Translating AI to Clinical Practice: Overcoming Data Shift with Explainability
Open accessAbstract
To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice. Most medical AI is trained with datasets gathered from limited environments, such as restricted disease populations and center-dependent acquisition conditions. The data shift that commonly exists in the limited training set often causes a significant performance decrease in the deployment environment. To develop a medical application, it is important to detect potential data shift and its impact on clinical translation. During AI training stages, from premodel analysis to in-model and post hoc explanations, explainability can play a key role in detecting model susceptibility to data shift, which is otherwise hidden because the test data have the same biased distribution as the training data. Performance-based model assessments cannot effectively distinguish the model overfitting to training data bias without enriched test sets from external environments. In the absence of such external data, explainability techniques can aid in translating AI to clinical practice as a tool to detect and mitigate potential failures due to data shift. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Artificial Intelligence and Ophthalmology
Gurnani, B., & Kaur, K. (2026) · Springer eBooks · doi:10.1007/978-3-032-11375-7_524
Implementing AI in Neuroradiology
Shanker, R. R. B. J., Shetty, A., & Ginat, D. T. (2026) · Springer eBooks · doi:10.1007/978-3-032-24568-7_2
Kowalska, A., Nowak, P., Zielinski, T., & Mazur, K. (2023) · Journal of Health Informatics and Digital Systems · doi:10.68159/u679026846
Hassan, H. S., Naji, S. A., & Jaber, A. G. (2026) · Al-Mustansiriyah Journal of Sciences · doi:10.23851/mjs.v37i3.1832
An Ensemble-Based Approach to Validating AI-Generated Clinical Discharge Summaries
Reichenpfader, D., & Denecke, K. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00201
From AI to AID: Algorithmic imaging detection (AID) as a more precise label for AI in radiology
Almasri, S., Eibschutz, L., & Gholamrezanezhad, A. (2026) · Clinical Imaging · doi:10.1016/j.clinimag.2026.110972
- Authors
- Choi, Y., Yu, W., Nagarajan, M. B., Teng, P., Goldin, J. G., Raman, S. S., Enzmann, D. R., Kim, G. H. J., & Brown, M. S.
- Affiliations
- University of California, Los Angeles (US)
- From the Center for Computer Vision and Imaging Biomarkers, 924 Westwood Blvd, Los Angeles, CA 90024 (Y.C., W.Y., M.B.N., P.T., J.G.G., G.H.J.K., M.S.B.); and Department of Radiology, University of California–Los Angeles, Los Angeles, Calif (Y.C., W.Y., M.B.N., P.T., J.G.G., S.S.R., D.R.E., G.H.J.K., M.S.B.).
- ORCID
- Venue
- RadioGraphics
- Published
- 2023-05-01
- Volume
- 43
- Issue
- 5
- Pages
- e220105-e220105
- ISSN
- 0271-5333 (ISSN-L)
- 1527-1323
- PMC
- PMC10190133
- OpenAlex
- W4367174392
- Cited by
- 65
- References
- 34
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 11 · 2023: 3
- FWCI
- 6.13
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiographics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- David Geffen School of Medicine, University of California, Los Angeles
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- distribution shift, explainable AI, clinical translation of AI, model generalization, artificial intelligence in healthcare, training data bias, external validation, post-hoc explanation, overfitting, artificial intelligence in radiology
- MeSH terms
- Algorithms, Artificial Intelligence, Humans
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