Evidence map›Paper›PMID 36971405›Full record

ReviewThe British journal of radiology2023

Data drift in medical machine learning: implications and potential remedies.

Berkman Sahiner, Weijie Chen, Ravi K Samala, Nicholas Petrick

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 111 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
111citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

111 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. A life-cycle approach to digital health technology assessment.International journal of technology assessment in health care · 2026
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51 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Berkman SahinerCenter for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002.
Weijie ChenCenter for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002.
Ravi K SamalaCenter for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002.
Nicholas PetrickCenter for Devices and Radiological Health, U.S. Food and Drug Administration 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Data drift refers to differences between the data used in training a machine learning (ML) model and that applied to the model in real-world operation. Medical ML systems can be exposed to various forms of data drift, including differences between the data sampled for training and used in clinical operation, differences between medical practices or context of use between training and clinical use, and time-related changes in patient populations, disease patterns, and data acquisition, to name a few. In this article, we first review the terminology used in ML literature related to data drift, define distinct types of drift, and discuss in detail potential causes within the context of medical applications with an emphasis on medical imaging. We then review the recent literature regarding the effects of data drift on medical ML systems, which overwhelmingly show that data drift can be a major cause for performance deterioration. We then discuss methods for monitoring data drift and mitigating its effects with an emphasis on pre- and post-deployment techniques. Some of the potential methods for drift detection and issues around model retraining when drift is detected are included. Based on our review, we find that data drift is a major concern in medical ML deployment and that more research is needed so that ML models can identify drift early, incorporate effective mitigation strategies and resist performance decay.

Indexed as

Machine LearningMedical Informatics Computing

Identifiers

PMID36971405
PMCPMC10546450

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.