Will machines take over? Algorithms for human–machine collaborative decision making in healthcare
Published:
Our paper, “Will machines take over? Algorithms for human–machine collaborative decision making in healthcare,” was published online in Production and Operations Management on April 3, 2026. The paper asks a central question for organizations adopting AI: when should a machine make a decision on its own, and when should an AI system work together with a human expert?
We develop a diagnostic system for healthcare that allocates mammography interpretation tasks between AI algorithms and radiologists. Rather than viewing the choice as a simple comparison between full automation and human-only decision making, the framework considers a delegation strategy in which AI evaluates cases, makes recommendations for low- and high-risk cases, and sends ambiguous cases to radiologists for additional assessment.
A key result is an analytically derived two-threshold policy. When AI performance does not exceed that of radiologists, the system can recommend no follow-up for low-risk cases, recommend follow-up for high-risk cases, and delegate cases in an intermediate risk range to human experts. The optimal thresholds depend on the economics and safety tradeoffs of the diagnostic system rather than on the system’s prior history.
Backtesting with data from a mammography AI contest and real-world cost and performance measures shows potential cost savings of up to 20.9% compared with an expert-alone approach. More broadly, the paper argues that the important organizational question is not whether machines will simply replace people, but how decision-making tasks should be divided between humans and machines to achieve better outcomes at sustainable cost.
The article is an advance online publication in Production and Operations Management.