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

Journal: Pediatric radiology
PMID: 39644355

Abstract

BACKGROUND: Using 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.

Authors

  • Julian Lopez-Rippe
    Children's Hospital of Philadelphia, Philadelphia, PA, USA. lopezrippj@chop.edu.
  • Manasa Reddy
    Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Maria Camila Velez-Florez
    Children's Hospital of Philadelphia, Department of Radiology, 3401 Civic Center Blvd., Philadelphia, PA. 19104. Electronic address: velezmc@chop.edu.
  • Raisa Amiruddin
    Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Wondwossen Lerebo
    Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Ami Gokli
    Division Chief of Pediatric Radiology, Staten Island University Hospital, Staten Island, New York; and Associate Program Director, Department of Radiology Residency Program, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Uniondale, New York.
  • Michael Francavilla
    Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Janet Reid
    Children's Hospital of Philadelphia, Philadelphia, PA, USA. reidj@chop.edu.

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