Liang Mingpei
Affiliated Hospital of Youjiang Medical College for Nationalities, Baise, Guangxi, China.
Front Public Health. 2025 Jul 18;13:1583507. doi: 10.3389/fpubh.2025.1583507. eCollection 2025.
The integration of artificial intelligence (AI) into medical text generation is transforming public health by enhancing clinical documentation, patient education, and decision support. However, the widespread deployment of AI in this domain introduces significant ethical challenges, including fairness, privacy protection, and accountability. Traditional AI-driven medical text generation models often inherit biases from training data, resulting in disparities in healthcare communication across different demographic groups. Moreover, ensuring patient data confidentiality while maintaining transparency in AI-generated content remains a critical concern. Existing approaches either lack robust bias mitigation mechanisms or fail to provide interpretable and privacy-preserving outputs, compromising ethical compliance and regulatory adherence.
To address these challenges, this paper proposes an innovative framework that combines privacy-preserving AI techniques with interpretable model architectures to achieve ethical compliance in medical text generation. The method employs a hybrid approach that integrates knowledge-based reasoning with deep learning, ensuring both accuracy and transparency. Privacy-enhancing technologies, such as homomorphic encryption and secure multi-party computation, are incorporated to safeguard sensitive medical data throughout the text generation process. Fairness-aware training protocols are introduced to mitigate biases in generated content and enhance trustworthiness.
The proposed approach effectively addresses critical challenges of bias, privacy, and interpretability in medical text generation. By combining symbolic reasoning with data-driven learning and embedding ethical principles at the system design level, the framework ensures regulatory alignment and improves public trust. This methodology lays the groundwork for broader deployment of ethically sound AI systems in healthcare communication.
将人工智能（AI）集成到医学文本生成中正在通过增强临床文档、患者教育和决策支持来改变公共卫生。然而，AI在这一领域的广泛应用带来了重大的伦理挑战，包括公平性、隐私保护和问责制。传统的AI驱动的医学文本生成模型往往从训练数据中继承偏差，导致不同人口群体在医疗保健沟通方面存在差异。此外，在保持AI生成内容透明度的同时确保患者数据保密仍然是一个关键问题。现有方法要么缺乏强大的偏差缓解机制，要么未能提供可解释且保护隐私的输出，从而损害了伦理合规性和监管遵循性。
为应对这些挑战，本文提出了一个创新框架，该框架将保护隐私的AI技术与可解释的模型架构相结合，以在医学文本生成中实现伦理合规。该方法采用了一种混合方法，将基于知识的推理与深度学习相结合，确保准确性和透明度。引入了同态加密和安全多方计算等增强隐私的技术，以在整个文本生成过程中保护敏感的医学数据。引入了公平感知训练协议，以减轻生成内容中的偏差并增强可信度。
所提出的方法有效地解决了医学文本生成中偏差、隐私和可解释性的关键挑战。通过将符号推理与数据驱动的学习相结合，并在系统设计层面嵌入伦理原则，该框架确保了与监管的一致性并提高了公众信任。这种方法为在医疗保健沟通中更广泛地部署符合伦理的AI系统奠定了基础。
