Ethical Use of Artificial Intelligence for Processing Medical Images

Abstract

Artificial intelligence (AI) tools employ prompts and algorithms to perform tasks that typically require human expertise, hypothesis formulation, and critical evaluation. AI enables rapid analysis of complex imaging data, automates segmentation and lesion detection, and supports real-time image-guided interventions. Deep learning architectures (CNNs, RNNs, U-Net, and transformer-based models) facilitate advanced image classification, reconstruction, and interpretation, achieving clinical accuracies above 90% in multiple domains, including coronavirus disease 2019, oncology, and rheumatology. Generative AI platforms (MedGAN, StyleGAN, CycleGAN, SinGAN-Seg) further support synthetic image creation and dataset augmentation, mitigating data scarcity while preserving patient privacy. However, the integration of AI in healthcare presents significant ethical challenges. Key concerns include algorithmic bias, patient privacy, transparency, accountability, and equitable access. Biases-such as annotation, automation, confirmation, demographic, and feedback-loop bias-can compromise diagnostic reliability and patient outcomes. Ethical deployment requires rigorous data governance, informed consent, anonymization, standardized validation frameworks, human oversight, and regulatory compliance. Maintaining interpretability and transparency of AI outputs is essential for clinical decision-making, while professional training and AI literacy are critical to mitigate overreliance and ensure patient safety.

Keywords: Artificial Intelligence; Diagnostic Imaging; Ethics; Generative AI Platforms.

Fig. 1. Panel (A) depicts the open-access osteoarthritis hand image, panel (B) presents the healthy hand image, and panel (C) displays the synthetic hand image generated with Recraft AI by merging panels (A) and (B). The integration of AI into healthcare research presents a transformative avenue for deepening the understanding of complex pathological processes and clinical conditions among clinicians and investigators. When access to original clinical images is constrained by various restrictions, AI-mediated image synthesis enables the generation of composite or augmented visual materials by combining legally accessible resources, such as textbook illustrations and personally acquired photographs. This approach not only safeguards intellectual property rights but also facilitates the creation of scientifically rigorous, high-fidelity representations that enhance comprehension, support critical learning, and promote innovative research, ultimately advancing medical knowledge and evidence-based practice.

AI = artificial intelligence.