Aug 2026· Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie· 0 citations· 41 references
Medicine
TL;DR
This narrative review examines the ethical considerations and challenges of using generative AI - particularly Generative Adversarial Networks (GANs) and diffusion models) to create synthetic data for ophthalmic research and concludes with policy recommendations to foster responsible innovation.
Synthetic data is emerging as a strategic solution to persistent barriers in medical artificial intelligence (AI) development, including data scarcity, privacy constraints, and dataset bias. This perspective examines the landscape of synthetic multimodal data in healthcare, including its mechanisms, demonstrated benefi...
S. Reddy, Maryam Haghighat, Andrzej Janusz· Precision and Future Medicin...· 0 citations
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for i...
Abdulkadir Yıldırım, Ö. Özdemi̇r· Artificial Intelligence in M...· 0 citations
AI offers considerable potential to reduce costs, increase diagnostic accuracy, personalize care and expand access to quality services, provided its deployment overcomes technical, ethical and regulatory barriers through proactive, adaptive and patient-centered governance.
Laura Beatriz Fernández Feitó, Mariam Alejandra González González, Andy Alonso Pazos et al.· SAP Social AI· 0 citations
It is argued that LLM-based CDSS are not supported for routine autonomous use and require clinician supervision, and set out a research agenda centred on validation, governance and human-in-the-loop deployment.
A. Babu, A. J. Nehemiah, V. Jagadeep et al.· Frontiers in Digital Health· 0 citations
AI-generated synthetic data are increasingly used to address data scarcity, privacy constraints and experimental limitations in biomedicine, but how far these methods have translated into practice remains unclear. We conducted a systematic mapping and bibliometric analysis of 4,143 publications spanning 2015-2025, comb...
N. Asgari, I. Perez, G. Epelde et al.· medRxiv· 0 citations
ABSTRACT The integration of artificial intelligence (AI) into dentistry is reshaping clinical workflows, opening new possibilities for population‐level public health monitoring. Machine learning approaches, such as convolutional neural networks and other deep learning architectures, are becoming more and more capable t...
Panagiotis Douris, George Kodovazenitis· Public Health Challenges· 0 citations
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