研究动态
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生成语言系统在提高患者对结肠癌筛查意识中的作用。

The role of generative language systems in increasing patient awareness of colon cancer screening.

发表日期:2024 Aug 14
作者: Marcello Maida, Daryl Ramai, Yuichi Mori, Mário Dinis-Ribeiro, Antonio Facciorusso, Cesare Hassan
来源: ENDOSCOPY

摘要:

本研究旨在评估 ChatGPT(Chat Generative Pretrained Transformer)在回答患者有关结直肠癌 (CRC) 筛查问题的有效性,最终目标是提高患者对国家筛查计划的认识和依从性。共 15 个关于 CRC 筛查的问题向 ChatGPT4 展示。答案由 20 名胃肠病学专家和 20 名非专家在三个领域(准确性、完整性和可理解性)以及 100 名患者在三个二分领域(完整性、可理解性和可信性)进行评分。根据专家评分,平均准确度得分评分范围为 1 至 6 的评分为 4.8±1.1。男性完整性评分为 2.1±0.7,评分范围为 1 至 3 的平均可理解性评分为 2.8±0.4。总体而言,准确性(4.8±1.1 与 5.6±0.7,P) <0.001)和完整性(2.1±0.7 vs 2.7±0.4,P<0.001)分数与非专家相比,专家显着较低,而两组之间的可理解性相当(2.7±0.4 vs 2.8±0.3,P=0.546) )。在 97% 至 100% 的病例中,患者将所有问题评为完整、可理解和可信。ChatGPT 显示出良好的性能,有可能提高对 CRC 的认识并改善筛查结果。根据科学证据和当前指南进行适当的训练后,生成语言系统可能会得到进一步改进。Thieme。版权所有。
This study aims to evaluate the effectiveness of ChatGPT (Chat Generative Pretrained Transformer) in answering patients' questions about colorectal cancer (CRC) screening, with the ultimate goal of enhancing patients' awareness and adherence to national screening programs.15 questions on CRC screening were posed to ChatGPT4. The answers were rated by 20 gastroenterology experts and 20 non-experts in three domains (accuracy, completeness, and comprehensibility), and by 100 patients in three dichotomic domains (completeness, comprehensibility and trustability).According to expert rating, the mean accuracy score was 4.8±1.1 on a scale ranging from 1 to 6. Men completeness score was 2.1±0.7 and mean comprehensibility score was 2.8±0.4 on a scale ranging from 1 to 3. Overall, accuracy (4.8±1.1 vs 5.6±0.7, P<0.001) and completeness (2.1±0.7 vs 2.7±0.4, P<0.001) scores were significantly lower for expert compared to non-expert, while comprehensibility was comparable among the two groups (2.7±0.4 vs 2.8±0.3, P=0.546). Patients rated all questions as complete, comprehensible and trustable in 97 to 100% of cases.ChatGPT shows good performance with the potential to enhance awareness about CRC and improve screening outcomes. Generative language systems may be further improved after proper training in accordance with scientific evidence and current guidelines.Thieme. All rights reserved.