Researchers from MBZUAI introduce Forget-MI, a machine unlearning method tailored for multimodal medical data, enhancing privacy by removing specific patient data from AI models. Forget-MI utilizes loss functions and perturbation techniques to unlearn both unimodal and joint data representations. The method demonstrates superior performance in reducing Membership Inference Attacks and improving data removal compared to existing techniques, while preserving overall model performance and enabling data forgetting.
MBZUAI researchers Darya Taratynova and Shahad Hardan developed Forget-MI, a method for making clinical AI models "unlearn" specific patient data without retraining the entire model. Forget-MI addresses the challenge of removing patient data from AI models trained on multimodal records (like chest X-rays and reports) due to regulations like GDPR and HIPAA. The method unlearns both unimodal (image or text) and joint (image-text) associations while retaining overall accuracy using a late-fusion multimodal classifier. Why it matters: This research provides a practical solution to a critical privacy concern in healthcare AI, enabling compliance with data protection regulations and fostering trust in AI-driven medical applications.
A Caltech researcher presented at MBZUAI on memory representation and retrieval, contrasting AI and neuroscience approaches. Current AI retrieval systems like RAG retrieve via fine-tuning and embedding similarity, while the presenter argued for exploring retrieval via combinatorial object identity or spatial proximity. The research explores circuit-level retrieval via domain fine-tuned LLMs and distributed memory for image retrieval using semantic similarity. Why it matters: The work suggests structured databases and retrieval-focused training can allow smaller models to outperform larger general-purpose models, offering efficiency gains for AI development in the region.
A KAUST team developed piRNAi, a gene-silencing tool in nematode worms using synthetic RNA sequences interacting with the piRNA pathway. They successfully silenced genes involved in sex determination and other functions, demonstrating multiplexed gene silencing. The gene silencing lasted for varying durations across generations, up to six generations. Why it matters: This expands the molecular toolkit for gene manipulation and offers potential therapeutic applications in humans, given the presence of the same gene-silencing pathway.
This paper introduces a domain generalization (DG) method for Diabetic Retinopathy (DR) classification that maximizes mutual information using a large pretrained model. The method aims to address the challenge of domain shift in medical imaging caused by variations in data acquisition. Experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art techniques, achieving a 5.25% improvement in average accuracy.
MBZUAI researchers presented DEFUSE-MS at MICCAI 2025, a novel AI system for analyzing changes in MRI scans of multiple sclerosis (MS) patients. DEFUSE-MS uses a deformation field-guided spatiotemporal graph-based framework to identify new lesions by reasoning about how the brain has changed. The model constructs graphs of small regions within baseline and follow-up MRIs, linking them across time with edges enriched with learned embeddings of the deformation field. Why it matters: DEFUSE-MS reframes the task from simple "spot the difference" to understanding structural changes, potentially improving the speed and accuracy of MS diagnosis and treatment monitoring.