Economics of AI and Human Task Sharing for Decision Making in Screening Mammography
Published:
Our paper, “Economics of AI and human task sharing for decision making in screening mammography,” was published in Nature Communications. The study asks a practical question facing healthcare organizations as artificial intelligence becomes increasingly capable: when should an AI system replace a human expert, when should experts remain fully responsible, and when is it better for humans and AI to share the work?
Using screening mammography as the application, we develop an optimization framework that compares three approaches: an expert-alone strategy in which radiologists interpret mammograms, an automation strategy in which AI performs the task, and a delegation strategy in which AI evaluates cases and selectively refers appropriate cases to radiologists. The analysis shows that the best approach depends not only on predictive performance, but also on disease prevalence, the relative costs of false-positive and false-negative decisions, algorithm costs, expert performance, and potential liability. In backtesting with data from the Digital Mammography DREAM Challenge, the delegation strategy was optimal for the strongest-performing algorithm and produced substantial cost savings compared with relying solely on human experts. More broadly, the results suggest that the future of AI-enabled healthcare may often involve carefully designed human–AI workflows rather than a simple choice between humans and full automation.
The article was published in Nature Communications, volume 16, article 2289, on March 7, 2025.