Large Language Model Prompting Techniques for Advancement in Clinical Medicine.

J Clin Med

Department of Orthopedics, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA.

Published: August 2024

AI Article Synopsis

  • Large Language Models (LLMs) can transform clinical medicine by improving access to healthcare, diagnosis, surgical planning, and education.
  • Effective use of LLMs requires understanding technical concepts and strategies to minimize errors, biases, and inaccuracies in medical contexts.
  • Ongoing collaboration between AI developers and medical professionals, along with attention to ethical issues like data security, is vital for maximizing the benefits of LLMs in healthcare innovation.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11396764PMC
http://dx.doi.org/10.3390/jcm13175101DOI Listing

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