Langer, Steve G.

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.

PurposeWe summarize the AAPM TG248 Task Group report on interoperability assessment for the commissioning of medical imaging acquisition systems in order to bring needed attention to the value and role of quality assurance testing throughout the imaging chain.MethodsTo guide the clinical physicist involved in commissioning of imaging systems, we describe a framework and tools for incorporating interoperability assessment into imaging equipment commissioning.ResultsWhile equipment commissioning may coincide with equipment acceptance testing, its scope may extend beyond validation of product or purchase specifications. Equipment commissioning is meant to provide assurance that a system is ready for clinical use, and system interoperability plays an essential role in the clinical use of an imaging system.ConclusionThe functionality of a diagnostic imaging system extends beyond the acquisition console and depends on interoperability with a host of other systems such as the Radiology Information System, a Picture Archive and Communication System, post‐processing software, treatment planning software, and clinical viewers.

Much work has been done on ultrasound phantoms foe biopsy and anatomic training of physicians and sonographers. Artifacts observed during scans in vivo also have been described. The authors' purpose in this article is to describe the construction and perdormance of phantoms designed to demonstrate ultrasound image artifacts for teaching purposes. Phantom images exhibiting ringdown, range errors, reverberation, and multipath artifacts produced by scanning intrauterine devices, shotgun pellets, and other objects are illustrated.