ArticleJMIR medical informatics2021
Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Accuracy of breast density assessment using artificial intelligence by convolutional neural network for carriers of UGT1A1 polymorphisms with Gilbert's Syndrome - a pilot study.Clinics (Sao Paulo, Brazil) · 2026Article
- Integrating Artificial Intelligence into Prostate MR Imaging: Technical Foundations, Clinical Applications, and Workflow Implications.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Survival Prediction for Postoperative Patients With Kidney Cancer Based on Computed Tomography Radiomics: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Review
- Understanding Providers' Attitude Toward AI in India's Informal Health Care Sector: Survey Study.JMIR formative research · 2025Article
- Predictive models for post-ERCP pancreatitis: a systematic review and meta-analysis.Frontiers in gastroenterology (Lausanne, Switzerland) · 2025Review
- From advanced imaging to molecular insights: the state of the art in omics for axial spondyloarthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Diving into Sweat: Advances, Challenges, and Future Directions in Wearable Sweat Sensing.ACS nano · 2024Review
- Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact.PLOS digital health · 2024Article
- Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review.Interactive journal of medical research · 2023Article
- RoMIA: a framework for creating Robust Medical Imaging AI models for chest radiographs.Frontiers in radiology · 2023Article
- Levels of Autonomous Radiology.Interactive journal of medical research · 2022Article
- Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease.Psychoradiology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of machine learning to develop intelligent software tools for the interpretation of radiology images has gained widespread attention in recent years. The development, deployment, and eventual adoption of these models in clinical practice, however, remains fraught with challenges. In this paper, we propose a list of key considerations that machine learning researchers must recognize and address to make their models accurate, robust, and usable in practice. We discuss insufficient training data, decentralized data sets, high cost of annotations, ambiguous ground truth, imbalance in class representation, asymmetric misclassification costs, relevant performance metrics, generalization of models to unseen data sets, model decay, adversarial attacks, explainability, fairness and bias, and clinical validation. We describe each consideration and identify the techniques used to address it. Although these techniques have been discussed in prior research, by freshly examining them in the context of medical imaging and compiling them in the form of a laundry list, we hope to make them more accessible to researchers, software developers, radiologists, and other stakeholders.
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Registered trials
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.