Precision in Practice: Enhancing Healthcare with Domain-Specific Language Models
DOI:
https://doi.org/10.5281/zenodo.13253336Keywords:
domain-specific language models (dslms), medical information dissemination, telehealth integrationAbstract
This paper investigates the role of domain-specific language models (DSLMs) in enhancing the accuracy and reliability of responses to general medical inquiries. Focusing on the healthcare sector, we explore how LLMs, when fine-tuned with specialized medical datasets, surpass general language models in handling complex medical terminology and ensuring patient data confidentiality. The benefits of these models include increased precision in medical advice, a reduction in misinformation risks, and tailored responses based on individual patient histories. Nonetheless, implementing these technologies comes with significant ethical, technical, and regulatory challenges, such as ensuring patient privacy, maintaining up-to-date medical knowledge, and navigating stringent compliance requirements. The paper concludes by discussing future directions, including the integration of DSLMs with telehealth services and ongoing advancements in model training techniques, underscoring the necessity for continued research, widespread adoption, and rigorous evaluation to fully leverage their potential in improving healthcare outcomes.
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Copyright (c) 2024 Hao Qin, Zhi Li
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