Patients’ consent is essential when using their health data to train medical AI

The use of patient data to train medical AI systems without explicit knowledge or consent raises serious ethical and privacy concerns. While AI has the potential to revolutionize healthcare by improving diagnostics and treatment, its development must be grounded in transparency and respect for patient autonomy. Many individuals are unaware that their sensitive health information is being used to train algorithms that may later influence medical decisions about them or others. This lack of informed consent undermines trust in the healthcare system and risks violating fundamental principles of data privacy. Patients have a right to know how their data is collected, stored, and used—especially when it contributes to powerful AI tools that can shape clinical outcomes. Without clear communication and opt-in mechanisms, the use of personal health data becomes a form of covert data exploitation. Moreover, the long-term implications of unregulated AI training on patient records are not fully understood. As AI models are repeatedly refined using real-world data, there is a risk of feedback loops that amplify biases or distort clinical patterns, potentially leading to inaccurate predictions or harmful recommendations. These issues are compounded when data is used without oversight or accountability. To address these challenges, healthcare institutions and AI developers must adopt strict ethical guidelines. This includes implementing transparent data governance policies, obtaining informed consent, and allowing patients to opt out of AI training programs. Independent oversight and public engagement are also essential to ensure that AI in medicine serves patients, not just technological advancement. In short, building better AI in healthcare should never come at the cost of patient trust. Responsible innovation requires not only technical excellence but also a commitment to ethical integrity and patient rights.