The Radiology Sparring Partner: A PoC Study of Multi-Agentic Workflows for Clinical Decision Support and Teaching

Abstract Integrating Large Language Models (LLMs) into radiology is often hindered by the inability of monolithic architectures to balance clinical reasoning with pedagogical safety. This study evaluates MedGemma-27B-IT across two specialized agentic frameworks: a clinical "Radiology Sparring Partner" and a "Socratic Teacher". For clinical decision support, we compared three architectures: Raw (base model output, no prompt), Minimal Prompted (single-shot prompt), and Multi-Agentic, using a 4-stage pipeline for clinical synthesis. Separately, the educational track employs a dual-agent architecture for progressive educational scaffolding. A pooled evaluation of 120 interaction transcripts (10 per architecture with three independent LLM-as-a-judge runs) was finalized using an expert-anchored combined mean approach, anchored via Bühlmann credibility calibration against radiologist-authored references (5 per architecture). Results demonstrate that agentic orchestration outperforms monolithic baselines in high-level reasoning. Most notably, Pathophysiological Logic scores rose from 3.20 (Raw) to 4.14 (Agentic) on a 5-point Likert scale, with a simultaneous reduction in standard deviation from 1.23 to 0.70. While this Proof of Concept (PoC) highlights the advantages of modularity, results remain subject to evaluator formulation bias and a measurable "latency tax" inherent in sequential agent hand-offs. Future work will focus on longitudinal clinical validation and optimization of cached memory architectures to refine reasoning depth while mitigating latency.