Ethics by Design: A Lifecycle Framework for Trustworthy AI in Medical Imaging From Transparent Data Governance to Clinically Validated Deployment

Ethics must be built into every stage of AI development, not added at the end. The paper identifies five critical phases—data collection, processing, model training, evaluation, and deployment—where ethical safeguards are needed. This "ethics by design" approach means organizations should plan for ethical concerns from the very beginning rather than treating them as an afterthought.

Patient consent and data privacy require careful attention during data collection. The research emphasizes that ensuring proper patient consent and anonymization are foundational ethical requirements. Without these protections, even well-intentioned AI systems can violate patient rights and erode trust in medical institutions.

AI bias in medical imaging can harm patient care and must be actively addressed during model training. The paper highlights that biases can be introduced during the model training stage, which could lead to unfair or inaccurate diagnoses for certain patient populations. This requires deliberate efforts to identify and correct these biases before deployment.

Transparency and fairness during model evaluation are essential for clinical accountability. The study stresses that how AI systems are tested and validated must be transparent and fair, ensuring that results can be understood and justified to patients, clinicians, and regulators.

Continuous ethical monitoring must continue after deployment, not stop at launch. The research emphasizes that ethical assessments should be ongoing throughout an AI system's use in clinical settings, recognizing that new ethical issues may emerge as the technology is used with diverse patient populations and in different healthcare contexts.