As digital healthcare evolves, the security of electronic health records
(EHR) becomes increasingly crucial. This study presents the GPT-Onto-CAABAC
framework, integrating Generative Pretrained Transformer (GPT), medical-legal
ontologies and Context-Aware Attribute-Based Access Control (CAABAC) to enhance
EHR access security. Unlike traditional models, GPT-Onto-CAABAC dynamically
interprets policies and adapts to changing healthcare and legal environments,
offering customized access control solutions. Through empirical evaluation,
this framework is shown to be effective in improving EHR security by accurately
aligning access decisions with complex regulatory and situational requirements.
The findings suggest its broader applicability in sectors where access control
must meet stringent compliance and adaptability standards.

数字医疗健康演变过程中，电子健康记录 (EHR) 的安全性变得越来越关键。本研究提出了 GPT-Onto-CAABAC 框架，整合了生成式预训练转换器 (GPT)、医学法律本体和上下文感知属性基于访问控制 (CAABAC)，以加强 EHR 访问安全性。与传统模型不同，GPT-Onto-CAABAC 动态解释政策并适应不断变化的医疗和法律环境，提供定制的访问控制解决方案。通过实证评估，该框架被证明能够通过准确对齐访问决策和复杂的监管和情境要求，有效提高 EHR 安全性。研究结果表明 GPT-Onto-CAABAC 在需要符合严格合规性和适应性标准的领域具有广泛的适用性。