The integration of artificial intelligence in radiation medical physics: insights from an international survey with regional variability

Clinical Snapshot

Key Findings

• P Value: Significant regional differences reported (specific values not provided)

• Effect Size: Strong association between AI familiarity and adoption (specific measures not provided)

• Primary Outcome: Moderate AI familiarity among medical physicists with nearly half not incorporating AI despite interest

• Nnt Or Sensitivity: Higher AI usage in developed countries with regional variability in adoption patterns

• Confidence Interval: Not reported in abstract

Clinical Application

Findings suggest need for structured AI training programs and infrastructure development before widespread implementation Relevant to ACPSEM professional development requirements and potential TGA regulatory frameworks for AI medical devices Australian medical physicists in radiation therapy, diagnostic imaging, and nuclear medicine

Abstract

Artificial intelligence (AI) is considered to be a leading technology in radiation medical physics, which has the potential for improving efficiency and precision in imaging, radiotherapy, and nuclear medicine. Nonetheless, its application in the clinical setting is hindered by education, regulation, and ethical issues. To evaluate the views of medical physicists on AI adoption, potential issues, perceived advantages, ethical issues, and training needs. A cross-sectional survey in the form of an online questionnaire was distributed internationally to practicing medical physicists, representing specialties in radiation therapy, diagnostic imaging, and nuclear medicine. The instrument was used to capture demographic variables and AI familiarity, perceived benefits and barriers, ethical issues, training preferences, and future expectations. Descriptive statistics, chi-square tests, and binary logistic regression were performed. Open-ended responses were analyzed using thematic analysis. Most respondents were moderately familiar with AI, but nearly half had not incorporated AI into their practice despite being interested. Adoption patterns differed significantly across geographic regions, with higher usage reported in developed countries. AI familiarity was strongly associated with adoption. The most prominent benefits noted were dose calculation accuracy, patient safety, and workflow efficiency. In contrast, the main obstacles were inadequate training, high implementation costs, absence of standardized protocols, and low availability of advanced equipment. Ethical apprehensions focused on accountability and reduced human oversight. Preferred educational strategies included hands-on workshops and on-the-job training. Regional analysis and modeling revealed variability in AI adoption and identified key predictors, including AI familiarity and specialization. Results indicate that AI is considered an important technology in radiation medical physics. However, implementation was influenced by regional and professional differences. These findings highlight the necessity to expand AI education, create standard training programs, improve infrastructure, and provide clearer governance frameworks to support safe and responsible AI integration.