The rapid growth of healthcare data and increasing demand for intelligent and sustainable digital healthcare systems create significant challenges in computational efficiency, resource management, and decision-making.
Our research, “Synergetic Intelligence: A Multi-Agent Learning Framework for Sustainable Healthcare Computing,” investigates how Multi-Agent Learning can address these challenges by enabling multiple intelligent agents to interact, learn, collaborate, and contribute to decentralized decision-making.
The proposed approach emphasizes collaborative intelligence, where autonomous agents can work together rather than relying entirely on a single centralized intelligent system. This creates opportunities to develop healthcare computing environments that are more adaptive, scalable, efficient, and capable of responding to changing conditions.
A major focus of the research is the integration of Artificial Intelligence, multi-agent learning, sustainable computing, and data-driven healthcare decision-making. By improving coordination between intelligent agents and optimizing computational resources, such systems have the potential to support smarter healthcare operations while reducing unnecessary computational overhead.
The research was published at the 4th International Conference on ICT for Digital, Smart, and Sustainable Development, held at Jamia Hamdard University, New Delhi, on April 23–24.
This work represents an exploration of how collaborative AI systems can contribute toward the next generation of intelligent, efficient, and sustainable healthcare computing solutions.