Evidence map›Paper›PMID 42449079›Full record

ArticleJournal of imaging informatics in medicine2026

Detecting Performance Drift in AI Models for Medical Image Analysis Using CUSUM Chart.

Smriti Prathapan, Ravi Samala, Nathan Hadjiyski, Nicholas Petrick, Ghada Zamzmi, Brandon Nelson, Jana G Delfino, Pierre-François D'Haese, Fabien Maldonado, Phuong Nguyen and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Smriti PrathapanOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA.
Ravi SamalaOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA. Ravi.Samala@fda.hhs.gov.ORCID http://orcid.org/0000-0002-6661-4801
Nathan HadjiyskiOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA.
Nicholas PetrickOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA.
Ghada ZamzmiOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA.
Brandon NelsonOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA.
Jana G DelfinoOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA.
Pierre-François D'HaeseRockefeller Neuroscience Institute, West Virginia University, 33 Medical Center Dr, Morgantown, 26506, WV, USA.
Fabien MaldonadoVanderbilt Lung Institute, Vanderbilt University Medical Center, 1211 Medical Center Dr, Nashville, 37232, TN, USA.
Phuong NguyenDepartment of Computer Science, University of Miami, 1320 S Dixie Hwy, Coral Gables, 33146, FL, USA.
Yelena YeshaDepartment of Radiology, University of Miami, 1320 S Dixie Hwy, Coral Gables, 33146, FL, USA.
Berkman SahinerOffice of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, 20993, MD, USA. Berkman.Sahiner@fda.hhs.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of artificial intelligence (AI) models to assist clinical decisions for diagnosis and prediction has been shown to improve patient outcomes and clinical decision-making. However, the performance of an AI model deployed in a clinical setting can vary over time or between initial evaluations and clinical use because of data drift. Monitoring the performance of a deployed AI model may help to (i) ensure the AI model performs as expected in the clinical environment and (ii) detect performance deviations and alert stakeholders to these deviations. In this study, we investigate how a change in the performance of an AI model caused by an abrupt data drift can be detected using a cumulative sum (CUSUM) control chart. We demonstrate the use of CUSUM for computer-aided breast cancer detection using data from the publicly available Emory Breast Imaging Dataset (EMBED). Our results indicate that, when the magnitude of the drift is 1.5 times the standard deviation of the performance metric, CUSUM is able to detect changes in the test negativity rate of an AI model within an average of 5 days following the onset of performance drift, with a long duration (

Indexed as

Clinical AI workflowCumulative sum (CUSUM)Data drift detectionStatistical process control

Identifiers

PMID42449079

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.