What should radiology residency and fellowship training in artificial intelligence include? A Trainee's perspective-radiology in training

Research output: Contribution to journalReview articlepeer-review

Abstract

Artificial intelligence (AI) and machine learning (ML) have captured the imagination of researchers and clinicians alike. Of note, AI is a broad designation encompassing any technique that enhances the ability of machines to mimic human behavior, while ML represents a subset of AI techniques that enable models to improve performance with increasing exposure to data (1). AI has drawn the attention of trainees who prepare to join academic and private practices already implementing AI tools. Although programs have started to introduce technical ML concepts locally (ML theory, data curation, model development, computational methods), a standardized, holistic AI education beyond algorithm-focused lectures is lacking (2,3). Specifically, there is a need for a curriculum that introduces all trainees to factors crucial for clinical integration of AI tools; this will prepare tool deployers and users in addition to tool creators. The absence of a standardized curriculum leaves trainees to navigate AI technology without structure. Acknowledging commentary highlighting the need for an AI curriculum (4), we propose a framework that addresses the basics of ML, AI tool application to common clinical questions, and the regulatory, ethical, and economic implications on clinical practice. This curriculum would be offered in tandem with, or following, residency training either through dedicated rotations or a supplemental scholarly track. Regular content updates from local or national committees will ensure trainees are educated on clinically relevant paradigms and standards in this rapidly advancing arena.

Original languageEnglish (US)
Pages (from-to)E243-E245
JournalRADIOLOGY
Volume299
Issue number2
DOIs
StatePublished - 2021

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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