What is the role of human decisions in a world of artificial intelligence: an economic evaluation of human-AI collaboration in diabetic retinopathy screening

2025-03-26

AI Summary

While AI’s role in healthcare is expanding, the health economic value of human-AI collaboration remains poorly quantified. Method: Grounded in health economics, this study systematically evaluated 270 diabetic retinopathy screening scenarios, comparing cost-effectiveness of eight human-AI collaborative strategies against manual screening alone. We developed and validated a novel “co-pilot” paradigm—referral initiated only upon consensus between clinician and AI—and integrated real-world data simulation, decision-analytic modeling, and cost-utility analysis (CUA). Contribution/Results: The co-pilot strategy generated an additional $4.64 million in health benefits per 100,000 individuals compared to manual screening, representing the sole approach delivering both statistically significant health gains and optimal cost-effectiveness (ICER below willingness-to-pay thresholds). This provides generalizable, evidence-based guidance for clinically viable AI implementation and establishes a scalable human-AI collaboration framework with robust economic justification.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

As Artificial intelligence (AI) has been increasingly integrated into the medical field, the role of humans may become vague. While numerous studies highlight AI's potential, how humans and AI collaborate to maximize the combined clinical benefits remains unexplored. In this work, we analyze 270 screening scenarios from a health-economic perspective in a national diabetic retinopathy screening program, involving eight human-AI collaborative strategies and traditional manual screening. We find that annual copilot human-AI screening in the 20-79 age group, with referral decisions made when both humans and AI agree, is the most cost-effective strategy for human-AI collaboration. The 'copilot' strategy brings health benefits equivalent to USD 4.64 million per 100,000 population compared to manual screening. These findings demonstrate that even in settings where AI is highly mature and efficient, human involvement remains essential to ensuring both health and economic benefits. Our findings highlight the need to optimize human-AI collaboration strategies for AI implementation into healthcare systems.

Problem

• Evaluating cost-effective human-AI collaboration in diabetic retinopathy screening
• Determining optimal human-AI decision-making for maximum clinical benefits
• Assessing economic and health impacts of human involvement in AI-driven healthcare

Innovation

• Copilot human-AI screening strategy
• Cost-effective referral decision agreement
• Optimized human-AI healthcare collaboration