Trustworthiness of Artificial Intelligence Models in Radiology and the Role of Explainability
Journal of the American College of Radiology - Volume 18, Number 8 - https://doi.org/10.1016/j.jacr.2021.02.008
Summary
Trust is the cornerstone of relationships in the medical profession. To provide the best course of action for each patient, medical professionals must rely on a web of trust that involves numerous players, from pharmaceutical companies to laboratory technicians to breakthrough results published in leading scientific journals, to name but a few.
Keywords:
Cornerstone | Trustworthiness | Action (physics) | Medical profession | Computer science | Medical education | Psychology | Knowledge management | Medicine | Internet privacy
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15. Original article: Health services research and policy ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee Tessler et al.
16. Original Article: Leadership ACR Lung-RADS v2022: Assessment Categories and Management Recommendations Christensen et al.
17. Original article: Clinical practice management Management of Incidental Adrenal Masses: A White Paper of the ACR Incidental Findings Committee Mayo-Smith et al.
18. Latest Articles ACR Appropriateness Criteria® ACR Appropriateness Criteria® Myelopathy: 2026 Update Expert Panel on Neurological Imaging ACR Appropriateness Criteria ACR Appropriateness Criteria® Chronic Knee Pain: Update 2026 Expert Panel on Musculoskeletal Imaging Research Article Supply Chain Vulnerabilities in Breast Imaging: Site- and Network-Level Strategies for a Concentrated Consumable Market Kommula et al.
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20. ACR Appropriateness Criteria ACR Appropriateness Criteria® Chronic Knee Pain: Update 2026 Expert Panel on Musculoskeletal Imaging
21. Research Article Supply Chain Vulnerabilities in Breast Imaging: Site- and Network-Level Strategies for a Concentrated Consumable Market Kommula et al.
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Diffusion-Weighted Imaging with Color-Coded Images: Towards a Reduction in Reading Time While Keeping a Similar Accuracy
The aim of this study was to develop a diagnostic tool capable of providing diffusion and apparent diffusion coefficient (ADC) map information in a single color-coded image and to assess the performance of color-coded images compared with their corresponding diffusion and ADC map. The institutional review board approved this retrospective study, which sequentially enrolled 36 head MRI scans. Diffusion-weighted images (DWI) and ADC maps were compared to their corresponding color-coded images. Four raters had their interobserver agreement measured for both conventional (DWI) and color-coded images. Differences between conventional and color-coded images were also estimated for each of the 4 raters. Cohen’s kappa and percent agreement were used. Also, paired-samples t -test was used to compare reading time for rater 1. Conventional and color-coded images had substantial or almost perfect agreement for all raters. Mean reading time of rater 1 was 47.4 seconds for DWI and 27.9 seconds for color-coded images ( P = .00007). These findings are important because they support the role of color-coded images as being equivalent to that of the conventional DWI in terms of diagnostic capability. Reduction in reading time (which makes the reading easier) is also demonstrated for one rater in this study.
The RSNA Pediatric Bone Age Machine Learning Challenge
Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze AI model creation, and identify innovators in medical imaging. Materials and Methods The goal of this challenge was to solicit individuals and teams to create an algorithm or model using ML techniques that would accurately determine skeletal age in a curated data set of pediatric hand radiographs. The primary evaluation measure was the mean absolute distance (MAD) in months, which was calculated as the mean of the absolute values of the difference between the model estimates and those of the reference standard, bone age. Results A data set consisting of 14 236 hand radiographs (12 611 training set, 1425 validation set, 200 test set) was made available to registered challenge participants. A total of 260 individuals or teams registered on the Challenge website. A total of 105 submissions were uploaded from 48 unique users during the training, validation, and test phases. Almost all methods used deep neural network techniques based on one or more convolutional neural networks (CNNs). The best five results based on MAD were 4.2, 4.4, 4.4, 4.5, and 4.5 months, respectively. Conclusion The RSNA Pediatric Bone Age Machine Learning Challenge showed how a coordinated approach to solving a medical imaging problem can be successfully conducted. Future ML challenges will catalyze collaboration and development of ML tools and methods that can potentially improve diagnostic accuracy and patient care. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Siegel in this issue.
The Type III Secretion System (T3SS)-Translocon of Atypical Enteropathogenic Escherichia coli (aEPEC) Can Mediate Adherence
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