However, their utilization requires careful, prompt engineering to mitigate challenges like hallucinations and biases. Proper utilization of LLMs involves understanding foundational concepts such as tokenization, embeddings, and attention mechanisms, alongside strategic prompting techniques to ensure accurate outputs. For innovative healthcare solutions, it is essential to maintain ongoing collaboration between AI technology and medical professionals. Ethical considerations, including data security and bias mitigation, are critical to their application. By leveraging LLMs as supplementary resources in research and education, we can enhance learning and support knowledge-based inquiries, ultimately advancing the quality and accessibility of medical care. Continued research and development are necessary to fully realize the potential of LLMs in transforming healthcare. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20770383
Volume :
13
Issue :
17
Database :
Academic Search Index
Journal :
Journal of Clinical Medicine
Publication Type :
Academic Journal
Accession number :
179646151
Full Text :
https://doi.org/10.3390/jcm13175101