Artificial intelligence in clinical practice: Quality and evidence.

R Puchades, L Ramos-Ruperto
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Abstract

A revolution is taking place within the field of artificial intelligence (AI) with the emergence of generative AI. Although we are in an early phase at the clinical level, there is an exponential increase in the number of scientific articles that use AI (discriminative and generative) in their methodology. According to the current situation, we may be in an "AI bubble" stage; requiring filters and tools to evaluate its application, based on the quality and evidence provided. In this sense, initiatives have been developed to determine standards and guidelines for the use of discriminative AI (CONSORT AI, STARD AI and others), and more recently for generative AI (the CHART collaborative). As a new technology, AI requires scientific regulation to guarantee the efficacy and safety of its applications, while maintaining the quality of care; an evidence-based AI (IABE).

临床实践中的人工智能:质量与证据。
随着生成式人工智能的出现,人工智能(AI)领域正在发生一场革命。尽管在临床层面我们还处于早期阶段,但在方法论中使用人工智能(辨别式和生成式)的科学文章数量却呈指数级增长。根据目前的情况,我们可能正处于 "人工智能泡沫 "阶段;需要过滤器和工具来根据所提供的质量和证据评估其应用。从这个意义上讲,我们已经制定了一些举措,以确定使用判别式人工智能的标准和指南(CONSORT 人工智能、STARD 人工智能等),以及最近的生成式人工智能标准和指南(CHART 协作)。作为一项新技术,人工智能需要科学监管,以保证其应用的有效性和安全性,同时保持医疗质量;这就是循证人工智能(IABE)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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