Small Agent Group is the Future of Digital Health

TL;DR

This paper proposes Small Agent Groups (SAG) as a collaborative approach to digital health, demonstrating that SAG can outperform large models in clinical reasoning, reliability, and cost-efficiency.

Contribution

It introduces the SAG framework that shifts from monolithic models to collaborative reasoning, improving clinical decision-making and deployment in digital health.

Findings

SAG outperforms single large models in clinical metrics.

SAG maintains high effectiveness with lower deployment costs.

Collaborative reasoning enhances reliability in clinical AI.

Abstract

The rapid adoption of large language models (LLMs) in digital health has been driven by a "scaling-first" philosophy, i.e., the assumption that clinical intelligence increases with model size and data. However, real-world clinical needs include not only effectiveness, but also reliability and reasonable deployment cost. Since clinical decision-making is inherently collaborative, we challenge the monolithic scaling paradigm and ask whether a Small Agent Group (SAG) can support better clinical reasoning. SAG shifts from single-model intelligence to collective expertise by distributing reasoning, evidence-based analysis, and critical audit through a collaborative deliberation process. To assess the clinical utility of SAG, we conduct extensive evaluations using diverse clinical metrics spanning effectiveness, reliability, and deployment cost. Our results show that SAG achieves superior performance compared to a single giant model, both with and without additional optimization or retrieval-augmented generation. These findings suggest that the synergistic reasoning represented by SAG can substitute for model parameter growth in clinical settings. Overall, SAG offers a scalable solution to digital health that better balances effectiveness, reliability, and deployment efficiency.