Artificial intelligence in radiology education reports positive outcomes in 86% of reviewed studiesArtificial intelligence shows promise for training new radiology professionals

Frontiers in Medicine Published September 6, 2026 DOI ↗ Editorial oversight: Dr. Lars van Dijk, PhD · Surgical, Procedural & Diagnostic

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Key Takeaway

Note that 86% of studies report positive educational outcomes for AI in radiology, despite significant ethical and infrastructure barriers.

This systematic scoping review synthesizes 29 original studies regarding the integration of artificial intelligence, including machine learning and generative AI, into radiology education. The review focuses on educational outcomes such as skill development, engagement, and performance, as well as secondary outcomes like AI literacy and diagnostic reasoning.

The synthesis indicates that 86% of the included studies reported positive or improved educational outcomes. Skill development was the most frequently investigated outcome, appearing in 55.2% of the studies. Performance was investigated in 27.6% of studies, while engagement was investigated in 17.2% of studies. These findings suggest a general trend toward positive educational impacts when AI is integrated into the curriculum.

Several limitations and challenges were identified, including the reliability of generative AI, hallucinations, and ethical use concerns. Additionally, the authors note barriers such as limited faculty expertise, insufficient formal training, and inadequate infrastructure. While AI has potential to enhance personalized learning and competency, the review suggests that successful implementation requires standardized curricula and robust ethical oversight.

Learning to read medical images is a complex task that requires precision and confidence. A review of 29 different studies looked at how artificial intelligence, including machine learning and generative AI, impacts the way radiology students and professionals are trained. The goal was to see if these tools actually help people learn the ropes of medical imaging.

The findings were encouraging. Out of the studies reviewed, 86% reported positive or improved educational outcomes. These outcomes included better skill development, higher engagement, and improved performance. Specifically, skill development was the most common area of success. The research also looked at how AI helps with things like confidence and getting ready for real-world clinical work.

While the results are positive, there are hurdles to clear. The review noted several challenges, such as the risk of AI hallucinations, the need for better faculty expertise, and the importance of ethical oversight. Because the technology is still evolving, experts say we need standardized teaching plans and better infrastructure to make sure these tools are used safely and effectively in classrooms.

What this means for you:

Most studies show AI can improve skills and engagement for people learning to become radiology experts.

Common questions

How does artificial intelligence help people learn radiology?

Artificial intelligence can help with skill development, which was the most frequently investigated outcome in the research. It also helps with engagement and can improve a learner's confidence and readiness for clinical work. In the studies reviewed, 86% of the reports showed positive or improved educational outcomes for those using these tools.

What are the risks of using AI in medical training?

There are several challenges to consider, including the reliability of generative AI and the risk of hallucinations. Other concerns include the need for better faculty expertise, the lack of formal training for some, and the need for clear ethical oversight and governance to ensure the tools are used correctly.

Is AI a proven replacement for traditional radiology training?

The research does not say AI replaces traditional methods. Instead, it suggests that AI has the potential to strengthen education through personalized learning. However, experts note that for this to work, schools need standardized curricula and better infrastructure to manage the technology effectively.

Study Details

Study typeSystematic review

EvidenceLevel 1

PublishedSep 2026

View Original Abstract ↓

MotivationArtificial intelligence (AI) is reshaping radiology through advances in machine learning, deep learning, and generative AI. As these technologies become embedded in diagnostic workflows, radiology education must adapt to prepare learners for AI-enabled clinical practice. This review aimed to synthesize current evidence, identify educational priorities, and highlight challenges and opportunities for implementation.IntroductionThe growing adoption of AI in medical imaging requires radiology curricula to extend beyond image interpretation and encompass AI literacy, ethical reasoning, critical appraisal, and human–AI collaboration. Understanding how AI is currently incorporated into radiology education is essential for developing effective training frameworks. This review examined educational approaches, learner outcomes, and implementation challenges associated with AI in radiology education.MethodologyA scoping review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), using the Population–Concept–Context(PCC) framework to define the review question and eligibility criteria. Searches were performed in Scopus, PubMed, and IEEE Xplore for studies published between 2020 and 2025. After screening and eligibility assessment, 29 original studies were included. Educational outcomes were classified into skill development, engagement, and performance domains.Results and discussionSkill development was the most frequently investigated outcome (55.2%), followed by performance (27.6%) and engagement (17.2%). Approximately 86% of studies reported positive or improved educational outcomes. AI-based interventions enhanced learner confidence, AI literacy, diagnostic reasoning, and readiness for clinical implementation. Generative AI tools showed promise for tutoring, assessment, and self-directed learning but raised concerns regarding reliability, hallucinations, bias, and ethical use. Common barriers included limited faculty expertise, insufficient formal training, curricular overcrowding, inadequate infrastructure, and governance challenges.ConclusionAI has significant potential to strengthen radiology education through personalized learning, competency development, and technology-enhanced training. Nevertheless, sustainable implementation requires standardized curricula, faculty development, competency frameworks, and robust ethical oversight. Future research should focus on longitudinal outcomes and evidence-based educational models that prepare radiology professionals for increasingly AI-integrated healthcare systems.

Artificial intelligence in radiology education reports positive outcomes in 86% of reviewed studiesArtificial intelligence shows promise for training new radiology professionals

Common questions

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Artificial intelligence in radiology education reports positive outcomes in 86% of reviewed studiesArtificial intelligence shows promise for training new radiology professionals

Common questions

Study Details

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Clinical research that matters. Delivered to your inbox.

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