Neura: a specialized large language model solution in neurology
Barrit, S.; Torcida, N.; Mazeraud, A.; Boulogne, S.; Benoit, J.; Carette, T.; Carron, T.; Delsaut, B.; Diab, E.; Kermorvant, H.; Maarouf, A.; Maldonado Slootjes, S.; Redon, S.; Robin, A.; Hadidane, S.; Harlay, V.; Tota, V.; Madec, T.; Niset, A.; El Hadwe, S.; Massager, N.; Lagarde, S.; Carron, R.
Show abstract
Large 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.
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