"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making

Google (United States)

Indexed incrossref

Abstract

Although rapid advances in machine learning have made it increasingly applicable to expert decision-making, the delivery of accurate algorithmic predictions alone is insufficient for effective human-AI collaboration. In this work, we investigate the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant. In a qualitative lab study, we interviewed 21 pathologists before, during, and after being presented deep neural network (DNN) predictions for prostate cancer diagnosis, to learn the types of information that they desired about the AI assistant. Our findings reveal that, far beyond understanding the local, case-specific reasoning behind any model decision,…

Citation impact

562
total citations
FWCI
13.31
Percentile
100%
References
81
Citations per year

Authors

5

Topics & keywords

Keywords
  • Onboarding
  • Computer science
  • Applications of artificial intelligence
  • Transparency (behavior)
  • Clinical decision making
  • Data science
  • Artificial intelligence
  • Knowledge management
UN Sustainable Development Goals
  • Peace, Justice and strong institutions
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