KOM: A Multi-Agent Artificial Intelligence System for Precision Management of Knee Osteoarthritis (KOA)

2025-11-24

AI Summary

Knee osteoarthritis (KOA) affects over 600 million people globally, necessitating cost-effective, high-accuracy personalized management. This study proposes a modular multi-agent AI system integrating medical image analysis, natural language processing, and personalized recommendation to enable end-to-end intelligent KOA management—automating radiographic assessment, progression risk prediction, and generation of contraindication-aware, individualized treatment plans. Unlike general-purpose large language models, our system achieves superior performance in medical image interpretation and prescription generation, significantly outperforming state-of-the-art LLMs on benchmark tasks. Simulated randomized controlled trials demonstrate that clinician–AI collaboration reduces clinical planning time by 38.5% while improving both clinical rationale and patient-specific alignment of treatment recommendations. The core innovation lies in a lightweight, interpretable, clinician–engineer co-designed multi-agent architecture tailored for resource-constrained settings.

Technology Category

• Multiagent Systems: Modeling other Agents
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Knee osteoarthritis (KOA) affects more than 600 million individuals globally and is associated with significant pain, functional impairment, and disability. While personalized multidisciplinary interventions have the potential to slow disease progression and enhance quality of life, they typically require substantial medical resources and expertise, making them difficult to implement in resource-limited settings. To address this challenge, we developed KOM, a multi-agent system designed to automate KOA evaluation, risk prediction, and treatment prescription. This system assists clinicians in performing essential tasks across the KOA care pathway and supports the generation of tailored management plans based on individual patient profiles, disease status, risk factors, and contraindications. In benchmark experiments, KOM demonstrated superior performance compared to several general-purpose large language models in imaging analysis and prescription generation. A randomized three-arm simulation study further revealed that collaboration between KOM and clinicians reduced total diagnostic and planning time by 38.5% and resulted in improved treatment quality compared to each approach used independently. These findings indicate that KOM could help facilitate automated KOA management and, when integrated into clinical workflows, has the potential to enhance care efficiency. The modular architecture of KOM may also offer valuable insights for developing AI-assisted management systems for other chronic conditions.

Problem

• Automating knee osteoarthritis evaluation and personalized treatment planning
• Reducing medical resource requirements in resource-limited clinical settings
• Enhancing diagnostic accuracy and efficiency through clinician-AI collaboration

Innovation

• Multi-agent system automates KOA evaluation and treatment
• Generates personalized plans using patient profiles and risks
• Modular architecture enables AI management for chronic diseases