MIRIAD: Augmenting LLMs with millions of medical query-response pairs
LLMs are bound to transform healthcare with advanced decision support and
flexible chat assistants. However, LLMs are prone to generate inaccurate
medical content. To ground LLMs in high-quality medical knowledge, LLMs have
been equipped with external knowledge via RAG, where unstructured medical
knowledge is split into small text chunks that can be selectively retrieved and
integrated into the LLMs context. Yet, existing RAG pipelines rely on raw,
unstructured medical text, which can be noisy, uncurated and difficult for LLMs
to effectively leverage. Systematic approaches to organize medical knowledge to
best surface it to LLMs are generally lacking. To address these challenges, we
introduce MIRIAD, a large-scale, curated corpus of 5,821,948 medical QA pairs,
each rephrased from and grounded in a passage from peer-reviewed medical
literature using a semi-automated pipeline combining LLM generation, filtering,
grounding, and human annotation. Unlike prior medical corpora, which rely on
unstructured text, MIRIAD encapsulates web-scale medical knowledge in an
operationalized query-response format, which enables more targeted retrieval.
Experiments on challenging medical QA benchmarks show that augmenting LLMs with
MIRIAD improves accuracy up to 6.7% compared to unstructured RAG baselines with
the same source corpus and with the same amount of retrieved text. Moreover,
MIRIAD improved the ability of LLMs to detect medical hallucinations by 22.5 to
37% (increase in F1 score). We further introduce MIRIAD-Atlas, an interactive
map of MIRIAD spanning 56 medical disciplines, enabling clinical users to
visually explore, search, and refine medical knowledge. MIRIAD promises to
unlock a wealth of down-stream applications, including medical information
retrievers, enhanced RAG applications, and knowledge-grounded chat interfaces,
which ultimately enables more reliable LLM applications in healthcare.
68473f1b3ec10bdd8ab4dce1
Qinyue Zheng
claimed_verified
2025-06-10T09:20:33.579Z
6564f4e2e09d65766856d741
/avatars/8e6b9a31898480ec22994d1020bca623.svg
Qinyue
user
Queeey
68473f1b3ec10bdd8ab4dce2
Salman Abdullah
claimed_verified
2025-06-10T09:20:35.607Z
646a8ef48dfd6ff79b6c33d2
https://cdn-avatars.huggingface.co/v1/production/uploads/646a8ef48dfd6ff79b6c33d2/Od83-X9bIwDo5MEirKX3f.jpeg
Salman Abdullah
user
salman-abdullah
68473f1b3ec10bdd8ab4dce3
Sam Rawal
68473f1b3ec10bdd8ab4dce4
Cyril Zakka
claimed_verified
2025-06-10T15:20:26.022Z
66ba71a4447411b9c0e19d71
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/4f93ZrYdaKfK3F53IB51x.jpeg
Cyril
user
cyrilzakka
68473f1b3ec10bdd8ab4dce5
Sophie Ostmeier
68473f1b3ec10bdd8ab4dce6
Maximilian Purk
claimed_verified
2025-07-10T09:12:59.965Z
65ddd65e3ae02a216b5b62b6
/avatars/8ed87f6ce77b6291f7b4aa2ada1c605e.svg
Maximilian Purk
user
maxpurk
68473f1b3ec10bdd8ab4dce7
Eduardo Reis
68473f1b3ec10bdd8ab4dce8
Eric J. Topol
68473f1b3ec10bdd8ab4dce9
Jure Leskovec
68473f1b3ec10bdd8ab4dcea
Michael Moor
claimed_verified
2025-06-17T21:13:34.775Z
6438d1d843d932c462404500
https://cdn-avatars.huggingface.co/v1/production/uploads/6438d1d843d932c462404500/6UrtJbed9N5ETbImgIh-C.png
Michael Moor
user
mdmoor
MIRIAD: Augmenting LLMs with millions of medical query-response pairs
LLMs are bound to transform healthcare with advanced decision support and
flexible chat assistants. However, LLMs are prone to generate inaccurate
medical content. To ground LLMs in high-quality medical knowledge, LLMs have
been equipped with external knowledge via RAG, where unstructured medical
knowledge is split into small text chunks that can be selectively retrieved and
integrated into the LLMs context. Yet, existing RAG pipelines rely on raw,
unstructured medical text, which can be noisy, uncurated and difficult for LLMs
to effectively leverage. Systematic approaches to organize medical knowledge to
best surface it to LLMs are generally lacking. To address these challenges, we
introduce MIRIAD, a large-scale, curated corpus of 5,821,948 medical QA pairs,
each rephrased from and grounded in a passage from peer-reviewed medical
literature using a semi-automated pipeline combining LLM generation, filtering,
grounding, and human annotation. Unlike prior medical corpora, which rely on
unstructured text, MIRIAD encapsulates web-scale medical knowledge in an
operationalized query-response format, which enables more targeted retrieval.
Experiments on challenging medical QA benchmarks show that augmenting LLMs with
MIRIAD improves accuracy up to 6.7% compared to unstructured RAG baselines with
the same source corpus and with the same amount of retrieved text. Moreover,
MIRIAD improved the ability of LLMs to detect medical hallucinations by 22.5 to
37% (increase in F1 score). We further introduce MIRIAD-Atlas, an interactive
map of MIRIAD spanning 56 medical disciplines, enabling clinical users to
visually explore, search, and refine medical knowledge. MIRIAD promises to
unlock a wealth of down-stream applications, including medical information
retrievers, enhanced RAG applications, and knowledge-grounded chat interfaces,
which ultimately enables more reliable LLM applications in healthcare.
MIRIAD, a large-scale, curated medical QA corpus, enhances LLM accuracy and hallucination detection in healthcare applications.
LLMs RAG medical QA pairs semi-automated pipeline peer-reviewed literature MIRIAD-Atlas medical hallucinations F1 score medical information retrievers knowledge-grounded chat interfaces