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A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
Open accessAbstract
Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.
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“The problem is exacerbated by issues related to interobserver and intraobserver variability of CT image interpretations rendered by radiologists, which can be as high as 37%.206”
From Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review (2021) · cites this paper as [206]
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“This was largely due to advances in deep learning, the branch of AI that learns from large amounts of data.”
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“In 2015, the ImageNet annual competition reached a milestone when the error rate of automatic classification of images dropped below 5%, which is the average human error rate (Figure S1) 17 .”
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Metadata
- Authors
- Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., Wang, G., & Kandarpa, K.
- Affiliations
- Mayo Clinic (US)
- National Institutes of Health (US)
- ORCID
- Venue
- Radiology
- Published
- 2019-06
- Volume
- 291
- Issue
- 3
- Pages
- 781-791
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC6542624
- OpenAlex
- W2939071985
- Cited by
- 368
- References
- 85
- Citations by year
- 2026: 34 · 2025: 33 · 2024: 45 · 2023: 57 · 2022: 56 · 2021: 68 · 2020: 58 · 2019: 17
- FWCI
- 15.77
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- artificial intelligence in medical imaging, research priorities, image reconstruction, image segmentation, computer-aided detection, computer-aided classification, radiogenomics, automatic image annotation, electronic phenotyping, federated learning, pre-trained models, explainable AI
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Diagnostic Imaging, Humans, Image Interpretation, Computer-Assisted, Biomedical Research
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