@inproceedings{pan-etal-2026-medicallm,
title = "{M}edica{LLM}: {LLM}-Driven Speech and Language Solutions for Healthcare",
author = "Pan, Ronghao and
Vivancos-Vicente, Pedro Jos{\'e} and
Castej{\'o}n-Garrido, Juan Salvador and
Bernal-Beltr{\'a}n, Tom{\'a}s and
Valencia-Garcia, Rafael",
editor = "Claramunt, German Rigau and
Gamallo, Pablo and
Mu{\~n}oz Guillena, Rafael and
Chiruzzo, Luis and
Mart{\'i}nez C{\'a}mara, Eugenio",
booktitle = "Proceedings of {LANLP}: Bridging {I}bero and {L}atin {A}merican {NLP} Communities",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.lanlp-1.5/",
doi = "10.63317/56qqyqyb2shn",
pages = "29--37",
abstract = "Although healthcare documentation is increasingly dependent on speech-based clinical interactions, general-purpose Automatic Speech Recognition (ASR) and Large Language Models (LLMs) lack the domain adaptation, structured control and interoperability guarantees required in regulated medical environments. These limitations often result in transcription errors, hallucinated content, and limited alignment with standardized coding systems. This paper introduces MedicaLLM, a multilingual, end-to-end framework integrating domain-adapted ASR, LLM-based structured report generation, and ontology-driven semantic enrichment within a modular architecture for clinical documentation. MedicaLLM combines medical interview transcription with structured report generation, summarization, and error correction; Named Entity Recognition (NER); and Medical Entity Linking (MEL) to align with standards such as SNOMED-CT and ICD-10. Deployed as a secure software as a service (SaaS) platform with REST API integration, MedicaLLM aims to reduce the administrative burden, improve the quality of documentation, and enhance semantic interoperability across healthcare systems, all while maintaining computational efficiency and clinical reliability."
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<abstract>Although healthcare documentation is increasingly dependent on speech-based clinical interactions, general-purpose Automatic Speech Recognition (ASR) and Large Language Models (LLMs) lack the domain adaptation, structured control and interoperability guarantees required in regulated medical environments. These limitations often result in transcription errors, hallucinated content, and limited alignment with standardized coding systems. This paper introduces MedicaLLM, a multilingual, end-to-end framework integrating domain-adapted ASR, LLM-based structured report generation, and ontology-driven semantic enrichment within a modular architecture for clinical documentation. MedicaLLM combines medical interview transcription with structured report generation, summarization, and error correction; Named Entity Recognition (NER); and Medical Entity Linking (MEL) to align with standards such as SNOMED-CT and ICD-10. Deployed as a secure software as a service (SaaS) platform with REST API integration, MedicaLLM aims to reduce the administrative burden, improve the quality of documentation, and enhance semantic interoperability across healthcare systems, all while maintaining computational efficiency and clinical reliability.</abstract>
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%0 Conference Proceedings
%T MedicaLLM: LLM-Driven Speech and Language Solutions for Healthcare
%A Pan, Ronghao
%A Vivancos-Vicente, Pedro José
%A Castejón-Garrido, Juan Salvador
%A Bernal-Beltrán, Tomás
%A Valencia-Garcia, Rafael
%Y Claramunt, German Rigau
%Y Gamallo, Pablo
%Y Muñoz Guillena, Rafael
%Y Chiruzzo, Luis
%Y Martínez Cámara, Eugenio
%S Proceedings of LANLP: Bridging Ibero and Latin American NLP Communities
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F pan-etal-2026-medicallm
%X Although healthcare documentation is increasingly dependent on speech-based clinical interactions, general-purpose Automatic Speech Recognition (ASR) and Large Language Models (LLMs) lack the domain adaptation, structured control and interoperability guarantees required in regulated medical environments. These limitations often result in transcription errors, hallucinated content, and limited alignment with standardized coding systems. This paper introduces MedicaLLM, a multilingual, end-to-end framework integrating domain-adapted ASR, LLM-based structured report generation, and ontology-driven semantic enrichment within a modular architecture for clinical documentation. MedicaLLM combines medical interview transcription with structured report generation, summarization, and error correction; Named Entity Recognition (NER); and Medical Entity Linking (MEL) to align with standards such as SNOMED-CT and ICD-10. Deployed as a secure software as a service (SaaS) platform with REST API integration, MedicaLLM aims to reduce the administrative burden, improve the quality of documentation, and enhance semantic interoperability across healthcare systems, all while maintaining computational efficiency and clinical reliability.
%R 10.63317/56qqyqyb2shn
%U https://aclanthology.org/2026.lanlp-1.5/
%U https://doi.org/10.63317/56qqyqyb2shn
%P 29-37
Markdown (Informal)
[MedicaLLM: LLM-Driven Speech and Language Solutions for Healthcare](https://aclanthology.org/2026.lanlp-1.5/) (Pan et al., LANLP 2026)
ACL
- Ronghao Pan, Pedro José Vivancos-Vicente, Juan Salvador Castejón-Garrido, Tomás Bernal-Beltrán, and Rafael Valencia-Garcia. 2026. MedicaLLM: LLM-Driven Speech and Language Solutions for Healthcare. In Proceedings of LANLP: Bridging Ibero and Latin American NLP Communities, pages 29–37, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).