Collection 

Prospective and Interventional Clinical Evidence in Medical AI Research

Submission status
Open
Submission deadline

Artificial intelligence (AI) is rapidly moving from retrospective benchmarking studies into clinical workflows. However, much of the literature still focuses on model accuracy as the primary endpoint. This Collection focuses on evaluating clinical AI beyond accuracy by examining the pathway from model output to clinical action and ultimately to patient and health-system outcomes. In particular, a model paired with different actions can actually produce different results. However, the importance of downstream action exploration is still not widely recognized. We particularly welcome studies built around AI–action bundles, the combination of an AI result and the predefined action expected from clinicians, patients, or care teams. Studies focused only on model development, benchmark performance, external validation, fairness, or implementation experience without a defined downstream action are outside the core scope of this Collection. Across all submissions, the causal pathway from model output to action to outcome should be explicit.

This collection seeks research that evaluates the full pathway from model output to clinical action and health outcomes in the following areas:

  • Prospective pragmatic trials, cluster-randomized trials, and stepped-wedge designs that evaluate AI-enabled screening, diagnosis, prognosis, triage, monitoring, and treatment.
  • Research on integrating generative AI into the clinical workflow, including referral pathways, and the design of human-AI interaction
  • High-quality retrospective causal analyses and target trial emulations that justify specific downstream actions.
  • Robust governance and surveillance strategies that monitor real-world effectiveness, model drift, safety, retraining, and recalibration of AI-action bundles.
  • Health technology assessments, cost-effectiveness, and economic evaluations of AI-action bundles.
  • Reporting, governance, and lifecycle frameworks that support clinically meaningful evaluation beyond accuracy alone.
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Photographic footage of a surgeon interacting with an AI holographic interface

Editors

The Collection will publish original research Articles, Reviews, Perspectives and Comments (full details on content types can be found here). Papers will be published in npj digital medicine as soon as they are accepted and then collected together and promoted on the Collection homepage. All Guest Edited Collections are associated with a call for papers and are managed by one or more of our Editorial Board Members and the journal's Editors.

This Collection welcomes submissions from all authors – and not by invitation only – on the condition that the manuscripts fall within the scope of the Collection and of npj digital medicine more generally. See our editorial process page for more details. 

All submissions are subject to the same peer review process and editorial standards as regular npj digital medicine articles, including the journal’s policy on competing interests. The Guest Editors have no competing interests with the submissions, which they handle through the peer-review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests. See our Collections guidelines for more details. 

This Collection is not supported by sponsorship.