One of the major challenges in glioma surgery is that tumor boundaries are often difficult to define. Diffuse gliomas can infiltrate surrounding brain tissue beyond what surgeons can see directly or what conventional imaging can fully resolve. During surgery, neurosurgeons must remove as much tumor as possible while preserving critical brain functions. This makes accurate intraoperative assessment of tumor infiltration essential for more precise surgical decision-making.  A study published in Cell now reports a new platform called ULTRA, short for ultrarapid cleared stimulated Raman with AI, designed to address this gap. Developed by researchers from Fudan University, Huashan Hospital of Fudan University, Zhongshan Hospital of Fudan University, Beijing Neurosurgical Institute, Capital Medical University, and collaborators, ULTRA combines rapid tissue clearing, stimulated Raman scattering microscopy, and AI-based virtual staining to perform deep 3D histological imaging of surgical tissue without conventional staining or sectioning.

A research team at The Hong Kong Polytechnic University (PolyU) has developed a patient-centric “Artificial Intelligence (AI) Virtual Patient Simulation System”, overcoming the limitations of conventional static diagnosis. By dynamically integrating multimodal patient data, including genomic data, medical imaging and clinical records, the system creates a continuously updated “digital twin” model. It can not only track changes in a patient’s condition in real time, but also predict the potential effectiveness of different cancer treatment options, helping healthcare teams formulate more precise and personalised medical solutions.

Insilico Medicine ("Insilico", HKEX: 3696), a clinical-stage drug discovery company powered by generative artificial intelligence (AI), today announced the launch of its Virtual Aging Cell (VAC) Webpage alongside the preview of the company’s Multi-Agent driven VAC platform.

An NJIT-led research team has developed an AI system that can spot faint warning signals of solar active regions, potential precursors to solar storms, nearly nine hours before they become visible.

Air pollution from ships poses a serious health risk in major port cities, yet existing mitigation strategies often rely on reducing the speed of sailing, which affects operational efficiency. Researchers from Pusan National University have now developed an AI-powered framework that combines real-time weather data, physics-informed learning and multi-objective optimization. It recommends optimal ship routes and speed profiles to reduce peak pollution exposure by predicting how exhaust plumes disperse, while maintaining fuel-efficient operations. 

Optical computing is attracting strong interest because light can carry and process information at extremely high speed while consuming less energy than many electronic approaches. In principle, optical systems can accelerate demanding tasks such as artificial intelligence inference, image processing, scientific simulation, and high-bandwidth signal processing. However, most proposed optical processors still depend on electronic memory to hold intermediate data. Each time information is converted between light and electronics, the system pays a penalty in speed, energy, and complexity.

Soft electronics are an emerging class of mechanically compliant platforms that enable conformal, skin-interfaced sensing and actuation on curvilinear and dynamic surfaces. These systems combine deformation-tolerant electrical functionality with soft contact mechanics, but their in-use performance is strongly influenced by time-varying interfaces, motion-induced artifacts, and the system burden associated with dense multimodal integration. Advances in soft electronics are now converging with artificial intelligence, which supports reliable information extraction from high-dimensional signals and enables on-device inference that tolerates variability across users and day-to-day conditions. Here, progress in this convergence from materials to intelligent systems is summarized. Material and interface foundations are introduced first, focusing on deformation-tolerant conductors, low-impedance biointerfaces, and breathable substrate strategies that support extended wear. Manufacturing and integration approaches are then discussed, highlighting scalable fabrication, multilayer interconnects, and energy-autonomous wireless operation that enable higher channel counts and multifunctional architectures. Learning-based pipelines are subsequently reviewed with emphasis on artifact suppression, nonideality compensation, multimodal inference, and efficient edge deployment. Finally, emerging directions including neuromorphic computing and in-sensor computing are discussed, together with current challenges and future opportunities toward deployable intelligent soft systems that operate continuously and reliably in everyday settings.