Healthcare AI has not been widely evaluated for clinical benefits, leaving a dearth of information on whether the technology actually improves patient outcomes, a Nature Medicine correspondence argues.
Much of the existing literature focuses on algorithmic performance in retrospective or highly controlled datasets, where AI often shows strong accuracy, wrote authors Jenna Wiens, PhD, of Ann Arbor-based University of Michigan and Anna Goldenberg, PhD, of University of Toronto in the April 21 article. But technical success doesn’t guarantee clinical adoption or effectiveness. Plus, funding incentives for research may favor novelty and performance metrics over rigorous evaluation of clinical impact.
To address these gaps, the authors call for more randomized controlled trials and prospective studies, evaluation frameworks focused on patient-centered outcomes, ongoing monitoring after deployment, and stronger reporting and regulatory standards.
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