AI Chatbots in Clinical Laboratory Medicine: Capabilities, Limitations, and Path Forward
This Clinical Chemistry review examines the emerging role of AI chatbots, particularly large language models like ChatGPT, in laboratory medicine. The authors evaluate chatbot performance across medical knowledge, laboratory operations, regulatory questions, and clinical result interpretation, finding significant limitations including misinformation, inconsistencies, and lack of reasoning ability. They outline a development roadmap requiring rigorous training on validated medical knowledge and thorough evaluation against standard clinical practice before laboratory adoption.
The original study
AI Chatbots in Clinical Laboratory Medicine: Foundations and Trends.
- Authors
- Yang HS, Wang F, Greenblatt MB, Huang SX, Zhang Y
- Journal
- Clinical chemistry
- Type
- Review, Journal Article, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., Research Support, N.I.H., Extramural
- PMID
- 37664912
Original abstract
BACKGROUND: Artificial intelligence (AI) conversational agents, or chatbots, are computer programs designed to simulate human conversations using natural language processing. They offer diverse functions and applications across an expanding range of healthcare domains. However, their roles in laboratory medicine remain unclear, as their accuracy, repeatability, and ability to interpret complex laboratory data have yet to be rigorously evaluated. CONTENT: This review provides an overview of the history of chatbots, two major chatbot development approaches, and their respective advantages and limitations. We discuss the capabilities and potential applications of chatbots in healthcare, focusing on the laboratory medicine field. Recent evaluations of chatbot performance are presented, with a special emphasis on large language models such as the Chat Generative Pre-trained Transformer in response to laboratory medicine questions across different categories, such as medical knowledge, laboratory operations, regulations, and interpretation of laboratory results as related to clinical context. We analyze the causes of chatbots' limitations and suggest research directions for developing more accurate, reliable, and manageable chatbots for applications in laboratory medicine. SUMMARY: Chatbots, which are rapidly evolving AI applications, hold tremendous potential to improve medical education, provide timely responses to clinical inquiries concerning laboratory tests, assist in interpreting laboratory results, and facilitate communication among patients, physicians, and laboratorians. Nevertheless, users should be vigilant of existing chatbots' limitations, such as misinformation, inconsistencies, and lack of human-like reasoning abilities. To be effectively used in laboratory medicine, chatbots must undergo extensive training on rigorously validated medical knowledge and be thoroughly evaluated against standard clinical practice.