使用人工智能标记自由文本操作和超声波报告,用于分类儿科尾炎
Waseem Abu-Ashour1, Sherif Emil1, Dan Poenaru1
1Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada; McGill University Health Centre Research Institute, Montreal, Quebec, Canada.
与手动方法相比,人工智能 (AI) 聊天机器人显著提高了用于儿科尾炎研究的电子病历数据提取精度. 人工智能工具也更准确地评估尾炎的严重程度,提高研究效率.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 儿科手术 儿科手术
背景情况:
- 儿童尾炎管理的数据科学面临着由于小数据集和非结构化的电子医疗记录 (EMR) 的挑战.
- 大型语言模型 (LLM) 为构建自由文本EMR数据提供了潜力.
- 准确的数据提取对于开发人工智能模型来个性化儿科护理至关重要.
研究的目的:
- 将ChatGPT-4的数据提取质量与儿童尾炎的人类数据收集器进行比较.
- 从操作报告中评估AI在分级尾炎严重性的准确性.
主要方法:
- 人类数据收集者从2100例儿科尾炎病例中提取了手术前和手术数据.
- 聊天GPT-4被提示从103个匿名超声波和操作记录中构建自由文本.
- 一位儿科外科医生判断所有提取的数据以识别错误.
主要成果:
- 手动数据提取在不一致的报告中有98%的错误.
- 在28.2%的病例中,尾炎的严重程度被手动错误分类,而在ChatGPT-4中,这一比例为2.9%.
- 人工智能聊天机器人的使用在59.2%的记录中避免了错误分类,并且速度快40倍.
结论:
- 人工智能聊天机器人在儿童尾炎研究数据的准确性和效率上明显优于手动数据提取.
- 人工智能工具在改善研究数据收集的准确性和速度方面表现有前途.
- 为了广泛采用人工智能,需要进一步验证和解决数据安全问题.
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