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RADHawk-an AI-based knowledge recommender to support precision education, improve reporting productivity, and reduce

Julian Lopez-Rippe1, Manasa Reddy2, Maria Camila Velez-Florez2

  • 1Children's Hospital of Philadelphia, Philadelphia, PA, USA. lopezrippj@chop.edu.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances radiology training with RADHawk (RH), a novel knowledge recommender. RH significantly reduces reporting time and improves accuracy for trainees, supporting precision education.

Keywords:
Artificial intelligenceEducationPediatricsRadiology

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Area of Science:

  • Artificial Intelligence in Medical Education
  • Radiology Training and Development
  • Precision Education Systems

Background:

  • Developed RADHawk (RH), the first AI-derived knowledge recommender for radiology.
  • RH provides personalized, real-time educational content for radiology residents and fellows.
  • Aims to augment knowledge and establish precision education in radiology.

Purpose of the Study:

  • Assess the impact of an AI-based knowledge recommender (RH) versus traditional methods.
  • Evaluate effects on radiology trainee reporting time, quality, and cognitive load.
  • Compare learning experiences and attitudes toward AI in radiology education.

Main Methods:

  • Mixed methods prospective study comparing RH users (intervention) to non-users (control).
  • Assessed technology acceptance, report quality, time, cognitive load, and learning attitudes via questionnaires and simulated reporting.
  • Used non-parametric regression and Mann-Whitney tests for outcome comparisons (P<0.05).

Main Results:

  • Intervention group showed significant reduction in reporting time (-162s/case) and increased accuracy (+14%).
  • Lower mental demand, effort, and frustration reported in the intervention group.
  • >78% of intervention group rated RH positively; 89% requested continued access.

Conclusions:

  • RH, the first AI knowledge recommender in radiology, reduces reporting time and improves accuracy.
  • RH decreases trainee workload and mental demand, enhancing the learning experience.
  • High acceptance suggests RH's potential for self-directed learning and precision radiology education.