Radiology Training Faces an AI Skills Shift

Artificial intelligence is becoming part of radiology practice, from image acquisition to interpretation and decision support. This shift is changing radiology training, requiring future specialists to use AI confidently while maintaining core interpretive and clinical reasoning skills. Current trainee education remains inconsistent and dependent on local faculty expertise. A clearer training agenda must preserve independent judgement while developing competencies for responsible AI use.

Current Gaps in AI Education

Radiology education already includes AI-related activities such as lectures and simulations. These initiatives tend to focus on technical knowledge, but they do not fully address how AI reshapes the core competencies of radiology practice. The gap is important because AI affects how radiologists gather information, interpret images and reach clinical decisions. Training that concentrates only on system mechanics may leave trainees less prepared for the cognitive and professional demands of AI-supported work.

The Canadian Association of Radiologists called for AI education within the profession in 2018, and later multi-society statements supported appropriate oversight during clinical adoption. Even so, education for trainees remains uneven. Local expertise continues to shape what individual programmes can provide. At the same time, there is no consensus on the core AI competencies that radiology trainees should develop. The specialty also faces a broader skills transition, as some competencies may diminish while new ones emerge.

Previous technological advances have also reshaped radiology practice, reinforcing the need for deliberate curriculum choices rather than ad hoc adaptation. Training programmes therefore need flexible curricula that can adapt to changing technology and practice demands. They also need to define which foundational competencies remain essential for safe, independent practice and meaningful oversight of AI systems.

Judgement, Bias and Core Skills

AI can alter how trainees develop diagnostic reasoning. Automation bias may encourage over-reliance on automated systems, including among experienced radiologists. The problem can be greater when confidence is low or cognitive load is high. Trust calibration is therefore a central training issue. Trainees need to learn when to trust AI, when to question it and how to weigh its output against clinical judgement. Excessive reliance and under-utilisation can both undermine performance.

The risk is especially relevant for trainees whose diagnostic schemas are still forming. A seemingly reliable algorithm may be difficult to override. Early reliance on AI can also reduce the effortful practice required to develop systematic search patterns, critical appraisal, differential diagnosis generation and clinical integration. Cognitive off-loading may lead to never-skilling, where key abilities fail to develop. Learners may also absorb AI errors as truth, allowing faulty diagnostic reasoning to spread.

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For experienced radiologists, fewer opportunities for independent practice may contribute to de-skilling across domains. Core interpretive and clinical reasoning skills remain indispensable for oversight and for protection against AI failures, erroneous outputs and adversarial attacks. Without sound judgement and properly calibrated trust, reliance on AI may increase patient risk, introduce financial costs, create liability ambiguity and erode public trust.

Responsible Training and Fair Access

AI can also strengthen education when it supports meaningful patient care rather than simply shifting radiologists into oversight roles. Systems can provide structured and timely feedback on missed findings, helping trainees identify gaps and performance patterns that supervisors may find difficult to detect. AI can enrich simulation-based education and redistribute cognitive effort, allowing trainees to focus on higher-order competencies such as clinical reasoning and decision-making. In this setting, AI may support and accelerate competency development.

Training should preserve opportunities for interpretation without AI support. For modalities radiologists continue to interpret, junior trainees should make their own assessments before AI exposure where feasible, with AI used as a second reader. Conceptual AI literacy should extend beyond technical familiarity and include intended use, performance characteristics and failure modes. AI-focused journal clubs, tool-specific evaluations and shared repositories of AI failures can support critical appraisal and post-deployment monitoring. Training should also cover how AI outputs influence perception and decision-making, with assessment comparing performance with and without AI support where appropriate.

Faculty development is critical because attending radiologists model responsible AI use. Evidence on learning outcomes must guide education so interventions preserve essential competencies, foster AI-relevant skills and support safe, independent practice over time. Training must also account for unequal access across institutions, preparing radiologists to practise both with and without AI while keeping core competencies independent of specific tools.

AI will change the competencies radiologists need, but training will determine whether that change strengthens or weakens human expertise. Education must protect interpretive skill, clinical judgement and independent reasoning while developing the literacy required to use AI responsibly. Radiologists should not become mere overseers of automated output. Where AI systems perform tasks more reliably, radiologists can focus on areas where human judgement, creativity and adaptability retain distinct value. Aligning training with changing radiology practice can support safe, high-quality care and preserve the meaning of the profession.

Source: Canadian Association of Radiologists Journal

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