Chain of Thought Utilization in Large Language Models and Application in Nephrology
Issued Date
2024-01-01
Resource Type
ISSN
1010660X
eISSN
16489144
Scopus ID
2-s2.0-85183098644
Pubmed ID
38256408
Journal Title
Medicina (Lithuania)
Volume
60
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Medicina (Lithuania) Vol.60 No.1 (2024)
Suggested Citation
Miao J., Thongprayoon C., Suppadungsuk S., Krisanapan P., Radhakrishnan Y., Cheungpasitporn W. Chain of Thought Utilization in Large Language Models and Application in Nephrology. Medicina (Lithuania) Vol.60 No.1 (2024). doi:10.3390/medicina60010148 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/95943
Title
Chain of Thought Utilization in Large Language Models and Application in Nephrology
Corresponding Author(s)
Other Contributor(s)
Abstract
Chain-of-thought prompting enhances the abilities of large language models (LLMs) significantly. It not only makes these models more specific and context-aware but also impacts the wider field of artificial intelligence (AI). This approach broadens the usability of AI, increases its efficiency, and aligns it more closely with human thinking and decision-making processes. As we improve this method, it is set to become a key element in the future of AI, adding more purpose, precision, and ethical consideration to these technologies. In medicine, the chain-of-thought prompting is especially beneficial. Its capacity to handle complex information, its logical and sequential reasoning, and its suitability for ethically and context-sensitive situations make it an invaluable tool for healthcare professionals. Its role in enhancing medical care and research is expected to grow as we further develop and use this technique. Chain-of-thought prompting bridges the gap between AI’s traditionally obscure decision-making process and the clear, accountable standards required in healthcare. It does this by emulating a reasoning style familiar to medical professionals, fitting well into their existing practices and ethical codes. While solving AI transparency is a complex challenge, the chain-of-thought approach is a significant step toward making AI more comprehensible and trustworthy in medicine. This review focuses on understanding the workings of LLMs, particularly how chain-of-thought prompting can be adapted for nephrology’s unique requirements. It also aims to thoroughly examine the ethical aspects, clarity, and future possibilities, offering an in-depth view of the exciting convergence of these areas.