Small Agent Group is the Future of Digital Health
For digital health practitioners, this work offers a more balanced approach to clinical AI that prioritizes reliability and cost-efficiency over raw model size.
The paper challenges the scaling-first paradigm in digital health by proposing a Small Agent Group (SAG) that distributes reasoning and evidence-based analysis among multiple small models. SAG outperforms a single giant model across clinical metrics including effectiveness, reliability, and deployment cost, suggesting that collaborative reasoning can substitute for model size growth.
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.