Ethical AI in medical text generation
Balancing innovation with privacy in public health
Bibliographic Data
| ID | 22079762 |
|---|---|
| Authors | Mingpei Liang (Youjiang Medical College for Nationalities, corresponding author) |
| Year | 2025 |
| Volume | 13 |
| Pages | 1583507-1583507 |
| Publication date | 2025-07-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2025.1583507 |
| PMID | 40756387 |
| OpenAlex | W4412957434 |
| Language | EN |
| Citations received | 1 |
| References cited | 30 |
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 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. Results and discussion: 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
Accountability · Computer security · Confidentiality · Data science · Encryption · Health care · Health Insurance Portability and Accountability Act · Homomorphic encryption · Information privacy · Interpretability · Knowledge management · Software deployment · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics and Social Impacts of AI · Machine Learning in Healthcare · Artificial Intelligence
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| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |