A CrewAI-based multi-agent system with human oversight built financial models and carried out model risk management checks on three public credit datasets, with results comparable to AutoML and Kaggle baselines.
Advancing Healthcare Automation: Multi-Agent System for Medical Necessity Justification
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Prior Authorization delivers safe, appropriate, and cost-effective care that is medically justified with evidence-based guidelines. However, the process often requires labor-intensive manual comparisons between patient medical records and clinical guidelines, that is both repetitive and time-consuming. Recent developments in Large Language Models (LLMs) have shown potential in addressing complex medical NLP tasks with minimal supervision. This paper explores the application of Multi-Agent System (MAS) that utilize specialized LLM agents to automate Prior Authorization task by breaking them down into simpler and manageable sub-tasks. Our study systematically investigates the effects of various prompting strategies on these agents and benchmarks the performance of different LLMs. We demonstrate that GPT-4 achieves an accuracy of 86.2% in predicting checklist item-level judgments with evidence, and 95.6% in determining overall checklist judgment. Additionally, we explore how these agents can contribute to explainability of steps taken in the process, thereby enhancing trust and transparency in the system.
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Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews
A CrewAI-based multi-agent system with human oversight built financial models and carried out model risk management checks on three public credit datasets, with results comparable to AutoML and Kaggle baselines.