导航不同寻常的:应用基于证据的医学在罕见疾病的挑战和人工智能解决方案的前景
1Institute for the History and Philosophy of Science and Technology, University of Toronto, 73 Queen's Park Cres. E, Toronto, ON, M5S 1K7, Canada. olivia.rennie@mail.utoronto.ca.
Medicine, health care, and philosophy
|May 9, 2024
概括
基于证据的医学 (EBM) 在罕见疾病研究中面临挑战. 人工智能 (AI) 提供了潜在的解决方案,但需要与患者价值观一起进行批判性评估.
科学领域:
- 医学研究 医学研究
- 罕见疾病 罕见疾病
- 人工智能的人工智能
背景情况:
- 罕见疾病为医学研究带来了独特的挑战,阻碍了与常见疾病相比了解病理生理学和治疗的进步.
- 基于证据的医学 (EBM) 方法对罕见疾病可能是不可行的,因为固有的约束限制了研究.
- 人工智能 (AI) 正在成为分析罕见疾病数据的有希望的工具.
研究的目的:
- 在罕见疾病的背景下批判性地研究EBM及其试验设计的局限性.
- 探索AI在罕见疾病研究中的当前应用.
- 提供有关AI在这个领域的潜在弱点和风险的哲学评论.
主要方法:
- 基于证据的医学原则的批判性分析及其对罕见疾病的应用.
- 审查目前用于罕见疾病研究的人工智能方法.
- 对人工智能在医学研究中的伦理和实际影响进行哲学考察.
主要成果:
- 由于EBM的结构硬,这给罕见病研究带来了重大挑战.
- 人工智能为克服罕见疾病数据局限性提出了一种新的方法.
- 罕见疾病研究中的AI算法具有固有的弱点和潜在危险,需要仔细考虑.
结论:
- 没有一种方法适合所有罕见疾病;平衡的观点至关重要.
- 对新的研究范式和人工智能的谨慎乐观是有必要的,但批判性评估是必不可少的.
- 患者的价值观和偏好必须纳入罕见病研究,即使使用人工智能驱动的模型.
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