探索AI驱动的应用在妇科瘤学临床决策的潜力
Bastian Meyer1, Raphael Kfuri-Rubens2,3,4, Georg Schmidt1
1Department of Gynecology and Obstetrics, TUM University Hospital, Technical University Munich, Munich, Germany.
概括
像生成预训练变压器 (GPT)-4这样的人工智能 (AI) 显示出在瘤学中自动化治疗建议的潜力. 虽然GPT-4适度足够,但需要对临床决策支持的准确性和完整性进行改进.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 人工智能 (AI) 和大型语言模型 (LLM) 在医疗应用中提供了新的可能性.
- 生成式预训练变压器 (GPT) 模型显示出在医疗保健中推进自然语言处理的前景.
研究的目的:
- 调查GPT-4在自动化治疗建议中的潜力.
- 分析GPT-4在妇科恶性瘤和乳腺癌患者健康记录上的表现.
主要方法:
- 在GPT-4中,多学科瘤委员会 (MDTs) 对60例患者病例提出了治疗建议.
- 使用临床一致性得分,将GPT-4建议与MDT决策进行了比较.
- 这些建议由妇科瘤学家进行了定性和定量审查.
主要成果:
- GPT-4为所有病例提供了连贯的建议,这些建议被认为适度足以用于临床使用.
- 准确性和完整性被认为是需要改进的领域.
- 在老年癌症 (乳腺癌) 病例中,GPT-4的表现优于妇科病例,代提示提高了一致性高达84%.
结论:
- GPT-4可以处理复杂的病例并产生详细的建议,但手术和全身治疗方法存在差异.
- 观察到一种倾向,即建议过度进行遗传测试.
- 人工智能驱动的自动化治疗建议可能会支持未来的临床决策.
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