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

TL;DR

KOM is a multi-agent AI system designed to automate and personalize the management of knee osteoarthritis, improving efficiency and treatment quality in clinical settings.

Contribution

This paper introduces KOM, a novel multi-agent system that automates KOA evaluation, risk prediction, and treatment planning, demonstrating superior performance over large language models.

Findings

KOM outperforms general-purpose language models in imaging and prescription tasks.

Collaboration with KOM reduces diagnostic and planning time by 38.5%.

KOM improves treatment quality in simulated clinical scenarios.

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.