With the exponential growth of artificial intelligence/machine learning in our field, there is an ever-growing demand to provide resources to empower radiologists to understand, develop, and implement AI/machine learning tools into their practice.
We aim to target particularly relevant Informatics and AI resources, and bring them to the attention of TRS members via this webpage. We also welcome any suggestions for content addition, corrections, or improvements from any interested member.
Table of Contents
Click on green buttons below to be directed to individual content areas.
These Links to outside websites are not to be considered an endorsement by TRS. Examples included in this webpage are for illustrative purposes only, not specific recommendations.
Please contact Dr. Paul Weatherall or Dr. O’Neill Dr. O'Neill if you have suggestions for the Informatics Committee and/or the web resources.
*Adapted from ACR DSI
FDA Cleared - AI-Software
- Updated list maintained by ACR Data Science Institute: AI Central
American Board of Artificial Intelligence in Radiology, or ABAIR, formed 2022-Aug-17. Offering a two-day onsite course with the following topics:
- AI reimbursement strategies,
- HIPAA considerations,
- AI terminology,
- Medicolegal concerns and IT principles as applied to AI in radiology.
Pass a 75-question exam = certified “AI software in radiology” (“AiRC-certified”). ABAIR website lists initial course’s cost at $449 with a 30% discount for students. Website here, press announcement here.
Article Title | Journal | Publication Year |
| Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts | Radiology: Artificial Intelligence | RSNA | 2024 |
| Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA | Radiology: Artificial Intelligence | RSNA | 2024 |
| Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup | ACR | 2023 |
Radiology Resident Training – 1 day Introductory Course (Determining Effectiveness). AI-RADS: Successes and challenges of a novel artificial intelligence curriculum for radiologists across different delivery formats. Med. Technol., 04 January 2023. Volume 4 - 2022 | Frontiers | 2023 |
Artificial Intelligence and Radiology Education
Tejani, Ali S.(TRS member), et al. Radiology:Artificial Intelligence Vol. 5 No 1. | RSNA | 2022 |
| HIMSS-SIIM Collaborative White papers | SIIM | 2022 |
| Mitigating Bias in Machine Learning collection from Radiology: Artificial Intelligence | RSNA | 2022 |
AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency? | Skeletal Radiol | 2022 |
IHE White Paper on AI interoperability
| IHE | 2021 |
Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report | Canadian Association of Radiologists | 2021 |
| Noninterpretive Uses of Artificial Intelligence in Radiology | Academic Radiology | 2021 |
Who Will Pay for AI? | Radiology: AI | 2021 |
Deep Learning for Natural Language Processing in Radiology—Fundamentals and a System | JACR | 2020 |
Ethics of AI in Radiology (Multi-Society Statement): Summary Ethics of AI in Radiology (Multi-Society Statement): Long Version | ACR | 2019 |
Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers : American Journal of Roentgenology : Vol. 212, No. 3 (AJR) (ajronline.org) | AJR | 2019 |
Guide for Evaluating Clinical Performance and Effect of AI for Medical Diagnosis and Prediction | Radiology | 2018 |
