Ethical issues and coping strategies for artificial intelligence in medical research

With the rapid development of artificial intelligence (AI) technologies, their applications in medical research have become increasingly widespread. However, the extensive use of AI has also raised numerous ethical challenges, including data privacy and security, algorithmic bias, model transparency and explainability, and accountability. This paper explores strategies to address these ethical issues based on existing literature. Firstly, advanced techniques such as differential privacy and federated learning are employed to safeguard patient privacy and data security. Secondly, methods such as adversarial learning and data diversification are utilized to identify and mitigate algorithmic bias, ensuring the fairness of AI models. Additionally, explainable AI models are developed to enhance transparency and foster trust among clinicians and patients. Furthermore, accountability for AI systems is clarified, and a robust legal framework is established, adhering to international ethical standards to ensure the ethical application of AI in medical research. This study aimed to provide scientific references and recommendations for the sustainable development of AI technologies in the medical research field.