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With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Purpose: Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software., Methods: A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices., Results: In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened., Conclusions: Artificial intelligence-based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective., (Published by Elsevier Inc.)

MRI is a ubiquitous medical imaging technology typically using superconductivity to generate a strong, homogeneous, and generally ceaseless magnetic field. MRI and its magnetic field pose many safety hazards, including magnetic forces on metals, tissue heating and burns, nerve stimulation, bioeffects, acoustic noise, and contrast agent complications. The primary concern is that a wide variety of patients, staff members, technologists, and physicians can approach the incessant magnetic field, creating great potential for accidents that could occur if metals from the environment, adornments, implants, and other unintended sources are also present in or near the field. Many accidents have occurred and are occasionally reported in the United States and countries all over the world. Through carefully structured oversight and the establishment of strict guidelines regarding access, responsibilities, and training, these risks can be mitigated, and accidents can be prevented. Fortunately, there is currently a wide variety of resources available to facilitate the successful implementation of an effective MRI safety program. This article presents a general overview of and the authors' experience with an MRI safety program in terms of risk management and training. The MR safety program requirements and regulations in the United States devised by The Joint Commission and the ACR are also discussed. With these resources and a carefully selected team, the risk for MRI-related accidents can be vastly reduced if not completely eliminated., (Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.)