Medical question answering systems face deployment challenges including hallucinations, bias, computational demands, privacy concerns, and the need for specialized expertise across diverse domains. Here, we present SOLVE-Med, a multi-agent architecture combining domain-specialized small language models for complex medical queries. The system employs a Router Agent for dynamic specialist selection, ten specialized models (1B parameters each) fine-tuned on specific medical domains, and an Orchestrator Agent that synthesizes responses. Evaluated on Italian medical forum data across ten specialties, SOLVE-Med achieves superior performance with ROUGE-1 of 0.301 and BERTScore F1 of 0.697, outperforming standalone models up to 14B parameters while enabling local deployment. Our code is publicly available on GitHub: https://github.com/PRAISELab-PicusLab/SOLVE-Med.

SOLVE-Med: Specialized Orchestration for Leading Vertical Experts Across Medical Specialties / Di Marino, R.; Dioguardi, G.; Romano, A.; Riccio, G.; Barone, M.; Postiglione, M.; Amato, F.; Moscato, V.. - 413:(2025), pp. 5135-5138. ( 28th European Conference on Artificial Intelligence, ECAI 2025, including 14th Conference on Prestigious Applications of Intelligent Systems, PAIS 2025 Bologna, Italia 25-30 October 2025) [10.3233/FAIA251438].

SOLVE-Med: Specialized Orchestration for Leading Vertical Experts Across Medical Specialties

Barone M.;Postiglione M.;Amato F.;Moscato V.
2025

Abstract

Medical question answering systems face deployment challenges including hallucinations, bias, computational demands, privacy concerns, and the need for specialized expertise across diverse domains. Here, we present SOLVE-Med, a multi-agent architecture combining domain-specialized small language models for complex medical queries. The system employs a Router Agent for dynamic specialist selection, ten specialized models (1B parameters each) fine-tuned on specific medical domains, and an Orchestrator Agent that synthesizes responses. Evaluated on Italian medical forum data across ten specialties, SOLVE-Med achieves superior performance with ROUGE-1 of 0.301 and BERTScore F1 of 0.697, outperforming standalone models up to 14B parameters while enabling local deployment. Our code is publicly available on GitHub: https://github.com/PRAISELab-PicusLab/SOLVE-Med.
2025
9781643686318
SOLVE-Med: Specialized Orchestration for Leading Vertical Experts Across Medical Specialties / Di Marino, R.; Dioguardi, G.; Romano, A.; Riccio, G.; Barone, M.; Postiglione, M.; Amato, F.; Moscato, V.. - 413:(2025), pp. 5135-5138. ( 28th European Conference on Artificial Intelligence, ECAI 2025, including 14th Conference on Prestigious Applications of Intelligent Systems, PAIS 2025 Bologna, Italia 25-30 October 2025) [10.3233/FAIA251438].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/1044955
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