Neura: a specialized large language model solution in neurology
Author:
Barrit SamiORCID, Torcida Nathan, Mazeraud Aurélien, Boulogne Sébastien, Benoit Jeanne, Carette Timothée, Carron Thibault, Delsaut Bertil, Diab Eva, Kermorvant Hugo, Maarouf Adil, Slootjes Sofia Maldonado, Redon Sylvain, Robin Alexis, Hadidane Sofiène, Harlay Vincent, Tota Vito, Madec Tanguy, Niset Alexandre, El Hadwe Salim, Massager Nicolas, Lagarde Stanislas, Carron Romain
Abstract
AbstractLarge language models’ (LLM) ability in natural language processing holds promise for diverse applications, yet their deployment in fields such as neurology faces domain-specific challenges. Hence, we introduce Neura: a scalable, explainable solution to specialize LLM. Blindly evaluated on a select set of five complex clinical cases compared to a cohort of 13 neurologists, Neura achieved normalized scores of 86.17% overall, 85% for differential diagnoses, and 88.24% for final diagnoses (55.11%, 46.15%, and 70.93% for neurologists) with rapid response times of 28.8 and 19 seconds (9 minutes and 37.2 seconds and 8 minutes and 51 seconds for neurologists) while consistently providing relevant, accurately cited information. These findings support the emerging role of LLM-driven applications to articulate human-acquired and integrated data with a vast corpus of knowledge, augmenting human experiential reasoning for clinical and research purposes.
Publisher
Cold Spring Harbor Laboratory
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