Wang Zhixiang, Zhang Zhen, Traverso Alberto, Dekker Andre, Qian Linxue, Sun Pengfei
Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Department of Radiation Oncology (Maastro), GROW-School for Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
Quant Imaging Med Surg. 2024 Feb 1;14(2):1602-1615. doi: 10.21037/qims-23-1180. Epub 2024 Jan 11.
As artificial intelligence (AI) becomes increasingly prevalent in the medical field, the effectiveness of AI-generated medical reports in disease diagnosis remains to be evaluated. ChatGPT is a large language model developed by open AI with a notable capacity for text abstraction and comprehension. This study aimed to explore the capabilities, limitations, and potential of Generative Pre-trained Transformer (GPT)-4 in analyzing thyroid cancer ultrasound reports, providing diagnoses, and recommending treatment plans.
Using 109 diverse thyroid cancer cases, we evaluated GPT-4's performance by comparing its generated reports to those from doctors with various levels of experience. We also conducted a Turing Test and a consistency analysis. To enhance the interpretability of the model, we applied the Chain of Thought (CoT) method to deconstruct the decision-making chain of the GPT model.
GPT-4 demonstrated proficiency in report structuring, professional terminology, and clarity of expression, but showed limitations in diagnostic accuracy. In addition, our consistency analysis highlighted certain discrepancies in the AI's performance. The CoT method effectively enhanced the interpretability of the AI's decision-making process.
GPT-4 exhibits potential as a supplementary tool in healthcare, especially for generating thyroid gland diagnostic reports. Our proposed online platform, "ThyroAIGuide", alongside the CoT method, underscores the potential of AI to augment diagnostic processes, elevate healthcare accessibility, and advance patient education. However, the journey towards fully integrating AI into healthcare is ongoing, requiring continuous research, development, and careful monitoring by medical professionals to ensure patient safety and quality of care.
随着人工智能（AI）在医学领域日益普及，人工智能生成的医学报告在疾病诊断中的有效性仍有待评估。ChatGPT是OpenAI开发的一种大型语言模型，具有显著的文本抽象和理解能力。本研究旨在探讨生成式预训练变换器（GPT）-4在分析甲状腺癌超声报告、提供诊断和推荐治疗方案方面的能力、局限性及潜力。
我们使用109例不同的甲状腺癌病例，通过将GPT-4生成的报告与不同经验水平医生生成的报告进行比较，评估了GPT-4的性能。我们还进行了图灵测试和一致性分析。为提高模型的可解释性，我们应用了思维链（CoT）方法来解构GPT模型的决策链。
GPT-4在报告结构、专业术语和表达清晰度方面表现出色，但在诊断准确性方面存在局限性。此外，我们的一致性分析突出了人工智能性能方面的某些差异。CoT方法有效地提高了人工智能决策过程的可解释性。
GPT-4在医疗保健领域展现出作为辅助工具的潜力，特别是在生成甲状腺诊断报告方面。我们提出的在线平台“ThyroAIGuide”与CoT方法一起，凸显了人工智能增强诊断过程、提高医疗可及性和推进患者教育的潜力。然而，将人工智能全面整合到医疗保健中的进程仍在继续，需要医学专业人员持续进行研究、开发并仔细监测，以确保患者安全和医疗质量。
