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#321Computer Science & AIFront Page29 November 2025

The Missing Link in AI-Driven Diabetes Prediction

A systematic review of 49 studies reveals that while researchers are actively using machine learning to predict diabetes complications, they have yet to adopt recent breakthroughs like generative AI. Although eye-related issues are the most studied focus, significant opportunities remain in analysing unstructured data such as medical images.

By Pescol, Bosoni, Ghilotti, De Cata, Sacchi, Bellazzi

#322Medicine & HealthFront Page15 December 2025

AI in Healthcare: When High Accuracy Meets High Risk

While deep learning models now achieve over 90% accuracy in medical imaging, they bring significant risks regarding bias and transparency. The integration of these tools requires rigorous human oversight to prevent automation errors.

By Fedorchenko, Zimba

#323Computer Science & AIFront Page18 November 2025

AI Eyes: New Optimisation Technique Boosts Image Captioning for the Visually Impaired

Researchers have developed a robust new AI framework that converts visual data into descriptive spoken text to assist individuals with visual disabilities. By fusing powerful deep learning models with a specialized 'Gannet Optimisation Algorithm', the system significantly improves the accuracy and context of automated image descriptions.

By Alkhaldi, Asiri, Alzahrani, Sharif

#324Medicine & HealthFront Page14 November 2025

AI Models Untangle Cities’ Twin Crises of Heat and Floods

Cities face the interconnected threats of urban heat islands and flooding, which amplify each other's impact. A major review reveals that scientists typically analyse these hazards in isolation, but highlights an urgent need for unified AI frameworks to model their complex interplay and build more resilient urban environments.

By Imroz M, Akhtar MP, Sharma MK, Alshehri F.

#325Medicine & HealthFront Page15 November 2025

AI in the A&E: A Digital Co-Pilot for Emergency Doctors

A major review of research finds artificial intelligence is making emergency care faster and more accurate across triage, diagnosis, and workflow optimisation. While AI shows clear clinical benefits, its widespread adoption is held back by significant technical, ethical, and legal challenges that must be addressed.

By Almagharbeh WT, Alharrasi M, Khan Rony MK, Kabir S, Alrazeeni DM, Akter F.

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