人工智能可以改善患者对放射学报告的理解
Kanhai Amin1, Pavan Khosla2, Rushabh Doshi2
1Yale University, New Haven, CT, USA.
The Yale journal of biology and medicine
|October 2, 2023
概括
由于复杂的语言,患者很难理解医学成像报告. 人工智能,特别是大型语言模型 (LLM),显示出对简化这些报告的承诺,以改善患者的理解和结果.
科学领域:
- 医疗成像医学成像
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 诊断成像报告是为医疗专业人员撰写的,使用技术术语.
- "21世纪治疗法"允许患者直接访问这些报告.
- 目前的报告格式超过了患者理解水平的平均水平,阻碍了对疾病的理解.
研究的目的:
- 探索人工智能 (AI) 的潜力,以简化诊断成像报告以帮助患者理解.
- 为了解决为放射学报告提供以患者为中心的AI解决方案的缺口.
- 研究自然语言处理 (NLP) 和大语言模型 (LLM) 的应用,以提高患者对成像检测结果的理解.
主要方法:
- 审查现有的关于人工智能在放射学中的应用文献.
- 分析NLP和LLM在简化复杂医学文本方面的潜力.
- 考虑拟议的人工智能驱动解决方案的工作流的影响.
主要成果:
- 人工智能,特别是LLM,为简化成像报告提供了潜在的解决方案,而不会造成重大工作流程中断.
- 现有的放射学人工智能应用在很大程度上忽视了以患者为中心的简化.
- LLM提供了一种新的方法,以提高患者对诊断成像结果的理解.
结论:
- 使用人工智能简化成像报告可以改善患者的理解,并可能导致更好的健康结果.
- 在临床实施之前,LLM驱动的简化需要进一步的研究.
- 以患者为中心的AI解决方案对于改善医疗保健可访问性和患者参与度至关重要.
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