Abstract：
BACKGROUND: The optimal management of distal radius fractures remains a challenge for orthopaedic surgeons. The emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), especially ChatGPT, affords significant potential in improving healthcare and research. This study aims to assess the accuracy and consistency of ChatGPT\'s knowledge in managing distal radius fractures, with a focus on its capability to provide information for patients and assist in the decision-making processes of orthopaedic clinicians.
METHODS: We presented ChatGPT with seven questions on distal radius fracture management over two sessions, resulting in 14 responses. These questions covered a range of topics, including patient inquiries and orthopaedic clinical decision-making. We requested references for each response and involved two orthopaedic registrars and two senior orthopaedic surgeons to evaluate response accuracy and consistency.
RESULTS: All 14 responses contained a mix of both correct and incorrect information. Among the 47 cited references, 13% were accurate, 28% appeared to be fabricated, 57% were incorrect, and 2% were correct but deemed inappropriate. Consistency was observed in 71% of the responses.
CONCLUSIONS: ChatGPT demonstrates significant limitations in accuracy and consistency when providing information on distal radius fractures. In its current format, it offers limited utility for patient education and clinical decision-making.
METHODS: We presented ChatGPT with seven questions on distal radius fracture management over two sessions, resulting in 14 responses. These questions covered a range of topics, including patient inquiries and orthopaedic clinical decision-making. We requested references for each response and involved two orthopaedic registrars and two senior orthopaedic surgeons to evaluate response accuracy and consistency.
RESULTS: All 14 responses contained a mix of both correct and incorrect information. Among the 47 cited references, 13% were accurate, 28% appeared to be fabricated, 57% were incorrect, and 2% were correct but deemed inappropriate. Consistency was observed in 71% of the responses.
CONCLUSIONS: ChatGPT demonstrates significant limitations in accuracy and consistency when providing information on distal radius fractures. In its current format, it offers limited utility for patient education and clinical decision-making.
摘要：
背景：桡骨远端骨折的最佳治疗仍然是骨科医师面临的挑战。人工智能(AI)和大型语言模型(LLM)的出现，尤其是ChatGPT,在改善医疗保健和研究方面提供了巨大的潜力。本研究旨在评估ChatGPT知识在治疗桡骨远端骨折方面的准确性和一致性。专注于其为患者提供信息并协助骨科临床医生决策过程的能力。
方法：我们为ChatGPT提供了七个关于桡骨远端骨折治疗的问题，得到14个答复。这些问题涵盖了一系列主题，包括患者咨询和骨科临床决策。我们要求每个响应的参考，并涉及两名骨科注册师和两名高级骨科外科医生，以评估响应的准确性和一致性。
结果：所有14个回答都包含了正确和不正确的信息。在引用的47篇参考文献中，13%是准确的，28%似乎是捏造的，57%的人不正确。2%是正确的，但被认为是不合适的。在71%的响应中观察到一致性。
结论：ChatGPT在提供桡骨远端骨折信息时，在准确性和一致性方面存在显著限制。以目前的格式,它为患者教育和临床决策提供了有限的效用。
方法：我们为ChatGPT提供了七个关于桡骨远端骨折治疗的问题，得到14个答复。这些问题涵盖了一系列主题，包括患者咨询和骨科临床决策。我们要求每个响应的参考，并涉及两名骨科注册师和两名高级骨科外科医生，以评估响应的准确性和一致性。
结果：所有14个回答都包含了正确和不正确的信息。在引用的47篇参考文献中，13%是准确的，28%似乎是捏造的，57%的人不正确。2%是正确的，但被认为是不合适的。在71%的响应中观察到一致性。
结论：ChatGPT在提供桡骨远端骨折信息时，在准确性和一致性方面存在显著限制。以目前的格式,它为患者教育和临床决策提供了有限的效用。
