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Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, co-organized by the ACR and the RSNA, participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives. The major themes that emerged fell into two categories: (1) AI product development and (2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economic, and health care settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods conducted by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Many radiologists are considering investments in artificial intelligence (AI) to improve the quality of care for our patients. This article outlines considerations for the purchasing process beginning with performance evaluation. Practices should decide whether there is a need to independently verify performance or accept vendor-provided data. Successful implementations will consider who will receive AI results, how results will be presented, and the impact on efficiency. The article provides education on infrastructure considerations including the benefits and drawbacks of best-of-breed and platform approaches in addition to highly specialized server requirements like graphical processing unit availability. Finally, the article presents financial and quality and safety considerations, some of which are unique to AI. Examples include whether additional revenue could be obtained, as in the case of mammography, and whether an AI model unintentionally leads to reinforcing healthcare disparities., (Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

We compared the prevalence of a baseline diagnosis of cancer in patients with and without misty mesentery (MM) and determined its association with the development of a new cancer. This was a retrospective, HIPAA-compliant, IRB-approved case-control study of 148 cases and 4:1 age- and gender-matched controls. Statistical tests included chi-square, t-test, hazard models, and C-statistic. Patients with MM were less likely to have cancer at baseline (RR=0.74, p=0.003), but more likely to develop a new malignancy on follow-up (RR=2.13, p=0.003; survival analysis HR 1.74, p=0.05). MM may confer an increased probability of later developing cancer, particularly genitourinary tumors., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Workflow interruptions in the health care delivery environment are a major contributor to medical errors and have been extensively studied within numerous hospital settings, including the nursing environment and the operating room, along with their effects on physician workflow. Less understood, though, is the role of interruptions in other highly specialized clinical domains and subspecialty services, such as diagnostic radiology. The workflow of the on-call radiologist, in particular, is especially susceptible to disruption by telephone calls and other modes of physician-to-physician communication. Herein, the authors describe their initial efforts to quantify the degree of interruption experienced by on-call radiologists and examine its potential implications in patient safety and overall clinical care., (Copyright © 2014 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Background: Brain metastases from colorectal cancer (CRC) are uncommon. There has been relatively little published on the host and tumor factors that might lead to this clinical scenario. We reviewed all cases of brain metastases from CRC at Dartmouth-Hitchcock Medical Center over a more than 20-year period to establish incidence and to identify patient and cancer characteristics which were associated with their development., Patients and Methods: We present a retrospective review of 39 confirmed cases of brain metastases from CRC diagnosed between 1984 and 2006. Immunohistochemical staining for CXCR4 was performed on all available brain metastasis biopsy specimens., Results: The incidence of brain metastases from CRC was 2.3%. Left-sided primary colon tumors predominated. The majority of patients had pulmonary metastases at the time brain metastases were identified, and those with preexisting pulmonary metastases had progression of that disease. All patients were symptomatic from brain metastases, and the cerebellum was the most common area of brain involvement. Immunohistochemical analysis confirmed strong expression of CXCR4 in all brain metastases sampled., Conclusion: The incidence of brain metastases from CRC is low. Primary tumor in the left colon, long-standing pulmonary metastases, especially those with recent progression, and CXCR4 expression by tumor cells are all associated with increased risk of brain metastases. Increased survival among patients with metastatic CRC will likely result in an increased incidence of brain metastases. Further characterization of the role of tumor and host factors might yield better insight into the development, and potentially the prevention, of this devastating situation.

Background: Brain metastases from colorectal cancer (CRC) are uncommon. There has been relatively little published on the host and tumor factors that might lead to this clinical scenario. We reviewed all cases of brain metastases from CRC at Dartmouth-Hitchcock Medical Center over a more than 20-year period to establish incidence and to identify patient and cancer characteristics which were associated with their development. Patients and Methods: We present a retrospective review of 39 confirmed cases of brain metastases from CRC diagnosed between 1984 and 2006. Immunohistochemical staining for CXCR4 was performed on all available brain metastasis biopsy specimens. Results: The incidence of brain metastases from CRC was 2.3%. Left-sided primary colon tumors predominated. The majority of patients had pulmonary metastases at the time brain metastases were identified, and those with preexisting pulmonary metastases had progression of that disease. All patients were symptomatic from brain metastases, and the cerebellum was the most common area of brain involvement. Immunohistochemical analysis confirmed strong expression of CXCR4 in all brain metastases sampled. Conclusion: The incidence of brain metastases from CRC is low. Primary tumor in the left colon, long-standing pulmonary metastases, especially those with recent progression, and CXCR4 expression by tumor cells are all associated with increased risk of brain metastases. Increased survival among patients with metastatic CRC will likely result in an increased incidence of brain metastases. Further characterization of the role of tumor and host factors might yield better insight into the development, and potentially the prevention, of this devastating situation.