The impact of leadership on AI deployment study outcomes in healthcare: an integrative analysis

Abstract

Studies on AI deployment in healthcare demand interdisciplinary collaboration, making the team structure and leadership essential for guiding AI-driven innovation. Drawing on Upper Echelons Theory, a theory associating organizational outcomes with leadership expertise, we investigated how studies on AI outcomes in healthcare reflect the team structure and leadership. Study data were obtained from 105 studies globally in two literature reviews including 96 randomized clinical trials (RCT). We hypothesized that clinician-led AI deployment studies are more likely to have a significant impact, assuming that last authorship represents leadership. Our analysis using logistic regression controlled for AI- and workflow-related confounders, including AI types and origin, clinical settings, and region. We found that leadership background was significantly associated with AI impact, with clinical leadership having a higher likelihood of impact (OR = 7.793, p = 0.039). The finding maintained when analyzed within RCT only, revealing associations among leadership background, study design, and region.

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