Generative Digital Twins and Intelligent Systems for Decision Support Across Industrial and Medical Domains

Abstract

Digital twins and intelligent systems are increasingly employed across engineering and medicine, yet existing applications remain largely domain-specific, focusing on predictive accuracy while neglecting broader decision-support functions. This paper proposes a conceptual framework that repositions digital twins as generative platforms for counterfactual reasoning, enabling systematic exploration of alternative scenarios in complex systems. The proposed architecture integrates multi-modal data fusion, hybrid mechanistic-data-driven modeling, Bayesian uncertainty quantification, a counterfactual inference engine, and an interactive visualization interface. Two use cases are examined: predictive maintenance of aircraft engines and adaptive treatment planning for solid tumors. Results suggest that the primary value of digital twins lies not in superior prediction but in expanding the space of considered alternatives, thereby supporting more robust decision-making under uncertainty. Persistent challenges include the tension between model generalizability and local fidelity, and the difficulty of validating counterfactual outcomes in non-stationary environments. The paper articulates design principles and open questions for future research, emphasizing the need for methodological pluralism and attention to the ethical dimensions of algorithmically-generated possibilities.