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TRV-2026-1007Certified recordPeer-reviewed

Hands-on Artificial Intelligence Education for Radiology Residents: A Three-year Feasibility and Curriculum Implementation Study

Rationale and objectives Artificial intelligence (AI) has rapidly transformed radiology practice, yet structured and practical AI education remains inconsistently integrated into radiology residency training. We developed and implemented a hands-on AI curriculum designed to integrate core computational principles with clinically relevant imaging applications. This study describes the curriculum design and evaluates its feasibility, reproducibility, and preliminary educational outcomes over three consecutive year…

Education · The Trace — both readings · certified 2026-09-07 · v1 · article view · machine-readable

Current reading — gain

An 8-hour hands-on AI rotation delivered to 27 radiology residents over three years was completed by all participants with consistent structure, yielding approximate 80-90% post-training quiz performance and favorable ratings for overall value.

Current reading — problem

Learner survey showed mixed perceptions with half of respondents reporting technical complexity did not match their training level, prompting requests for a more introductory primer and greater clinical emphasis.

What this doesn’t fix

Single-institution design with small cohorts, retrospective approximate performance estimate without centrally archived individual scores, and survey response from only 16 of 27 residents limits generalizability; authors note need for multi-institutional prospective validation.

Evidence

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Truvace Impact Record TRV-2026-1007, v1: “Hands-on Artificial Intelligence Education for Radiology Residents: A Three-year Feasibility and Curriculum Implementation Study.” Truvace, 2026-09-07. /record/TRV-2026-1007 (accessed at citation time). sha256 1e6fe06248dbc2bf…

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