An Integrated Systems Thinking-Artificial Intelligence Approach to Advancing Biology and Biomedicine
The complexity of living systems, ranging from cellular networks to organisms to ecosystems, poses a significant challenge to the progress of contemporary biology and biomedicine. Living organisms are complex adaptive systems that are characterized by nonlinear interactions, causal-dynamic feedback loops, and context-dependent behaviors that give rise to emergent properties that are not completely explainable or predictable from the properties of the individual components alone. Investigating and understanding systemic complexity is therefore essential for advancing biology and biomedicine. Recently, artificial intelligence (AI) has been developed for analyzing large-scale, high-dimensional datasets generated by experimental and clinical research. AI enables the identification of correlations, causal patterns, and predictive relationships, which are difficult to discern using traditional analytical approaches. However, without an overarching framework, it risks being applied in fragmented or purely data-driven ways. Systems thinking (ST) provides a framework by emphasizing holism, interconnections, and dynamic behaviors across multiple organizational scales. By integrating ST and AI, researchers can creatively and effectively investigate living systems, ensuring that computational insights are meaningful and contextually grounded. An integrated ST–AI approach is proposed as a guiding framework for twenty-first century biology and biomedicine.