Previously submitted to: JMIR Cancer (no longer under consideration since Mar 26, 2026)
Date Submitted: Jan 28, 2026
Open Peer Review Period: Feb 3, 2026 - Mar 31, 2026
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Bridging Diagnostic Accuracy and Patient Outcomes in Medical Imaging AI: A Structured Narrative Review (2018–2025)
ABSTRACT
Background:
Artificial intelligence (AI) has reached expert-level performance across many areas of medical imaging, yet this progress has not translated proportionally into improvements in patient outcomes. While deep learning models excel at pixel-level pattern recognition, their impact on clinical decision-making, workflow efficiency, and patient-centered care remains poorly characterized
Objective:
This structured narrative review synthesizes evidence from high-quality studies (2018–2025) to evaluate whether imaging AI systems meaningfully improve patient outcomes beyond diagnostic accuracy. The review critically examines clinical integration, workflow implications, ethical considerations, and the persistent gap between algorithmic performance and patient-centered benefit.
Methods:
A structured search of PubMed, Scopus, IEEE Xplore, and Web of Science (2018–October 2025) identified empirical studies applying AI to human medical imaging and reporting both diagnostic metrics and real-world clinical, workflow, or patient-centered outcomes. Studies were screened independently by two reviewers, and data were extracted using predefined categories : model type, dataset characteristics, validation strategy, performance metrics, workflow impact, patient outcomes, and ethical considerations.
Results:
Ten high-quality studies met the inclusion criteria. Across domains (ophthalmology, mammography, echocardiography, CT, PET/CT, and chest radiography), AI models achieved strong diagnostic performance (pooled mean AUC = 0.91 ± 0.03). However, only 30% of studies reported measurable patient impact and 20% reported workflow improvements. External validation often revealed 5–10% performance degradation, and only four systems were deployed in routine care. Ethical analyses showed emerging concerns regarding bias, explainability, and trustworthiness particularly related to racial inference from imaging data.
Conclusions:
Medical imaging AI has matured algorithmically but remains clinically immature. Achieving true patient-centered benefit requires shifting from model-centric development to systems-level innovation: multimodal integration, explainable AI, human-in-the-loop designs, equity-aware training, and prospective clinical evaluation. AI will advance from “seeing the organ” to “understanding the patient” only when technical performance aligns with clinical workflows, ethical oversight, and human experience.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.