Guardian AI: A Federated Multi-Agent Framework for Medical Error Prevention - Systematic Evidence Synthesis and Research Architecture

Abstract

Background: Medical errors constitute the third leading cause of death globally, with current patient safety monitoring achieving 30-50% accuracy rates and detecting adverse events 48-72 hours post-occurrence.

Objectives: This research addressed fundamental architectural limitations in existing autonomous multi-agent systems for medical error prevention through systematic evidence synthesis and development of a federated multi-agent framework. The investigation analyzed clinical performance metrics and economic outcomes of contemporary systems and designed collaborative intelligence networks for transforming healthcare safety outcomes.

Methods: We conducted systematic review methodology following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines across seven databases spanning 2015-2025. The search strategy employed controlled vocabulary terms combining artificial intelligence, machine learning, and multi-agent systems with patient safety domains. Study selection followed rigorous two-stage screening by three independent reviewers requiring multi-agent systems investigation with quantitative performance metrics reporting. The Guardian AI framework employed mathematical problem formulation as multi-agent collaborative intelligence network with five specialized safety agents: Medication Safety, Clinical Deterioration, Surgical Safety, Infection Prevention, and Resource Optimization Agents utilizing advanced machine learning techniques. The system implemented Byzantine fault-tolerant consensus mechanisms requiring two-thirds plus one agent agreement before executing critical interventions. Federated learning infrastructure employed differential privacy with secure multi-party computation enabling cross-institutional model training while maintaining regulatory compliance.

Results: Systematic review analysis encompassed 45 studies representing over 340,000 patients across 15 countries. Current multi-agent architecture achieved 81.2% accuracy rates, improving over single-agent benchmarks of 65-70%. Optimal prediction windows of 4-24 hours achieved sensitivity exceeding 85% and specificity approaching 97%, with sepsis detection maintaining 88.19-97.05% sensitivity and 96.75% specificity while achieving 3.18% false alarm rates. Economic analysis revealed break-even costs of $14.59 per day with implementations demonstrating $99,984,542 annual cost savings. Guardian AI projections indicate 75% reduction in preventable adverse events, Area Under Receiver Operating Characteristic values exceeding 0.97, and 80% false alarm reduction. Economic modeling demonstrates 336% return on investment within 18 months, generating $32.5 million annual savings per 300-bed hospital.

Conclusions: Existing multi-agent patient safety systems operate as loosely coupled agents rather than collaborative intelligence networks, constraining clinical decision-making effectiveness. The Guardian AI framework introduces algorithmic innovations through Byzantine fault-tolerant consensus mechanisms optimized for medical applications and federated learning protocols enabling privacy-preserving cross-institutional knowledge sharing. National deployment across 6,090 United States hospitals would require $56 billion investment but generate $348 billion annual savings while preventing 187,500 deaths annually. The framework establishes new paradigms for collaborative medical artificial intelligence systems through standardized evaluation protocols.