Assessing the clinical reasoning of ChatGPT for mechanical thrombectomy in patients with stroke

医学 冲程(发动机) 医学物理学 物理医学与康复 物理疗法 人工智能 计算机科学 机械工程 工程类
作者
Tse Chiang Chen,Mitchell W. Couldwell,J.P. Singer,Alyssa M. Singer,Laila Koduri,Emily Kaminski,Khoa D. Nguyen,Evan Multala,Aaron S. Dumont,Arthur Wang
出处
期刊:Journal of NeuroInterventional Surgery [BMJ]
卷期号:16 (3): 253-260 被引量:4
标识
DOI:10.1136/jnis-2023-021163
摘要

Background Artificial intelligence (AI) has become a promising tool in medicine. ChatGPT, a large language model AI Chatbot, shows promise in supporting clinical practice. We assess the potential of ChatGPT as a clinical reasoning tool for mechanical thrombectomy in patients with stroke. Methods An internal validation of the abilities of ChatGPT was first performed using artificially created patient scenarios before assessment of real patient scenarios from the medical center’s stroke database. All patients with large vessel occlusions who underwent mechanical thrombectomy at Tulane Medical Center between January 1, 2022 and December 31, 2022 were included in the study. The performance of ChatGPT in evaluating which patients should undergo mechanical thrombectomy was compared with the decisions made by board-certified stroke neurologists and neurointerventionalists. The interpretation skills, clinical reasoning, and accuracy of ChatGPT were analyzed. Results 102 patients with large vessel occlusions underwent mechanical thrombectomy. ChatGPT agreed with the physician’s decision whether or not to pursue thrombectomy in 54.3% of the cases. ChatGPT had mistakes in 8.8% of the cases, consisting of mathematics, logic, and misinterpretation errors. In the internal validation phase, ChatGPT was able to provide nuanced clinical reasoning and was able to perform multi-step thinking, although with an increased rate of making mistakes. Conclusion ChatGPT shows promise in clinical reasoning, including the ability to factor a patient’s underlying comorbidities when considering mechanical thrombectomy. However, ChatGPT is prone to errors as well and should not be relied on as a sole decision-making tool in its present form, but it has potential to assist clinicians with more efficient work flow.
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