Cook, Tessa S.

Abstract Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. 2021; Gilson et al. 2023; Almeida et al. 2024; Krishna et al. 2024). These advanced algorithms have the potential to improve disease detection, diagnosis, prognosis, and treatment outcomes. However, the complexity of machine learning models, the large amounts of curated and annotated data required by some methods, and the potential for bias and error make it challenging for individuals to safely and effectively leverage these methods (Lin et al. 2024; Guo et al. 2024; Xu et al. 2024; Linguraru et al. 2024; Wood et al. 2019). To address these challenges, the American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) have worked together to develop a syllabus detailing a recommended set of competencies for medical imaging professionals interacting with these systems. This guide is aimed at four different personas: users of AI systems, purchasers of AI systems, individuals who provide clinical expertise during the development of AI systems (“clinical collaborators”), and developers of AI systems.1 This is a syllabus, not a curriculum, and is intentional in this scope. Recognizing that individuals may benefit from different presentations of the same material, this work enumerates a series of relevant competencies but does not prescribe, nor offer, a method of instruction (Schuur, Rezazade Mehrizi, and Ranschaert 2021; Garin et al. 2023). By addressing the task‐specific demands of each role, this guide will enable medical imaging professionals to utilize machine learning systems more safely and effectively, ultimately improving patient care and outcomes.

Abstract Background There has been increasing interest in the superficial circumflex iliac artery perforator (SCIP) flap as a source of thin, pliable soft tissue combined with a favorable donor site. Despite several clinical series from Asia, barriers to adoption include reluctance to perform submillimeter “supermicrosurgery” and the effect of body habitus on flap feasibility. The purpose of this study is to distinguish vascular anatomic characteristics of the SCIP flap in a North American population. Methods Computed tomography angiography was examined in 84 flaps in healthy prospective renal donor patients from a radiographic database. Descriptive statistics as well as linear regression comparing variables to body mass index (BMI) were performed. Results Mean BMI was 27.1 ± 3.5 kg/m2, while the mean patient age was 47.8 ± 11.4 years. The superficial circumflex iliac artery (SCIA) originated from the common femoral artery in 92% cases, with remainder originating from the profunda femoris. The mean vessel diameter was 1.85 mm at source vessel origin. Distance from skin to source vessel averaged 30.7 mm. Suprascarpal subcutaneous thickness averaged 16.5 mm. The mean distance from Scarpa's fascia to vessel origin was 14.1 mm. Direct three-dimensional distance from vessel origin to pubic tubercle was 50.2 mm. A medial and lateral perforator split off of the SCIA was observed in 38 cases (45%). Significant differences were shown when comparing BMI to skin to source vessel distance (p < 0.001), suprascarpal subcutaneous fat thickness (p < 0.001), and fascial distance to vessel origin (p < 0.001). BMI did not significantly affect vessel diameter. Conclusion Despite a significantly higher BMI than many previously published cohorts, the SCIP remains an excellent source of thin and pliable tissue. When dissected closer to the source vessel, a vessel caliber of nearly 2 mm can be achieved, which may obviate the need for “supermicrosurgery” in this population.

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.