On the Application of Artificial Intelligence/Machine Learning (AI/ML) in Late-Stage Clinical Development.

IF 2 4区 医学 Q4 MEDICAL INFORMATICS
Karl Köchert, Tim Friede, Michael Kunz, Herbert Pang, Yijie Zhou, Elena Rantou
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引用次数: 0

Abstract

Whereas AI/ML methods were considered experimental tools in clinical development for some time, nowadays they are widely available. However, stakeholders in the health care industry still need to answer the question which role these methods can realistically play and what standards should be adhered to. Clinical research in late-stage clinical development has particular requirements in terms of robustness, transparency and traceability. These standards should also be adhered to when applying AI/ML methods. Currently there is some formal regulatory guidance available, but this is more directed at settings where a device or medical software is investigated. Here we focus on the application of AI/ML methods in late-stage clinical drug development, i.e. in a setting where currently less guidance is available. This is done via first summarizing available regulatory guidance and work done by regulatory statisticians followed by the presentation of an industry application where the influence of extensive sets of baseline characteristics on the treatment effect can be investigated by applying ML-methods in a standardized manner with intuitive graphical displays leveraging explainable AI methods. The paper aims at stimulating discussions on the role such analyses can play in general rather than advocating for a particular AI/ML-method or indication where such methods could be meaningful.

Abstract Image

论人工智能/机器学习(AI/ML)在后期临床开发中的应用。
人工智能/ML 方法在一段时间内被认为是临床开发中的实验工具,而如今它们已被广泛使用。然而,医疗保健行业的利益相关者仍需要回答这些方法究竟能发挥什么作用,以及应遵守哪些标准。后期临床开发阶段的临床研究在稳健性、透明度和可追溯性方面有特殊要求。在应用人工智能/ML 方法时也应遵守这些标准。目前有一些正式的监管指南,但更多是针对设备或医疗软件的研究环境。在此,我们将重点关注人工智能/ML 方法在临床药物开发后期的应用,即在目前指导较少的情况下的应用。为此,我们首先总结了现有的监管指南和监管统计人员所做的工作,然后介绍了一个行业应用案例,在该案例中,可以通过标准化的方式应用 ML 方法,并利用可解释的人工智能方法进行直观的图形显示,从而研究大量基线特征集对治疗效果的影响。本文旨在激发对此类分析所能发挥的一般作用的讨论,而不是提倡使用某种特定的人工智能/ML 方法或此类方法可能有意义的适应症。
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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来源期刊
Therapeutic innovation & regulatory science
Therapeutic innovation & regulatory science MEDICAL INFORMATICS-PHARMACOLOGY & PHARMACY
CiteScore
3.40
自引率
13.30%
发文量
127
期刊介绍: Therapeutic Innovation & Regulatory Science (TIRS) is the official scientific journal of DIA that strives to advance medical product discovery, development, regulation, and use through the publication of peer-reviewed original and review articles, commentaries, and letters to the editor across the spectrum of converting biomedical science into practical solutions to advance human health. The focus areas of the journal are as follows: Biostatistics Clinical Trials Product Development and Innovation Global Perspectives Policy Regulatory Science Product Safety Special Populations
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