Domain-Specific Language Models in Healthcare: A Framework for Trust, Governance, and Safe Deployment

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Authors: Preeti Tupsakhare
Cited by: 0

Abstract

General-purpose large language models (LLMs) have demonstrated significant potential in healthcare applications but remain ill-suited for high-stakes clinical environments due to limited domain awareness, lack of explainability, and opaque reasoning.This white paper introduces Domain-Specific Language Models (DSLMs) as a purpose-built alternative designed to meet the trust, safety, and regulatory demands of healthcare systems.It presents a comprehensive framework for the development, validation, and deployment of DSLMs, emphasizing data governance, model transparency, bias mitigation, and regulatory alignment.The paper outlines practical strategies for integrating DSLMs across clinical and administrative workflows while maintaining human oversight and patient safety.Collectively, this framework provides healthcare organizations with a scalable and responsible pathway for adopting AI in mission-critical settings.

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Explainable Artificial Intelligence (XAI) Ethics and Social Impacts of AI

Publication details

Journal / source
Journal of recent trends in computer science and engineering.
Volume
14
Issue
1
Pages
7–15
Publication date
9 February 2026
Language
EN