A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging

From the 2018 NIH/RSNA/ACR/The Academy Workshop

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

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)781-791
Number of pages11
JournalRADIOLOGY
Volume291
Issue number3
DOIs
StatePublished - Jun 1 2019
Externally publishedYes

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Artificial Intelligence
Diagnostic Imaging
Education
Research
Computer-Assisted Image Processing
Information Storage and Retrieval
Expert Systems
Information Dissemination
Triage
National Institutes of Health (U.S.)
Noise
Industry
Machine Learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., ... Kandarpa, K. (2019). A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. RADIOLOGY, 291(3), 781-791. https://doi.org/10.1148/radiol.2019190613

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

In: RADIOLOGY, Vol. 291, No. 3, 01.06.2019, p. 781-791.

Research output: Contribution to journalArticle

Langlotz, CP, Allen, B, Erickson, BJ, Kalpathy-Cramer, J, Bigelow, K, Cook, TS, Flanders, AE, Lungren, MP, Mendelson, DS, Rudie, JD, Wang, G & Kandarpa, K 2019, 'A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop', RADIOLOGY, vol. 291, no. 3, pp. 781-791. https://doi.org/10.1148/radiol.2019190613
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. / A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging : From the 2018 NIH/RSNA/ACR/The Academy Workshop. In: RADIOLOGY. 2019 ; Vol. 291, No. 3. pp. 781-791.
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