Clinical Artificial Intelligence: Design Principles and Fallacies.

Journal: Clinics in laboratory medicine
Published Date:

Abstract

Clinical artificial intelligence (AI)/machine learning (ML) is anticipated to offer new abilities in clinical decision support, diagnostic reasoning, precision medicine, clinical operational support, and clinical research, but careful concern is needed to ensure these technologies work effectively in the clinic. Here, we detail the clinical ML/AI design process, identifying several key questions and detailing several common forms of issues that arise with ML tools, as motivated by real-world examples, such that clinicians and researchers can better anticipate and correct for such issues in their own use of ML/AI techniques.

Authors

  • Matthew B A McDermott
  • Bret Nestor
    Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada.
  • Peter Szolovits
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.