From volume to value: leveraging artificial intelligence and deliberate practice to foster precision learning in radiology.
Authors
Affiliations (6)
Affiliations (6)
- School of Computer Science, University College Dublin, Dublin, Ireland. [email protected].
- Department of Clinical Radiology, Great Ormond Street Hospital, London, United Kingdom. [email protected].
- Department of Radiology, Children's Health Ireland at Crumlin, Dublin, Ireland. [email protected].
- Department of Cardiology, Children's Health Ireland at Crumlin, Dublin, Ireland.
- Department of Clinical Radiology, Great Ormond Street Hospital, London, United Kingdom.
- Department of Radiology, Children's Health Ireland at Crumlin, Dublin, Ireland. [email protected].
Abstract
The increasing integration of artificial intelligence (AI) into radiology practice presents both opportunities and challenges for the education of future radiologists. This review critically examines the interplay between AI, the theory of deliberate practice, and radiology training. Deliberate practice, defined by focused, goal-directed activities with immediate feedback and opportunities for refinement, has been shown to be superior to traditional volume- and experience-based learning models in developing clinical expertise. AI integration risks attenuating essential learning processes by reducing primary interpretation opportunities, fostering automation bias, and promoting over-reliance on "black box" algorithms. However, AI also offers a powerful educational adjunct through precision learning, curating personalised learning experiences based on individual needs. AI can identify diagnostic errors in real time and enhance feedback mechanisms, aligning with deliberate practice principles. We argue that AI must be deliberately incorporated into radiology training to safeguard and enhance the development of diagnostic expertise. We also consider the impact AI will have on the role of the future radiologist and the importance and challenges of acquiring the necessary non-interpretative skills. We propose evidence-based recommendations for the integration of AI into residency programmes, emphasising the need for AI literacy, preservation of exposure to normal imaging findings, maintenance of primary interpretation skills, and structured feedback. We contend that thoughtful application of AI technologies offers the potential to optimise deliberate practice, accelerate skill acquisition, and ensure that future radiologists are equipped not only to work alongside AI but to surpass its limitations with superior clinical judgment and expertise.