ArticleNPJ digital medicine2025
The impact of leadership on AI deployment study outcomes in healthcare: an integrative analysis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Reassessing the evidence linking clinical leadership to AI deployment outcomes.NPJ digital medicine · 2026Article
- The bottleneck was never data or algorithms: building a learning utility for AI-enabled learning health systems.npj health systems · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.