Abstract Number:
2160
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Henry Domenico (1), Ryan Moore (1), Daniel Byrne (2)
Institutions:
(1) Vanderbilt University Medical Center, N/A, (2) Daniel Byrne Research, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
There is a lot of hype surrounding AI, some of which is justified. There exists, however, a gap in evidence for AI's efficacy in improving healthcare outcomes. Much of this can be attributed to inadequate design of studies evaluating AI tools, often lacking rigorous outcome assessments. Effect estimates are often based on observational studies, which fail to adequately account for selection bias, leading to unreliable, or outright incorrect, results. For AI to achieve the goal of improving health for patients, the industry must adopt randomized trials, particularly pragmatic RCTs, to robustly test AI tools before implementation. Speed and rigor in research are not mutually exclusive and can responsibly accelerate AI's integration into clinical practice. Reforms in incentives must be made to prioritize rigorous AI research over proliferation of unvalidated models. Physicians must gain modern AI evaluation skills and lead these studies. In this poster, we present our progress in bridging this gap at a large academic medical center. In addition, we present several demonstration studies showing that large scale pragmatic RCTs of AI models can be done and do speed up progress toward imp
Keywords:
Artificial Intelligence in Healthcare|Pragmatic Randomized Trial|Machine Learning|Real-time predictive modeling| |
Sponsors:
ENAR
Tracks:
Miscellaneous
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