神经学中的人工智能:评估大型语言模型的诊断和治疗推理
Skylar Bentley1, Rees P Ridout2, Nadiya A Persaud1
1Research, Orlando College of Osteopathic Medicine, Winter Garden, USA.
Cureus
|January 7, 2026
概括
大型语言模型 (LLM) 在诊断诸如吉兰-巴雷综合征 (GBS) 这样的神经疾病方面表现有前途. 然而,这些人工智能工具需要进一步开发,以便在专家医生监督下提供个性化,长期的患者护理.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 人工智能 (AI) 和大型语言模型 (LLM) 越来越多地用于医学.
- 它们在临床决策中的作用,特别是在神经病学等复杂领域,尚未得到充分理解.
- 神经学诊断存在独特的挑战,因为重叠的综合征和时间敏感的决策.
研究的目的:
- 评估三个领先的法学士的诊断和治疗推理能力.
- 在涉及吉兰-巴雷综合征 (GBS) 和脊髓外周脂质瘤 (SEL) 的复杂神经病例中评估AI性能.
主要方法:
- 三位LLM (ChatGPT 4.0,Google Gemini,Claude 3.5 Sonnet) 被一个相同的复杂神经病例提示.
- 结果由四名失明的神经科医生评估,他们使用标准化标签对诊断准确性,差异诊断,干预计划和监测进行评估.
- 绩效是基于描述性评审员评估进行评估的.
主要成果:
- 所有的LLM都正确识别了GBS,并建议了与指南一致的治疗方法 (IVIG,等离子酶).
- 克劳德 3.5 索内特获得了最高的整体分数 (18.5/20),其次是聊天GPT (17.5) 和谷歌双子座 (17.25).
- 具体优势各不相同:双子座在初级诊断方面领先,克劳德在差异诊断和干预方面表现出色,ChatGPT在监测方面最强.
结论:
- 在支持神经学决策方面,LLM具有显著的潜力,提供准确的诊断和基于证据的治疗建议.
- 目前的LLM在为特定患者的疾病 (如SEL) 提供个性化护理以及制定全面的长期管理策略方面存在局限性.
- 在复杂的神经病例中,谨慎地将LLM与医生专业知识相结合,对于最佳的患者结果至关重要.
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