ArticleRadiology2020
Preparing Medical Imaging Data for Machine Learning.
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In one paragraph
Article in Radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 387 papers, 11 of them syntheses that pooled it.
0numbers the graph read from it
0cells of the map it votes in
387citing papers in PubMed, 11 pooled it
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What it found
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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.
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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.
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Who cites it
387 citing papers in PubMed, 11 syntheses or guidelines pooled it.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 2026Pooled it
- Diagnostic Accuracy of AI in Prediction and Assessment of Compromised Free Flaps: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- PRIME 2.0: Proposed Requirements for Cardiovascular Imaging-Related Multimodal-AI Evaluation: An Updated Checklist.JACC. Cardiovascular imaging · 2026Guideline
- Diagnostic performance of artificial intelligence in detecting bladder carcinoma.World journal of urology · 2025Pooled it
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- A systematic review of the hybrid machine learning models for brain tumour segmentation and detection in medical images.Frontiers in artificial intelligence · 2025Pooled it
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Deep learning in pulmonary nodule detection and segmentation: a systematic review.European radiology · 2025Pooled it
- Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review.AJNR. American journal of neuroradiology · 2024Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Getting Beyond Plain Text: Long-Term Adoption Rates and Survey of Radiologist Experience with Interactive Multimedia Reporting Technology.Journal of imaging informatics in medicine · 2026Article
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- AI In Leukemia Diagnostics: Complementing the Pathologist's Role.International journal of laboratory hematology · 2026Review
- Multicohort Evaluation of Acquisition Confounding in Single-Site Lung-CT Malignancy Classification with Frozen Foundation-Model Features.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence Reporting Guidelines in Dentomaxillofacial Radiology: Specialty-Oriented Practical Guide.Diagnostics (Basel, Switzerland) · 2026Review
- Clinical Specialty Expansion of AI-Enabled and Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration From 1995 to 2025: Longitudinal Content Analysis.Journal of medical Internet research · 2026Article
- Mapping concussion-induced cerebellar injury: a personalized approach using diffusion tensor imaging (DTI).Magma (New York, N.Y.) · 2026Article
- Artificial Intelligence for Diagnostic and Prognostic Support in Breast Cancer: A Literature Overview.Cancers · 2026Review
327 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Martin J WilleminkFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).ORCID 0000-0002-6991-6557
Wojciech A KoszekFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).ORCID 0000-0002-8949-8858
Cailin HardellFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).
Jie WuFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).ORCID 0000-0003-3564-9056
Dominik FleischmannFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).ORCID 0000-0003-0715-0952
Hugh HarveyFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).ORCID 0000-0003-4528-1312
Les R FolioFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).
Ronald M SummersFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).ORCID 0000-0001-8081-7376
Daniel L RubinFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).
Matthew P LungrenFrom the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, S-072, Stanford, CA 94305-5105 (M.J.W., D.F., D.L.R., M.P.L.); Segmed, Menlo Park, Calif (M.J.W., W.A.K., C.H., J.W.); School of Engineering, Stanford University, Stanford, Calif (J.W.); Institute of Cognitive Neuroscience, University College London, London, England (H.H.); Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (L.R.F.); Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, National Institutes of Health, Clinical Center, Bethesda, Md (R.M.S.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (D.L.R.); and Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford, Calif (M.P.L.).ORCID 0000-0002-8591-5861
Funding
Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0MSpectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1MQualification and Deployment of Imaging Biomarkers of Cancer Treatment ResponseU01CA190214 · NCI · STANFORD UNIVERSITY · PI RUBIN, DANIEL L · 2015 to 2020
$3.1MComputing, Optimizing, and Evaluating Quantitative Cancer Imaging BiomarkersU01CA187947 · NCI · STANFORD UNIVERSITY · PI NAPEL, SANDY A., RUBIN, DANIEL L · 2015 to 2019
$2.9MComputerized Quantitative Imaging Assessment of Tumor BurdenU01CA142555 · NCI · STANFORD UNIVERSITY · PI RUBIN, DANIEL L · 2010 to 2014
$2.9MDeep Learning for Pulmonary Embolism Imaging Decision Support: A Multi-institutional CollaborationR01LM012966 · NLM · STANFORD UNIVERSITY · PI SHAH, NIGAM H · 2018 to 2021
$1.4MDistributed Learning of Deep Learning Models for Cancer ResearchU01CA242879 · NCI · STANFORD UNIVERSITY · PI KALPATHY-CRAMER, JAYASHREE, RUBIN, DANIEL L · 2019 to 2021
$1.2MComputer Aided Detection for Radiologic ImagesZIACL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0kNCATS NIH HHS UL1 TR001085NCATS NIH HHS UL1 TR003142NCI NIH HHS U01 CA142555NCI NIH HHS U01 CA187947NCI NIH HHS U01 CA190214NCI NIH HHS U01 CA242879NLM NIH HHS R01 LM012966
Abstract
Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations). Current supervised AI methods require a curation process for data to optimally train, validate, and test algorithms. Currently, most research groups and industry have limited data access based on small sample sizes from small geographic areas. In addition, the preparation of data is a costly and time-intensive process, the results of which are algorithms with limited utility and poor generalization. In this article, the authors describe fundamental steps for preparing medical imaging data in AI algorithm development, explain current limitations to data curation, and explore new approaches to address the problem of data availability.
Indexed as
AlgorithmsData CollectionData ManagementDiagnostic ImagingMachine LearningHumans
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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 Socratic graph.