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评估与遗传条件中的衰老相关的大型语言模型性能

Amna A Othman¹, Kendall A Flaharty², Suzanna E Ledgister Hanchard²

¹Medical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. amna.othman@nih.gov.

|May 3, 2025

概括

像GPT-3.5和Llama-2-70b-chat这样的大型语言模型 (LLM) 可以为遗传疾病生成适合年龄的医疗信息. 然而,他们的临床管理计划需要进一步改进,以提高准确性和完整性.

科学领域:

医学遗传学医学遗传学
人工智能在医学中的应用
临床信息学临床信息学

背景情况:

在儿科患者群体中,遗传病常常被描述为遗传病,从而造成成人患者护理方面的知识差距.
大型语言模型 (LLM) 在各种应用中显示出希望,促使对其医疗实用性的调查.

研究的目的:

评估Llama-2-70b-chat (70b) 和GPT-3.5 (GPT) 在儿童和成人假设患者遗传疾病中产生适合年龄的医疗细节,对话和管理计划的能力.
评估282个遗传条件的LLM生成内容的正确性和完整性.

主要方法:

他们要求LLM (70b和GPT) 在282种遗传疾病中为假设的儿科和成人患者创建医学简报,患者遗传学家对话和管理计划.
临床医生对生成的内容进行了正确性和完整性的分级,重点是年龄相适应性和临床合理性.
对代谢状况进行了分分析,包括新生儿出现的代谢状况.

主要成果:

根据临床医生评分,LLM在为儿童和成人患者生成医疗简报和对话时表现出适合年龄的反应.
副分析证实了适合年龄的LLM对代谢条件的反应,即使是那些具有新生儿危机表现的代谢条件.
70b和GPT模型在生成可信的临床管理计划方面都取得了较低的正确性和完整性得分,取决于模型和特定指标,从50-90%不等.

结论:

LLM显示出产生特定年龄的遗传疾病医疗信息的潜力,有助于弥合儿科和成人知识差距.
目前的LLM在制造临床准确和完整的管理计划方面存在局限性,强调了未来开发和验证医疗保健应用的领域.

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