Assessing Artificial Intelligence Solution Effectiveness: The Role of Pragmatic Trials
Mayo Clinic Proceedings: Digital Health, ISSN: 2949-7612, Vol: 2, Issue: 4, Page: 499-510
2024
- 14Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures14
- Readers14
- 14
Review Description
The emergence of artificial intelligence (AI) and other digital solutions in health care has considerably altered the landscape of medical research and patient care. Rigorous evaluation in routine practice settings is fundamental to the ethical use of AI and consists of 3 stages of evaluations: technical performance, usability and acceptability, and health impact evaluation. Pragmatic trials often play a key role in the health impact evaluation. The current review introduces the concept of pragmatic trials, their role in AI evaluation, the challenges of conducting pragmatic trials, and strategies to mitigate the challenges. We also examined common designs used in pragmatic trials and highlighted examples of published or ongoing AI trials. As more health systems advance into learning health systems, where outcomes are continuously evaluated to refine processes and tools, pragmatic trials embedded into everyday practice, leveraging data and infrastructure from delivering health care, will be a critical part of the feedback cycle for learning and improvement.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2949761224000737; http://dx.doi.org/10.1016/j.mcpdig.2024.06.010; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85206131230&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/40206523; https://linkinghub.elsevier.com/retrieve/pii/S2949761224000737
Elsevier BV
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