Beyond high hopes - A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging

Aug 4, 2026

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Machine learning drives the digital health revolution while raising strong expectations alongside widespread hype. A scoping review examines machine learning in medical imaging across 2019–2021, mapping the field’s potential, limitations, and future directions. Reported strengths emphasize analytic power, efficiency, improved decision making, and equity, while challenges focus on structural barriers, imaging heterogeneity, limited interoperable datasets, validity and performance limits including bias, and missing clinical integration. Ethical and regulatory implications remain blurred, and explainability and trustworthiness receive limited technical and regulatory detail. Future work is expected to move toward multi-source, more open, explainable models combining imaging with other data streams.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages20

Reading Time50 min

AccessFree to download

Abstract

Promises and strengths

Challenges and limitations

Ethical and regulatory implications

Future directions

Zurich Open Repository and Archive University of Zurich University Library Strickhofstrasse 39 CH-8057 Zurich [www.zora.uzh.ch](www.zora.uzh.ch) Year: 2023 Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging Nittas, Vasileios ; Daniore, Paola ; Landers, Constantin ; Gille, Felix ; Amann, Julia ; Hubbs, Shannon ; Puhan, Milo Alan ; Vayena, Effy ; Blasimme, Alessandro DOI: [https://doi.org/10.1371/journal.pdig.0000189](https://doi.org/10.1371/journal.pdig.0000189) Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: [https://doi.org/10.5167/uzh-254056](https://doi.org/10.5167/uzh-254056) Journal Article Published Version The following work is licensed under a Creative Commons: Attribution 4.0 International (CC BY 4.0) License. Originally published at: Nittas, Vasileios; Daniore, Paola; Landers, Constantin; Gille, Felix; Amann, Julia; Hubbs, Shannon; Puhan, Milo Alan; Vayena, Effy; Blasimme, Alessandro (2023) . Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS Digital Health, 2(1):e0000189 . DOI: [https://doi.org/10.1371/journal.pdig.0000189](https://doi.org/10.1371/journal.pdig.0000189) PLOS DIGITAL HEALTH OPEN ACCESS Citation: Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, et al. (2023) Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging. PLOS Digit Health 2(1): e0000189 . [https://doi.org/10.1371/journal.pdig.0000189](https://doi.org/10.1371/journal.pdig.0000189) [Editor:](Editor: Thomas Schmidt)[ Thomas Schmidt](Editor: Thomas Schmidt), [University of Southern](University of Southern)[ ](University of Southern)Denmark, DENMARK Received: April 6, 2022 Accepted: January 2, 2023 Published: January 31, 2023 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal.pdig.0000189](https://doi.org/10.1371/journal.pdig.0000189) [Copyright:](Copyright:) © [2023 Nittas et al. This is an open](2023 Nittas et al. This is an open)[ ](2023 Nittas et al. This is an open)access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. RESEARCH ARTICLE Beyond high hopes: A scoping review of the 2019–2021 scientific discourse on machine learning in medical imaging Vasileios Nittas1,2, Paola Daniore3,4, Constantin Landers1, Felix Gille3,4, Julia Amann1 , Shannon Hubbs1, Milo Alan Puhan2, Effy Vayena1, Alessandro Blasimme1 * 1 Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland, 2 Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland, 3 Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland, 4 Digital Society Initiative, University of Zurich, Switzerland 􀀁 [alessandro.blasimme@hest.ethz.ch](alessandro.blasimme@hest.ethz.ch) Abstract Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field’s potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power,(b) efficienc

FAQs

What strengths are most commonly reported for machine learning in medical imaging?

The review highlights improved analytic power, efficiency, decision making, and equity as frequently reported strengths and promises.

What major challenges and limitations does the review identify?

It points to structural barriers and imaging heterogeneity, scarcity of well-annotated representative datasets, validity and performance limitations including bias and equity issues, and the absence of clinical integration.

How does the review characterize the relationship between strengths and challenges?

The boundaries between strengths and challenges, including cross-cutting ethical and regulatory implications, remain blurred, with limited discussion of technical and regulatory challenges around explainability and trustworthiness.

Aug 4, 2026

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