摘要
由一名新闻记者兼机器人与机器学习每日新闻编辑每日新闻-关于人工智能ce的详细数据已经呈现。根据NewsRx编辑来自加拿大多伦多的新闻报道，这项研究指出：“罕见疾病的研究长期以来一直是医学研究人员面临的挑战领域，与更常见的疾病相比，在改善对病理生理学和治疗的理解方面进展缓慢得令人痛苦。向循证医学(EBM)的推动，它将某些类型的证据置于其他类型的证据之上。”当映射到罕见疾病时，会带来一个特别的问题，由于一些限制，使用EBM认可的方法可能无法对这些疾病进行可行的调查。新闻记者引用了托尔大学的一句话：“虽然已经提出了其他的试验设计来克服这些局限性（成功程度各不相同），但最近被广泛采用的也许是人工智能在罕见疾病数据中的应用。本文对EBM（及其试验设计分支）在涉及罕见疾病时的缺陷进行了全面的考察。”探索人工智能作为应对这些挑战的潜在途径的当前格局。这次讨论也进一步深入，对应用于罕见疾病研究的人工智能算法的弱点和危险提供了哲学上的评论。虽然没有提出单一的解决方案，但这篇文章确实提供了一个深思熟虑的提醒，在罕见疾病的复杂世界中，没有‘一刀切’的方法存在。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligen ce have been presented. According to news reporting originating in Toronto, Cana da, by NewsRx editors, the research stated, “The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow moveme nt towards improved understanding of pathophysiology and treatments compared wit h more common illnesses. The push towards evidence-based medicine (EBM), which p rioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the meth odologies endorsed by EBM, due to a number of constraints.” The news reporters obtained a quote from the research from the University of Tor onto, “While other trial designs have been suggested to overcome these limitatio ns (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper c ritically examines the pitfalls of EBM (and its trial design offshoots) as it pe rtains to rare diseases, exploring the current landscape of AI as a potential so lution to these challenges. This discussion is also taken a step further, provid ing philosophical commentary on the weaknesses and dangers of AI algorithms appl ied to rare disease research. While not proposing a singular solution, this arti cle does provide a thoughtful reminder that no ‘one-size-fits-all’ approach exis ts in the complex world of rare diseases.”
NETL
NSTL