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- Title
Increasing acceptance of AI‐generated digital twins through clinical trial applications.
- Authors
Vidovszky, Anna A.; Fisher, Charles K.; Loukianov, Anton D.; Smith, Aaron M.; Tramel, Eric W.; Walsh, Jonathan R.; Ross, Jessica L.
- Abstract
Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real‐world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI‐generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI‐generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI‐generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI‐generated digital twins in clinical practice.
- Subjects
DIGITAL twins; CLINICAL trials; CLINICAL medicine; TECHNOLOGICAL innovations; INDIVIDUALIZED medicine
- Publication
CTS: Clinical & Translational Science, 2024, Vol 17, Issue 7, p1
- ISSN
1752-8054
- Publication type
Article
- DOI
10.1111/cts.13897