In this article we look at how language models are currently being used in healthcare settings, and project forward to see how they might be injected into the clinical landscape of the future.

In this article we discuss how High Performance Computing (HPC) can be deployed on different types of service provider. We believe that the most important factor when choosing the right service provider is the technical capability that has to be maintained within the purchasers organisation. Technical capabilities are difficult and very expensive to build and maintain, and the costs of this capability are often overlooked. This article is intended to expose the required capabilities for each service provider and to provide a guide to help navigate this tricky space.

The article delves into the integration of AI in radiology, where AI-driven tools assist rather than replace radiologists. Techniques like convolutional neural networks (CNNs) help identify pathologies in scans, enhancing accuracy and reducing clinician workload. Several companies are pioneering AI-based diagnostic products, optimizing workflows and providing a “second opinion” for radiologists. The article emphasizes that AI acts as augmented intelligence, complementing human expertise while improving diagnostic speed and precision.

High-performance computing (HPC) is revolutionizing drug discovery, reducing the discovery and preclinical phases from the traditional 5-7 years to under 4 years. HPC's ability to handle complex calculations has long supported scientific advancements, but its integration with AI is now driving rapid innovation in biotechnology. In the UK, companies like Benevolent, Charm Therapeutics, and Exscientia are leading the charge, leveraging HPC for AI-driven drug discovery. Despite the advantages, the rising demand for specialized HPC expertise poses challenges, as shortages and escalating costs make efficient infrastructure management critical to success.