ArticlePediatric radiology2025
RADHawk-an AI-based knowledge recommender to support precision education, improve reporting productivity, and reduce cognitive load.
Article in Pediatric radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Cognitive Workload and Mental Burden in Health Care Professionals Interacting With AI: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Use of large language models for providing automated feedback in medical imaging education: a systematic review.Frontiers in medicine · 2026Pooled it
- Evaluating Generative Artificial Intelligence as an Educational Tool for Radiology Resident Report Drafting.Journal of the American College of Radiology : JACR · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUsing artificial intelligence (AI) to augment knowledge is key to establishing precision education in modern radiology training. Our department has developed a novel AI-derived knowledge recommender, the first reported precision education program in radiology, RADHawk (RH), that augments the training of radiology residents and fellows by pushing personalized and relevant educational content in real-time and in context with the case being interpreted. PURPOSE: To assess the impact on trainees of an AI-based knowledge recommender compared to traditional knowledge sourcing for radiology reporting through reporting time, quality, cognitive load, and learning experiences. MATERIALS AND
methodsA mixed methods prospective study allocated trainees to intervention and control groups, working with and without access to RH, respectively. Validated questionnaires and observed and graded simulated picture archiving and communication system (PACS)-based reporting at the start and end of a month's rotation assessed technology acceptance, case report quality, case report time and sourcing time, cognitive load, and attitudes toward modified learning strategies. Non-parametric regression analyses and Mann-Whitney tests were used to compare outcomes between groups, with significance set at P<0.05.
resultsThe intervention group (n=28) demonstrated a statistically significant reduction in the case report time by -162 s per case (95%CI -275.76 s to -52.40 s) (P-value = 0.002) and an increase of 14% (95%CI 8.1-19.8%) (P-value <0.001) in accuracy scores compared to the control group (n=29) at the end of the rotation. The intervention group also showed lower levels of mental demand (P=0.030) and experienced less effort (P=0.030) and frustration (P=0.030) while reporting. Additionally, >78% of the intervention group gave positive ratings on RH's effectiveness, increase in productivity, job usefulness, and ease of use. Eighty-nine percent of participants in the intervention group requested access to RH for their next rotation.
conclusionThis study demonstrates that RH, as the first reported AI-derived knowledge recommender for radiology education, significantly reduces reporting time and improves reporting accuracy while reducing overall workload and mental demand for radiology trainees. The high acceptance among trainees suggests its potential for supporting self-directed learning. Further testing of a larger external cohort will support more widespread implementation of RH for precision education.
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Registered trials
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