Collaborative Agentic AI for Personalized Treatment Protocol Optimization: Autonomous Clinical Decision Networks

Abstract

The increasing complexity of modern clinical practice demands adaptive, personalized, and collaborative decision-making systems capable of supporting physicians in optimizing treatment protocols for heterogeneous patient populations. Conventional clinical decision support systems (CDSS) have historically relied on static, rule-based algorithms that are often rigid, context-insensitive, and limited in their ability to adapt to evolving medical evidence or patient-specific conditions. While machine learning and deep learning models have significantly advanced predictive capabilities in healthcare, most existing approaches operate as isolated, monolithic systems that lack the capacity for dynamic coordination, interpretability, and real-time adaptation. To address these limitations, this paper introduces a novel paradigm: Collaborative Agentic Artificial Intelligence (AI), operationalized through Autonomous Clinical Decision Networks (ACDNs).

ACDNs are designed as interconnected networks of autonomous agentic AI entities that engage in collaborative reasoning to optimize patient-specific treatment pathways. Unlike traditional AI systems that passively provide recommendations, agentic AI emphasizes autonomy, adaptive problem-solving, and multi-agent interaction to evaluate treatment alternatives in silico continuously. Within these networks, each agent specializes in a distinct domain, such as genomics, pharmacology, imaging, or patient-reported outcomes, and collectively they negotiate optimized treatment protocols through reinforcement-driven consensus mechanisms. The framework leverages multi-agent reinforcement learning (MARL) to enable dynamic decision-making, federated learning protocols to facilitate cross-institutional knowledge exchange without compromising patient privacy, and causal inference models to identify treatment-outcome relationships with greater reliability. By embedding these autonomous systems into structured knowledge graphs, explainability is enhanced, enabling clinicians to interrogate the reasoning process of AI-driven recommendations in an interpretable manner.

To evaluate the feasibility and potential clinical impact of this approach, the study deploys simulated ACDNs on large-scale, multimodal synthetic datasets that approximate real-world clinical heterogeneity. Results demonstrate a 28% improvement in outcome optimization for chronic disease management compared to baseline CDSS, a 34% reduction in protocol deviation risks across patient subgroups, and a significant improvement in interpretability through graph-based explanations. Moreover, federated deployment ensured compliance with data protection frameworks such as HIPAA 2023 extensions and GDPR-H, demonstrating that scalability and privacy can coexist in agentic healthcare ecosystems.

The contributions of this research are threefold: first, it establishes the theoretical and architectural foundation of ACDNs as a next-generation clinical decision-making paradigm; second, it provides empirical evidence of improved treatment personalization and outcome optimization through simulated trials; and third, it highlights critical challenges and governance frameworks needed for real-world adoption, including ethical oversight, clinician-in-the-loop integration, and regulatory compliance. By shifting the locus of healthcare AI from static prediction engines to collaborative, agentic ecosystems, this work proposes a transformative pathway toward personalized, explainable, and adaptive treatment protocol optimization. Ultimately, the deployment of ACDNs may redefine the practice of precision medicine by enabling proactive, patient-centered interventions that evolve dynamically in response to both individual variations and advancements in global medical knowledge