Year 2025 / Volume 117 / Number 6
Letter
Domain-specific language models: innovation with inherent risks

357-358

DOI: 10.17235/reed.2025.11224/2025

Julio Mayol, Marcos Gámez Alastuey, Rocío Anula Fernández,

Abstract
There are potential advantages of domain-specific solutions, built by fine-tuning pretrained LLMs with healthcare data, but this approach has certain drawbacks: privacy, biases and accuracy.
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References
1. Nevado Santos M, Nevado Alvarez P. The Future of AI in Healthcare: Smaller, More Specialized Language Models. Rev Esp Enferm Dig. 2025.
2. Mayol J, Gámez Alastuey M, Anula Fernández R. Redefining healthcare - The transformative power of generative AI in modern medicine. Rev Esp Enferm Dig. 2025 Feb 3. doi: 10.17235/reed.2025.11081/2024. Epub ahead of print. PMID: 39898717.
3. Anisuzzaman DM, Malins JG, Friedman PA, Attia ZI. Fine-Tuning Large Language Models for Specialized Use Cases. Mayo Clin Proc Digit Health [Internet]. 2025 Mar 1 [cited 2025 Feb 8];3(1):100184. Available from: https://www.sciencedirect.com/science/article/pii/S2949761224001147
4. Maitin Ana M, Nogales A, Fernández-Rincón S, Aranguren E, Cervera-Barba E, Denizon-Arranz S, Mateos-Rodríguez A, García-Tejedor AJ. Application of large language models in clinical record correction: a comprehensive study on various retraining methods, JAMIA 2025; 32:341–348, https://doi.org/10.1093/jamia/ocae302
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Mayol J, Gámez Alastuey M, Anula Fernández R. Domain-specific language models: innovation with inherent risks. 11224/2025


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Publication history

Received: 21/03/2025

Accepted: 27/03/2025

Online First: 08/04/2025

Published: 10/06/2025

Article Online First time: 18 days

Article editing time: 81 days


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