Mahmood, Usman

Abstract Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Abstract The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.

AbstractModern fluoroscopes used for image guidance have become quite complex. Adding to this complexity are the many regulatory and accreditation requirements that must be fulfilled during acceptance testing of a new unit. Further, some of these acceptance tests have pass/fail criteria, whereas others do not, making acceptance testing a subjective and time‐consuming task. The AAPM Task Group 272 Report spells out the details of tests that are required and gives visibility to some of the tests that while not yet required are recommended as good practice. The organization of the report begins with the most complicated fluoroscopes used in interventional radiology or cardiology and continues with general fluoroscopy and mobile C‐arms. Finally, the appendices of the report provide useful information, an example report form and topics that needed their own section due to the level of detail.

The American Association of Physicists in Medicine (AAPM) has established a comprehensive Code of Ethics for its members. The Code is a formal part of AAPM governance, maintained as Professional Policy 24, and includes both principles of ethical practice and the rules by which a complaint will be adjudicated. The structure and content of the Code have been crafted to also serve the much broader purpose of giving practical ethical guidance to AAPM members for making sound decisions in their professional lives. The Code is structured in four major parts: a Preamble, a set of ten guiding Principles, Guidelines that elucidate the application of the Principles in various practice settings, and the formal Complaint process. Guidelines have been included to address evolving social and cultural norms, such as the use of social media and the broadening scope of considerations important in an evolving workplace. The document presented here is the first major revision of the AAPM Code of Ethics since 2008. This revision was approved by the Board of Directors to become effective 1 January 2019.