Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance.

Journal: Medical physics
Published Date:

Abstract

BACKGROUND: Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability.

Authors

  • Geetha Mahadevaiah
    Philips Research India, Bangalore, 560045, India.
  • Prasad Rv
    Philips Research India, Bangalore, 560045, India.
  • Inigo Bermejo
    Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, 6229 ET, Netherlands.
  • David Jaffray
    Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
  • Andre Dekker
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Leonard Wee
    Maastricht University Medical Centre, Netherlands.