利用人工智能对儿科进展性和耐火性固体瘤进行决策
Oz Mordechai1, Inbar Meir1, Einav Blanche1
1Joan and Sanford Weill Pediatric Hematology Oncology and Bone Marrow Transplantation Division, Ruth Rappaport Children's Hospital, Rambam Medical Center, Haifa, Israel.
Pediatric hematology and oncology
|January 26, 2026
概括
人工智能 (AI) 工具在指导儿科瘤治疗决策中显示出潜在的潜力,用于耐火固体瘤. 这些大型语言模型 (LLM) 经常建议进行临床试验,但由于各种建议,人类监督至关重要.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 患有耐火固体瘤的儿科患者的预后不佳,尽管有先进的治疗方法.
- 复杂的病例需要多学科的方法,包括精准医学和临床试验.
- 人工智能 (AI),特别是大型语言模型 (LLM),可能会增强儿科瘤学中的临床推理.
研究的目的:
- 评估五种人工智能工具 (ChatGPT,Gemini,Claude,Perplexity,OpenEvidence) 的决策能力. 这些工具包括:
- 评估AI能够为假设的儿科耐火固体瘤病例产生和证明治疗选择的能力.
主要方法:
- 对五种人工智能工具提出了6种耐火儿科固体瘤的假设病例.
- 每个AI工具都收到了两个查询:生成治疗选项和选择/证明最佳选项.
- 分析了人工智能生成的建议的频率和类型.
主要成果:
- 人工智能工具产生了124个治疗建议,平均每种工具24.8.
- 临床试验招生是最常见的"最佳选择" (55.2%),其次是有针对性的治疗 (17.2%).
- 人工智能工具表现出不同的决策倾向,有些倾向于积极治疗,另一些则倾向于支持性护理.
结论:
- 人工智能工具在协助复杂的儿科瘤治疗决策方面表现有前途,特别是在确定临床试验方面.
- 人工智能建议的变化凸显了人类监督的必要性,以确保与临床证据和患者目标保持一致.
- 未来的研究应该专注于改进人工智能以提供个性化护理,并解决人工智能辅助决策的心理社会影响.
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