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

By Curtis P. Langlotz, B. Allen, Bradley James Erickson, Jayashree Kalpathy-Cramer, K. Bigelow, T. Cook, A. Flanders, Matthew P. Lungren, David S. Mendelson, J. Rudie and 2 more. 2019

330 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, background): “Many more works have thus been dedicated to exploring explainability in the medical fields [10] [14] [34], providing summaries of previous works [15] including subfield specific review [20] (for chest radiograph), or at least set aside a section to promote awareness for the importance of interpretab”
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, background): “lack of large datasets with trustworthy labels is considered to be one of the biggest challenges facing a wider adoption and successful deployment of deep learning methods in medical applications [26], [1], [27].”
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “thoracic (lung cancer); abdominal and pelvic (computed tomography (CT) and magnetic resonance imaging (MRI)); colonoscopy (colonic polyps); mammography (microcalcifications); brain (brain tumors); radiation oncology (segmenting tumors for radiation, and quantifying specific radiographic characterist”
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models (2020, background): “…(GA) using 3D optical coherence tomography (OCT) image volumes, characterized by small sample size ( 275 subjects) and highly heterogeneous (collected from 4 different facilities); and ( iii ) LIDC-IDRI [34], a public medical dataset of 1 , 085 lung lesion CT images annotated by 4 radiologists.”
Preparing Medical Imaging Data for Machine Learning (2020, methodology): “Artificial intelligence (AI), as a field defined broadly by the engineering of computerized systems able to perform tasks that normally require human intelligence, has substantial potential in the medical imaging field (1).”