Ethical Challenges in Artificial Intelligence for Medical Imaging

Abstract

Artificial intelligence (AI) is rapidly transforming medical imaging by enhancing screening, diagnosis, prognosis, and treatment planning, often achieving or surpassing human-level performance in specific tasks. However, its integration raises profound ethical challenges that must be addressed to ensure safe and equitable use in healthcare. Transparency and interpretability remain central concerns, as black-box algorithms limit both clinician trust and patient understanding, potentially undermining accountability in medical decisions. Dataset bias further complicates fairness, since underrepresentation of minority groups in training data can lead to unequal diagnostic performance and exacerbate existing healthcare disparities. Data privacy and security also demand careful governance, given that medical images are sensitive and vulnerable to re-identification and cyberattacks, which threaten both confidentiality and public trust. Accountability for AI errors remains unresolved, with unclear legal and professional responsibilities among developers, clinicians, institutions, and regulators. In addition, the evolving role of radiologists raises ethical questions about professional autonomy, skill preservation, and the need for AI to serve as augmentation rather than replacement. Validation and generalizability of models across diverse populations and clinical settings remain insufficient, creating risks when systems are deployed without robust external testing. Broader ethical concerns include equitable access to AI technologies, regulatory adaptation to continuously learning algorithms, and the environmental impact of computationally intensive model training. Ultimately, the successful and ethical deployment of AI in medical imaging requires a patient-centered approach grounded in transparency, fairness, accountability, and collaboration among stakeholders, ensuring that technological innovation strengthens rather than undermines the foundations of medical care.