Introduction: Artificial intelligence (AI) is rapidly transforming the field of radiology,
influencing image interpretation, diagnostic workflows, and clinical decision-making. The
diffusion of AI in clinical practice has necessitated radiologists to be trained in these
technologies, and residency programs have been increasingly motivated to equip their trainees
with the skills and knowledge to implement and critically assess these tools. However, education
for radiological imaging differs; graduate medical education programs lack standardization, and
courses vary in length and hands-on learning opportunities. This literature review analyzes the
current approaches, discrepancies, and future directions in AI training in radiology residency.
Methodology: Articles indexed by MEDLINE via Pubmed, EBSCO, Embase, and Google Scholar were captured by searching across three domains: Artificial Intelligence, Radiology/Medical Imaging, and Education/Residency Training.
Results: Educational strategies identified include didactic sessions, journal clubs, hands-on learning, and certificate programs. Systematic reviews and resident surveys consistently indicate a preference for longitudinal, hands-on, and case-based learning throughout residency. Key challenges in implementing AI education are the absence of standardized curricula, scheduling constraints for residents, and limited faculty expertise. Discrepancies exist in course content depth and assessment methods, with frequent omission of essential topics such as algorithmic bias, data management, and regulatory oversight.
Discussion: Standardization of AI competencies is urgently needed to prepare radiology trainees for the evolving landscape of AI in clinical practice. This review highlights the necessity for standardized, evidence-based, and longitudinal guidelines that incorporate practical skills, ethical considerations, and regulatory frameworks to ensure safe and effective integration of AI into radiology education.
Methodology: Articles indexed by MEDLINE via Pubmed, EBSCO, Embase, and Google Scholar were captured by searching across three domains: Artificial Intelligence, Radiology/Medical Imaging, and Education/Residency Training.
Results: Educational strategies identified include didactic sessions, journal clubs, hands-on learning, and certificate programs. Systematic reviews and resident surveys consistently indicate a preference for longitudinal, hands-on, and case-based learning throughout residency. Key challenges in implementing AI education are the absence of standardized curricula, scheduling constraints for residents, and limited faculty expertise. Discrepancies exist in course content depth and assessment methods, with frequent omission of essential topics such as algorithmic bias, data management, and regulatory oversight.
Discussion: Standardization of AI competencies is urgently needed to prepare radiology trainees for the evolving landscape of AI in clinical practice. This review highlights the necessity for standardized, evidence-based, and longitudinal guidelines that incorporate practical skills, ethical considerations, and regulatory frameworks to ensure safe and effective integration of AI into radiology education.