Department of Radiology, University of Calgary, Calgary, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
Clin Neuroradiol. 2024 Jun;34(2):293-305. doi: 10.1007/s00062-024-01382-7. Epub 2024 Jan 29.
Artificial intelligence (AI) has emerged as a transformative force in medical research and is garnering increased attention in the public consciousness. This represents a critical time period in which medical researchers, healthcare providers, insurers, regulatory agencies, and patients are all developing and shaping their beliefs and policies regarding the use of AI in the healthcare sector. The successful deployment of AI will require support from all these groups. This commentary proposes that widespread support for medical AI must be driven by clear and transparent scientific reporting, beginning at the earliest stages of scientific research.
A review of relevant guidelines and literature describing how scientific reporting plays a central role at key stages in the life cycle of an AI software product was conducted. To contextualize this principle within a specific medical domain, we discuss the current state of predictive tissue outcome modeling in acute ischemic stroke and the unique challenges presented therein.
Translating AI methods from the research to the clinical domain is complicated by challenges related to model design and validation studies, medical product regulations, and healthcare providers' reservations regarding AI's efficacy and affordability. However, each of these limitations is also an opportunity for high-impact research that will help to accelerate the clinical adoption of state-of-the-art medical AI. In all cases, establishing and adhering to appropriate reporting standards is an important responsibility that is shared by all of the parties involved in the life cycle of a prospective AI software product.
人工智能（AI）已成为医学研究中的一股变革力量，在公众意识中引起了越来越多的关注。这是一个关键时期，医学研究人员、医疗保健提供者、保险公司、监管机构和患者都在制定和塑造他们对医疗保健领域人工智能使用的信念和政策。AI 的成功部署需要所有这些群体的支持。本评论建议，必须通过从科学研究的最早阶段开始进行清晰透明的科学报告来推动对医疗 AI 的广泛支持。
对描述科学报告如何在 AI 软件产品生命周期的关键阶段发挥核心作用的相关准则和文献进行了回顾。为了将这一原则置于特定的医学领域背景下，我们讨论了当前急性缺血性中风预测组织预后建模的现状以及其中存在的独特挑战。
将 AI 方法从研究领域转化到临床领域是复杂的，这涉及到模型设计和验证研究、医疗产品法规以及医疗保健提供者对 AI 的疗效和可负担性的保留意见等挑战。然而，这些限制中的每一个都是进行高影响力研究的机会，这将有助于加速最先进的医疗 AI 的临床应用。在所有情况下，建立和遵守适当的报告标准都是一个重要的责任，涉及到潜在 AI 软件产品生命周期的所有相关方都应共同承担。
