ReviewBMJ digital health & AI2026
Identifying and understanding significant change due to drift when assessing AI models in healthcare: a narrative review.
Review in BMJ digital health & AI, 2026. 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.
- Drift isn't a bug, it's the work: post-deployment surveillance for clinical artificial intelligence.BMJ digital health & AI · 2026Article
- Déjà vu in healthcare AI: lessons from the world's pioneer AI clinical decision support system.BMJ digital health & AI · 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) as a Medical Device (AIaMD) or medical devices that use AI algorithms-like any other medical device-must meet the requirements of medical device regulation. For regulatory purposes, the most relevant requirement is that the developer must provide evidence that the device performs as intended under normal conditions of use for its entire lifecycle. However, healthcare data are not static and underlying characteristics can change for many reasons (eg, the introduction of new technologies which improve measurement accuracy, changes in population demographics, etc). This 'drift' may lead to a change in performance overall or in certain subgroups in AI models. Models can be updated with new data if significant drift is identified, but in the context of AIaMD, this needs to be done transparently and within a robust regulatory framework. This paper reports on the consensus view of an expert working group hosted by the UK Medicines and Healthcare products Regulatory Agency (MHRA). It aims to highlight the challenges with identifying and assessing significant changes in the performance of a model and understanding the nature of a detected drift to preserve patient safety. We discuss distinct drift subtypes from a statistical perspective and highlight potential causes in the real world that could lead to significant changes to the performance of AI algorithms. We also outline the regulatory challenges associated with risk assessment and the characteristics of drift that are crucial to examine (such as speed and severity) to correctly address interventions and ensure the deployment of safe healthcare products on the market. Finally, we discuss a range of considerations to best identify, risk-assess and intervene for drift when assessing healthcare AI products.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.