Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?
#clinical diagnosis #multi-agent LLMs #mixed-vendor #artificial intelligence #healthcare AI #model collaboration #diagnostic accuracy
📌 Key Takeaways
- The article examines whether combining LLMs from different vendors enhances clinical diagnostic accuracy.
- It explores multi-agent systems where diverse AI models collaborate on medical case analysis.
- Potential benefits include reduced bias and improved decision-making through varied model perspectives.
- The study likely compares performance against single-vendor or single-model approaches in clinical settings.
📖 Full Retelling
arXiv:2603.04421v1 Announce Type: cross
Abstract: Multi-agent large language model (LLM) systems have emerged as a promising approach for clinical diagnosis, leveraging collaboration among agents to refine medical reasoning. However, most existing frameworks rely on single-vendor teams (e.g., multiple agents from the same model family), which risk correlated failure modes that reinforce shared biases rather than correcting them. We investigate the impact of vendor diversity by comparing Single-
🏷️ Themes
AI Healthcare, LLM Collaboration
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Original Source
--> Computer Science > Computation and Language arXiv:2603.04421 [Submitted on 14 Feb 2026] Title: Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Chang Yuan , Xiaoman Zhang , Sung Eun Kim , Pranav Rajpurkar View a PDF of the paper titled Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?, by Grace Chang Yuan and 3 other authors View PDF HTML Abstract: Multi-agent large language model systems have emerged as a promising approach for clinical diagnosis, leveraging collaboration among agents to refine medical reasoning. However, most existing frameworks rely on single-vendor teams (e.g., multiple agents from the same model family), which risk correlated failure modes that reinforce shared biases rather than correcting them. We investigate the impact of vendor diversity by comparing Single-LLM, Single-Vendor, and Mixed-Vendor Multi-Agent Conversation frameworks. Using three doctor agents instantiated with o4-mini, Gemini-2.5-Pro, and Claude-4.5-Sonnet, we evaluate performance on RareBench and DiagnosisArena. Mixed-vendor configurations consistently outperform single-vendor counterparts, achieving state-of-the-art recall and accuracy. Overlap analysis reveals the underlying mechanism: mixed-vendor teams pool complementary inductive biases, surfacing correct diagnoses that individual models or homogeneous teams collectively miss. These results highlight vendor diversity as a key design principle for robust clinical diagnostic systems. Comments: Accepted as Oral at the EACL 2026 Workshop on Healthcare and Language Learning Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) Cite as: arXiv:2603.04421 [cs.CL] (or arXiv:2603.04421v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.04421 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Grace Chang Yuan [ view email ] [v1] Sat, 14 Feb 2026 18:42:58 UTC (1,279 KB) Full-text links: Access Paper: View a PDF of the paper titled ...
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