Economics of AI and human task sharing for decision making in screening mammography

The rising global incidence of breast cancer and the persistent shortage of specialized radiologists have heightened the demand for innovative solutions in mammography screening. Artificial intelligence (AI) has emerged as a promising tool to bridge this demand-supply gap, with potential applications ranging from full automation to integrated AI-human decision-making. This study evaluates the economic feasibility of incorporating artificial intelligence (AI) into mammography screening within healthcare settings, considering full or partial integration. To evaluate the economic viability, we employ an optimization model specifically designed to minimize mammography screening costs. This model considers three distinct approaches when interpreting mammograms: automation strategy utilizing AI exclusively, delegation strategy involving the selective allocation of tasks between radiologists and AI, and the expert-alone strategy relying solely on radiologist decisions. Our findings underscore the significance of disease prevalence in relation to the trade-off between costs associated with false positives (e.g., follow-up expenses) and false negatives (e.g., litigation costs stemming from missed diagnoses) in shaping the AI strategy for healthcare organizations. We backtest our approach using data from an AI contest in which participants aimed to match or surpass radiologists' performance in assessing screening mammograms for women. The contest data supports the optimality of the delegation strategy, potentially leading to cost savings of 17.5% to 30.1% compared to relying solely on human experts. Our research provides guidance for healthcare organizations considering AI integration in mammography screening, with broader implications for work design and human-AI hybrid solutions in various fields.