医疗保健领域的未来AI：虚假警报的海啸还是专家的产品？
The Future AI in Healthcare: A Tsunami of False Alarms or a Product of Experts?
摘要：
最近，经济实惠且可访问的计算能力和数据存储的显着增加使机器学习能够提供与训练有素的人类相比几乎令人难以置信的分类和预测性能。在复杂的医疗保健领域，特别是成像领域，已经取得了一些有希望的（但有限的）成果。这一前景使一些人得出结论，认为我们将通过将“人工智能”应用于“大（医疗）数据”，解决人类健康和医学方面越来越多的问题。科学文献已经被算法淹没，超出了我们有效审查它们的能力。不幸的是，我认为大多数，如果不是所有这些出版物或商业算法都犯了几个基本错误。我认为，因为每个人（因此每个算法）都有盲点，所以有多个“最佳”算法，每个算法都擅长不同类型的患者或不同的情况。因此，我们应该一起投票许多算法，根据它们的整体性能，它们之间的独立性以及定义上下文的一组特征（即，当一种算法优于另一种算法时，最大限度地区分这些情况的特征）。这种方法不仅提供了性能更好的分类器或预测器，而且提供了置信区间，使得临床医生可以判断如何对警报做出响应。此外，我认为，足够数量的（大多数）独立的算法，解决同一问题，可以通过一个大型的国际竞争/挑战，持续数月，并定义成功的事件的条件。最后，我建议引入要求主要受赠者在资助的最后一年进行挑战，以最大限度地提高研究价值，并选择新一代受赠者。
英文摘要：
Recent significant increases in affordable and accessible computational power and data storage have enabled machine learning to provide almost unbelievable classification and prediction performances compared to well-trained humans. There have been some promising (but limited) results in the complex healthcare landscape, particularly in imaging. This promise has led some individuals to leap to the conclusion that we will solve an ever-increasing number of problems in human health and medicine by applying `artificial intelligence' to `big (medical) data'. The scientific literature has been inundated with algorithms, outstripping our ability to review them effectively. Unfortunately, I argue that most, if not all of these publications or commercial algorithms make several fundamental errors. I argue that because everyone (and therefore every algorithm) has blind spots, there are multiple `best' algorithms, each of which excels on different types of patients or in different contexts. Consequently, we should vote many algorithms together, weighted by their overall performance, their independence from each other, and a set of features that define the context (i.e., the features that maximally discriminate between the situations when one algorithm outperforms another). This approach not only provides a better performing classifier or predictor but provides confidence intervals so that a clinician can judge how to respond to an alert. Moreover, I argue that a sufficient number of (mostly) independent algorithms that address the same problem can be generated through a large international competition/challenge, lasting many months and define the conditions for a successful event. Finally, I propose introducing the requirement for major grantees to run challenges in the final year of funding to maximize the value of research and select a new generation of grantees.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
