AI-Powered Learning for Smarter Radiology Education

Alexandra Hodder, fourth-year medical student at Memorial University of Newfoundland, contributed this article.

The role of AI in radiology is rapidly evolving. From detecting lung nodules to identifying hemorrhages on neuroimaging, AI is being explored as a tool to enhance both diagnostic accuracy and workflow efficiency. Its potential lies not just in its diagnostic power, but also in streamlining workflows by assisting with triaging and recognizing repetitive, high-volume findings that can be time-consuming to identify manually.

While AI successes receive much emphasis, its imperfections often prompt concern, with many worried about missed findings. But what if these errors could be reframed as valuable learning opportunities? Most conversations around AI focus on two extremes: Breakthroughs and limitations. Yet, between this duality lies a promising educational horizon where AI errors can become lessons for the humans working alongside it.

As AI becomes increasingly integrated into radiology, it’s crucial to advocate for structured programs that equip both trainees and practicing radiologists with the skills to critically engage with these tools, ensuring safe patient care and effective learning.

When AI Gets it Wrong: Advocating for Structured AI-Enhanced Education in the Reading Room

Current radiology AI models are trained to recognize patterns and continuously refine their ability to do so. A model might flag an area of pulmonary scarring as pneumonia or miss subtle abnormalities that a radiologist would identify using clinical experience and context. For example, imagine an AI system highlighting a dense area on a chest X-ray as consolidation caused by infection. A radiologist may correctly recognize this as post-surgical fibrosis based on the clinical history and prior imaging studies. This discrepancy can prompt a more intricate train of thought: Why did the AI flag this? What contextual cues is it missing? What features led me to a different conclusion?

Such AI missteps represent valuable teaching moments, reinforcing the importance of human judgment and contextual knowledge in imaging interpretation. This teaching potential expands even further with the advent of explainable AI (XAI), which emphasizes the use of models that can clearly outline the algorithmic reasoning behind their outputs. This allows radiologists to both understand and critique how an algorithm formulated its interpretation.

By comparing one’s own analysis with the AI decision pathway, radiologists and trainees gain a deeper understanding of not just imaging, but about how the AI model processed that imaging — and why AI sometimes fall short. Institutions can further enhance learning by curating libraries of AI misinterpretations to augment conventional resident teaching files, turning errors into structured educational opportunities.

For early trainees especially, it can be difficult to conceptualize subtle distinctions in interpretation. AI miscalls may help accelerate this learning. Imagine a teaching rounds session on subdural hematomas, where a set of cases flagged by an institution’s AI system for hemorrhage is reviewed by residents who are asked to evaluate whether they agree—and, more importantly—to explain why. Such exercises sharpen pattern recognition and promote critical thinking.

Compared to traditional one-on-one reads, this kind of AI-enhanced review may allow for faster exposure to a broader range of cases, particularly when curated by the nature of the error. This approach complements human mentorship and reinforces the critical role of radiologist oversight in AI-augmented interpretation.

Empowering Trainees and Radiologists for an AI-Enhanced Future

Perhaps most importantly, AI-based teaching exercises cultivate the diagnostic humility and resilience that will be critical throughout one’s radiology career. Learning to scrutinize AI “thought” processes builds a healthy skepticism and reinforces that intelligent tools still require responsible human supervision. Shifting the perspective from viewing AI errors as liabilities to recognizing them as educational assets allows every error, false positive or omission to become a conversation starter and case study for improvement.

These types of insights strengthen a learner’s ability to critically appraise AI and encourage thoughtful clinical reasoning in a rapidly evolving technological landscape. Advocating for trainee involvement in AI-related education initiatives also ensures that the next generation of radiologists has a voice in shaping policy and program design for the environments in which they will be practicing. It would be shortsighted to advance AI in radiology without ensuring that current and future radiologists are prepared to utilize it effectively and responsibly.

Future radiologists won’t just be asked to use AI — they’ll be expected to understand and challenge it. Advocacy in our rapidly evolving specialty requires encouraging radiology departments and professional organizations to support AI literacy, structured learning programs and integration of AI insights into residency curricula. Encouraging trainee engagement ensures that learners are active participants in shaping the future of radiology practice.

Like humans, AI has flaws. Maximizing its potential requires systems that allow radiologists to learn from these flaws, and ultimately become more capable clinicians because of them. For guidance on how to integrate AI into your workflow, explore resources from the ACR Data Science Institute^® (DSI) such as ACR AI-LAB^®, the searchable AI Central database and use cases in the Define-AI Directory. DSI also offers the recognition program, ACR^® Recognized Center for Healthcare-AI, which acknowledges healthcare organizations that follow current AI best practices in imaging interpretation. The ACR quality registry, Assess-AI, provides AI clinical monitoring, including detecting when a discrepancy happens between the AI and the radiologist.

Access additional data science resources from the ACR.