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从床边到机器人边:人工智能在紧急尾炎管理中的应用

Koray Ersahin1, Sebastian Sanduleanu2, Sithin Thulasi Seetha3

  • 1Department of General and Visceral Surgery, GFO Clinics Troisdorf, Academic Hospital of the Friedrich-Wilhelms-University Bonn, 50937 Troisdorf, Germany.

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概括

像GPT-4.5和DeepSeek R1这样的人工智能模型在协助尾炎的手术决策方面表现有前途,实现与机器学习模型相比较的高准确性. 需要进一步验证以优化它们在临床环境中的性能.

关键词:
这是尾切除术.大型语言模型.进行手术的决策.

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科学领域:

  • 医疗人工智能 医疗人工智能
  • 手术决策支持系统 手术决策支持系统
  • 胃肠道手术 胃肠道手术

背景情况:

  • 急性尾炎 (AA) 是一种常见的手术紧急情况,有可能导致严重的并发症.
  • 准确的诊断和及时的治疗对于管理AA和预防发病/死亡至关重要.
  • 目前的决策依赖于临床,实验室和放射学数据,手术干预通常是主要方法.

研究的目的:

  • 为了比较GPT-4.5,DeepSeek R1和机器学习模型的诊断和治疗建议准确度,与急性尾炎的董事会认证外科医生进行比较.
  • 评估结合临床指南对AI模型性能的影响.
  • 评估AI在协助下腹痛患者进行手术决策方面的潜力.

主要方法:

  • 一项多中心的回顾性研究包括63例确诊的尾炎病例和50例对照.
  • 临床,实验室和放射学数据被用于训练和测试人工智能模型 (GPT-4.5,DeepSeek R1,随机森林ML).
  • 人工智能对尾切除术和保守的抗生素治疗的建议与经过董事会认证的外科医生的参考标准进行了比较.

主要成果:

  • DeepSeek R1 显示了指导方针集成后的准确度从 80.5% 提高到 83.2%,但不显著.
  • 随着指导方针的整合,GPT-4.5的准确性从70.8%增加到76.1%.
  • 机器学习模型实现了84.3%的训练准确率和85.0%的验证准确率.

结论:

  • GPT-4.5,DeepSeek R1和机器学习模型显示出作为尾炎手术决策的辅助器的潜力.
  • 这些人工智能工具在资源有限的环境中可能特别有益.
  • 持续的研究和验证是必要的,以完善AI模型的性能,以临床应用.