Cited by 368Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

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A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

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Langlotz, Curtis P., Allen, Bibb, Erickson, Bradley J., Kalpathy-Cramer, Jayashree, Bigelow, Keith, Cook, Tessa S., Flanders, Adam E., Lungren, Matthew P., Mendelson, David S., Rudie, Jeffrey D., Wang, Ge, Kandarpa, Krishna·2019·Radiology

DOI 10.1148/radiol.2019190613PMC PMC6542624

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Abstract

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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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: Langlotz, Curtis P.
Allen, Bibb
Erickson, Bradley J.
Kalpathy-Cramer, Jayashree
Cook, Tessa S.
Flanders, Adam E.
Lungren, Matthew P.
Mendelson, David S.
Rudie, Jeffrey D.
Wang, Ge
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2019-06
Volume: 291
Issue: 3
Pages: 781-791
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.2019190613
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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A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

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