Accurate and interpretable gait analysis plays a crucial role in the early detection of Parkinsons disease (PD),yet most existing approaches remain limited by single-modality inputs, low robustness, and a lack of clinical transparency. This paper presents an explainable multimodal framework that integrates RGB and Depth (RGB-D) data to recognize Parkinsonian gait patterns under realistic conditions. The proposed system employs dual YOLOv11-based encoders for modality-specific feature extraction, followed by a Multi-Scale Local-Global Extraction (MLGE) module and a Cross-Spatial Neck Fusion mechanism to enhance spatial-temporal representation. This design captures both fine-grained limb motion (e.g., reduced arm swing) and overall gait dynamics (e.g., short stride or turning difficulty), even in challenging scenarios such as low lighting or occlusion caused by clothing. To ensure interpretability, a frozen Large Language Model (LLM) is incorporated to translate fused visual embeddings and structured metadata into clinically meaningful textual explanations. Experimental evaluations on multimodal gait datasets demonstrate that the proposed RGB-D fusion framework achieves higher recognition accuracy, improved robustness to environmental variations, and clear visual-linguistic reasoning compared with single-input baselines. By combining multimodal feature learning with language-based interpretability, this study bridges the gap between visual recognition and clinical understanding, offering a novel vision-language paradigm for reliable and explainable Parkinsons disease gait analysis. Code:https://github.com/manaralnaasan/RGB-D_parkinson-LLM

Sammy Selim strode through the dense, shiny green bushes on the slopes of his coffee farm in Sorwot village in Kericho, Kenya, accompanied by a younger farmer called Kennedy Kirui. They paused at each corner to input the farm's coordinates into a WhatsApp conversation. The conversation was with Virtual Agronomist, a tool that uses artificial intelligence to provide fertiliser application advice using chat prompts. The chatbot asked some further questions before producing a report saying that Selim should target a yield of 7.9 tonnes and use three types of fertiliser in specific quantities to achieve that goal. "My God!" Selim said upon receipt of the report.

Like many fans this week, you may have noticed something odd about the opening credits of Secret Invasion, Marvel's new show. Amidst all the Skrull green, there is something very … Midjourney about the whole thing. If you got that sense, you weren't wrong: Those credits were made with the help of artificial intelligence. The idea, Secret Invasion's executive producer Ali Selim told Polygon this week, was to make something that reflected the show's theme of aliens hiding among us. "When we reached out to the AI vendors, that was part of it--it just came right out of shape-shifting, Skrull-world identity, you know? Who is this?" Selim said.

Mindset takes people through three "fun" cognitive tasks with the aim of creating a significant dementia database and one day screen for the syndrome. A group of UK medical students have released medical app Mindset which hopes to become the "world's largest dementia AI initiative". The brain syndrome – which can cause memory loss and changes in behaviour – is a significant and growing problem, particularly for people over the age of 65. It is thought that around 62% of individuals suffering from dementia are undiagnosed. By 2050, the number of people who suffer from the condition is expected to triple, mostly because of an aging population.