Shah Krish, Xu Andrew Y, Sharma Yatharth, Daher Mohammed, McDonald Christopher, Diebo Bassel G, Daniels Alan H
Warren Alpert Medical School, Brown University, East Providence, RI 02914, USA.
Department of Orthopedics, Warren Alpert Medical School, Brown University, Providence, RI 02912, USA.
J Clin Med. 2024 Aug 28;13(17):5101. doi: 10.3390/jcm13175101.
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.
大语言模型(LLMs)有潜力通过改善医疗服务可及性、诊断、手术规划和教育来彻底改变临床医学。然而,其应用需要精心、及时的设计,以减轻诸如幻觉和偏差等挑战。正确使用大语言模型涉及理解诸如词元化、嵌入和注意力机制等基础概念,以及确保准确输出的策略性提示技术。对于创新的医疗保健解决方案而言,人工智能技术与医学专业人员之间保持持续合作至关重要。包括数据安全和偏差缓解在内的伦理考量对其应用至关重要。通过将大语言模型用作研究和教育中的辅助资源,我们可以加强学习并支持基于知识的探究,最终提高医疗服务的质量和可及性。要充分实现大语言模型在变革医疗保健方面的潜力,持续的研发是必要的。