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Guiding principles for the responsible development of artificial intelligence tools for healthcare.

Kimberly BadalCarmen M LeeLaura J Esserman
Published in: Communications medicine (2023)
Several principles have been proposed to improve use of artificial intelligence (AI) in healthcare, but the need for AI to improve longstanding healthcare challenges has not been sufficiently emphasized. We propose that AI should be designed to alleviate health disparities, report clinically meaningful outcomes, reduce overdiagnosis and overtreatment, have high healthcare value, consider biographical drivers of health, be easily tailored to the local population, promote a learning healthcare system, and facilitate shared decision-making. These principles are illustrated by examples from breast cancer research and we provide questions that can be used by AI developers when applying each principle to their work.
Keyphrases
  • artificial intelligence
  • healthcare
  • machine learning
  • big data
  • deep learning
  • health information
  • public health
  • mental health
  • metabolic syndrome
  • insulin resistance
  • affordable care act
  • health insurance