在临床病理学实践中评估生成人工智能的使用:机遇和前进的道路
Peter McCaffrey1, Ronald Jackups2, Jansen Seheult3
1From the Departments of Pathology (McCaffrey, Thaker) and Radiology (McCaffrey), University of Texas Medical Branch, Galveston.
Archives of pathology & laboratory medicine
|October 9, 2024
概括
生成型人工智能 (GAI) 对临床病理学 (CP) 工作流程,包括诊断和研究,具有重大潜力. 然而,仔细实施至关重要,以减轻诸如偏见之类的风险,并确保最终用户接受改善患者结果.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床病理学应用 临床病理学应用
背景情况:
- 生成型人工智能 (GAI) 准备改变医疗保健,特别是临床病理 (CP) 工作流程.
- 潜在的应用范围包括CP内的教育,数据挖掘,决策支持和患者趋势分析.
研究的目的:
- 审查GAI在临床病理学中的当前和潜在使用案例.
- 专注于大型语言模型 (LLM) 和它们在CP子专业领域的应用.
- 识别和解决与GAI在CP中的实施相关的潜在陷.
主要方法:
- 对医疗保健中GAI现有文献的广泛审查.
- 在临床化学,微生物学,血液病理学和分子诊断中分析GAI用例场景.
- 评估GAI的CP数据使用情况,考虑报告,质量指标和翻译研究.
主要成果:
- GAI为彻底改变患者护理和CP实践者效率提供了机会.
- 针对各种CP子专业,确定了特定的GAI应用.
- 潜在的挑战包括数据偏差,人工智能幻觉,工作流集成和用户采用.
结论:
- GAI是医疗保健的强大工具,但由于固有的局限性,需要谨慎实施.
- 人在循环模型被推用于将GAI集成到CP中.
- GAI可以更深入地了解个体和人口患者的结果.
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