Game Theoretical AI for Precision Medicine

Siyuan Guo; Dapeng Oliver Wu

doi:10.53941/tai.2025.100011

Abstract

Current clinical decision-making relies heavily on doctors’ experience, often leading to generic treatments that overlook individual patient factors. A lack of clinical expertise, especially in resource-limited regions, hinders optimal decisions and contributes to higher patient mortality. To address this, traditional AI systems have modeled clinical decision-making as a predator-prey game. However, such approaches fail to recognize that disease agents, such as cancer cells, can exhibit adaptive, human-like intelligence. Immunological studies reveal that malignant tumors evade the immune system through camouflage, coercion, and cytoprotection. To counter these adaptive strategies, game-theoretic approaches are essential. In this paper, we present Game Theoretical AI (GTAI)—a novel approach that formalizes and automates strategic reasoning to enhance clinical decision-making against complex diseases. Inspired by Sun Tzu's The Art of War and the Thirty-Six Stratagems, GTAI mimics expert clinical reasoning through four stages: (1) observation and diagnosis, (2) treatment planning, (3) execution, and (4) outcome evaluation. Within this framework, GTAI can dynamically select and carry out high-level tactics analogous to humans' stratagems at each decision stage. This unified approach yields six major discoveries that bridge theory and practice. Collectively, these advances demonstrate the power of integrating strategic intelligence with computational models, opening new avenues for the application of AI in precision medicine and adaptive clinical practice.

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