比较 ChatGPT、Gemini 和 Le Chat 与医生对在线健康论坛医学实验室问题的解释。

IF 3.8 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY
Annika Meyer, Ari Soleman, Janik Riese, Thomas Streichert
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引用次数: 0

摘要

目的:非医学专业人员通常无法直观地理解实验室医疗报告。因此,患者很可能会求助于基于人工智能的聊天机器人来了解他们的化验结果。然而,评估这些聊天机器人在回答现实生活中患者有关化验医学的询问时的功效的实证研究却很少:因此,本次调查包括来自在线健康论坛的 100 个患者咨询,特别是关于全血细胞计数解释的咨询。目的是评估三个基于人工智能的聊天机器人(ChatGPT、Gemini 和 Le Chat)与认证医生在线回复的熟练程度:结果:研究结果表明,聊天机器人对化验结果的解释不如在线医疗专业人员。虽然聊天机器人表现出更高程度的移情交流,但它们经常对复杂的患者问题做出错误或过于笼统的回答。聊天机器人回复的适当性从 51% 到 64% 不等,其中 22% 到 33% 的回复高估了患者的病情。一个值得注意的积极方面是聊天机器人始终包含非医疗性质的免责声明,并建议患者寻求专业医疗建议:聊天机器人对真实患者询问的化验结果的解释凸显了一种危险的二分法--感知到的可信度可能掩盖了事实的不准确性。鉴于人们越来越倾向于使用人工智能平台进行自我诊断,因此必须对这些聊天机器人进行进一步的研究和改进,以提高患者的认识,避免未来给医疗系统带来负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of ChatGPT, Gemini, and Le Chat with physician interpretations of medical laboratory questions from an online health forum.

Objectives: Laboratory medical reports are often not intuitively comprehensible to non-medical professionals. Given their recent advancements, easier accessibility and remarkable performance on medical licensing exams, patients are therefore likely to turn to artificial intelligence-based chatbots to understand their laboratory results. However, empirical studies assessing the efficacy of these chatbots in responding to real-life patient queries regarding laboratory medicine are scarce.

Methods: Thus, this investigation included 100 patient inquiries from an online health forum, specifically addressing Complete Blood Count interpretation. The aim was to evaluate the proficiency of three artificial intelligence-based chatbots (ChatGPT, Gemini and Le Chat) against the online responses from certified physicians.

Results: The findings revealed that the chatbots' interpretations of laboratory results were inferior to those from online medical professionals. While the chatbots exhibited a higher degree of empathetic communication, they frequently produced erroneous or overly generalized responses to complex patient questions. The appropriateness of chatbot responses ranged from 51 to 64 %, with 22 to 33 % of responses overestimating patient conditions. A notable positive aspect was the chatbots' consistent inclusion of disclaimers regarding its non-medical nature and recommendations to seek professional medical advice.

Conclusions: The chatbots' interpretations of laboratory results from real patient queries highlight a dangerous dichotomy - a perceived trustworthiness potentially obscuring factual inaccuracies. Given the growing inclination towards self-diagnosis using AI platforms, further research and improvement of these chatbots is imperative to increase patients' awareness and avoid future burdens on the healthcare system.

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来源期刊
Clinical chemistry and laboratory medicine
Clinical chemistry and laboratory medicine 医学-医学实验技术
CiteScore
11.30
自引率
16.20%
发文量
306
审稿时长
3 months
期刊介绍: Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically. CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France). Topics: - clinical biochemistry - clinical genomics and molecular biology - clinical haematology and coagulation - clinical immunology and autoimmunity - clinical microbiology - drug monitoring and analysis - evaluation of diagnostic biomarkers - disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes) - new reagents, instrumentation and technologies - new methodologies - reference materials and methods - reference values and decision limits - quality and safety in laboratory medicine - translational laboratory medicine - clinical metrology Follow @cclm_degruyter on Twitter!
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