A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench

General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings. We evaluate VITA, a retrieval-augmented generation (RAG) system purpose-built for contextual knowledge retrieval in India and other low- and middle-income (LMIC) settings. VITA retrieves from a curated corpus of disease-specific guidelines,

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ethics.ai (12 August 2026), “A corpus-specific clinical RAG system matches or outperforms newer frontier LLMs on HealthBench,” evidence record 19034, https://ethics.ai/record/19034 (originally published by arXiv cs.AI).

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