Ethical AI in medical text generation: balancing innovation with privacy in public health

Introduction 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. Methods 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 generat