Ali S. Tejani

To address the rapid evolution of artificial intelligence in medical imaging, the authors present the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) 2024 Update.

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.

Implementation of artificial intelligence (AI) applications into clinical practice requires AI-savvy radiologists to ensure the safe, ethical, and effective use of these systems for patient care. Increasing demand for AI education reflects recognition of the translation of AI applications from research to clinical practice, with positive trainee attitudes regarding the influence of AI on radiology. However, barriers to AI education, such as limited access to resources, predispose to insufficient preparation for the effective use of AI in practice. In response, national organizations have sponsored formal and self-directed learning courses to provide introductory content on imaging informatics and AI. Foundational courses, such as the National Imaging Informatics Course – Radiology and the Radiological Society of North America Imaging AI Certificate, lay a framework for trainees to explore the creation, deployment, and critical evaluation of AI applications. This report includes additional resources for formal programming courses, video series from leading organizations, and blogs from AI and informatics communities. Furthermore, the scope of “AI and radiology education” includes AI-augmented radiology education, with emphasis on the potential for “precision education” that creates personalized experiences for trainees by accounting for varying learning styles and inconsistent, possibly deficient, clinical case volume. © RSNA, 2022 Keywords: Use of AI in Education, Impact of AI on Education, Artificial Intelligence, Medical Education, Imaging Informatics, Natural Language Processing, Precision Education

T he rapid increase in publications related to artificial intelligence (AI) in medical imaging has pinpointed the need for transparent and organized research reporting.Publications on AI research should provide the necessary information to ensure adherence to high scientific standards while allowing the independent reproduction of the research.Reproducibility is necessary to enable clinical translation and adoption of AI algorithms that may otherwise remain on paper.For this purpose, a series of tools have been developed to guide the comprehensive reporting of AI research to promote research reproducibility, adherence to ethical standards, comprehensibility of research manuscripts, and publication of scientifically valid results.The use of such guidelines has been encouraged or mandated by scientific journals to allow appropriate evaluation of research output during the review process.The majority of existing guidelines and checklists allow authors to confirm the inclusion of specific information in each manuscript section: Title, Abstract, Introduction, Methods, Results, and Discussion.The content of these checklists highlights the minimum information a journal requires for the publication of an AI study, providing researchers with guidelines to conduct their research while also easing and standardizing the review process.Existing guidelines or guidelines under development specific to AI include CLAIM (Checklist for Artificial Intelligence in Medical Imaging), STARD-AI (Standards for Reporting of Diagnostic Accuracy Study-AI), CONSORT-AI (Consolidated Standards of Reporting Trials-AI), SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-AI), FUTURE-AI (Fairness Universality Traceability Usability Robustness Explainability-AI), MI-CLAIM (Minimum Information about Clinical Artificial Intelligence Modeling), MINIMAR (Minimum Information for Medical AI Reporting), and RQS (Radiomics Quality Score).In this editorial, we compare these guidelines and their content to help readers find the best guideline for their research.Table 1 provides a high-level overview including a summary of guidelines' availability, purpose, and format and brief information about which manuscript sections they address.