What: Recognising that clinical workflow is a key predictor of patient outcomes, researchers with the Mayo Clinic developed a digital twin of the Intensive Care Unit (ICU). A key challenge in creating a digital twin is properly conceptualising the different parts of a system and how they interact. To solve this, qualitative interviews and focus groups were conducted with clinicians, nurses and allied health. From this the team developed a hybrid simulation model capturing both system state and individual movement through the system. That model then underwent a series of validation iterations with prospectively recruited patients. The final product accurately modelled the state of the ICU at any time point and was used to model the effect of different management policies on patient outcomes. Overall, this was a significant, multi-stage process taking in model conceptualisation, systems engineering, qualitative and quantitative data collection, a real-time utilisation study and the computational modelling of interventions. 

What: Osso VR is a leading virtual reality surgical training and assessment platform. It offers custom-developed VR modules, early career healthcare professional (HCP) training, and extensive research resources. The platform enhances learning, improves procedural competency, and provides detailed performance analytics. Osso VR's technology supports medical device companies and healthcare professionals by enabling realistic, immersive training experiences that accelerate skill acquisition and improve patient outcomes.

What: Organovo, founded in 2007 and based in San Diego, pioneer’s 3D printing in tissue engineering. Their technology, licensed from the University of Missouri, utilizes droplet-based, inkjet, and continuous deposition methods to create human cell-based tissues without exogenous scaffolds or plastic culture dishes. These tissues closely mimic human physiology, making them valuable for in-depth in vitro studies and potentially reducing reliance on animal models in drug testing. Organovo aims to develop biocompatible tissues for transplantation, starting with smaller tissue grafts such as liver patches. These grafts have shown promise in animal studies, demonstrating stable engraftment and protein circulation akin to human liver function. The company targets paediatric inborn errors of metabolism and acute-on-chronic liver failure for initial applications, seeking FDA approval to advance into clinical trials.​

What: Unlearn is an AI and digital twin company seeking to reduce the cost and increase the effectiveness of clinical trials. By creating simplified digital twins of human trial participants, Unlearn can model the effect of that participant being enrolled in the placebo arm of a trial. Being able to model a digital placebo arm means that trials can enrol a greater proportion of participants into treatment (which can also boost overall enrolment numbers since most potential participants would prefer the treatment) and it can also boost the statistical power of the trial, resulting in a need for fewer participants overall. As a statistical methodology, the use of digital twin controls has received approval from the European Medicines Agency  and Unlearn is in the process of proving that its digital twins accurately represent the disease progression of physical participants in a control arm.

What: Touch Surgery™ is an AI-powered ecosystem that enhances surgical procedures through digital solutions. It offers tools for video capture, performance insights, live streaming, simulations, and connectivity. These features enable surgeons to turn complex data into actionable insights, track progress, and improve surgical efficiency. The platform integrates next-generation computing and visualization technology, supporting surgeons before, during, and after surgery.

What: ntroduced in 2024, Cisco Hypershield is a groundbreaking security architecture designed for the AI era. Built from the ground up with AI-native technology, it redefines how data centres and cloud environments are secured. Hypershield provides autonomous segmentation, distributed exploit protection, and self-qualifying upgrades. It leverages the power of AI to automatically segment networks, identify and shield vulnerabilities before they can be exploited, and deploy upgrades without downtime. This AI-driven approach significantly enhances security while reducing the complexity and cost traditionally associated with manual processes.

What: Qventus offers an AI-driven platform designed to optimize hospital operations by predicting bottlenecks, recommending solutions, and automating processes through seamless EHR integration. The platform improves surgical scheduling, discharge planning, and resource utilization, enhancing efficiency and reducing length of stay. By combining real-time data and machine learning, Qventus helps healthcare providers create capacity, reduce manual work, and increase revenue. Trusted by top healthcare institutions, Qventus delivers significant ROI and improves patient care through intelligent automation.

What: MedAware has developed an AI-driven platform to enhance medication safety by identifying and preventing prescription errors. Leveraging extensive data from electronic health records (EHRs), MedAware's system utilizes machine learning algorithms to detect anomalies and potential adverse drug events (ADEs). The technology analyses prescription patterns, comparing them to historical data to flag deviations that could indicate errors. By integrating seamlessly into existing healthcare workflows, MedAware's solution supports healthcare providers in making safer, more informed prescribing decisions, thereby reducing the risk of medication errors and improving patient outcomes. The platform also addresses challenges like alert fatigue by refining the accuracy of its notifications, ensuring that healthcare professionals receive only the most pertinent alerts. This approach not only enhances patient safety but also optimizes the efficiency of healthcare operations.

What: This project decodes mental health information by analysing gaming and social media data. It explores how gamers' bonds with their avatars can predict depression risk, using AI classifiers to analyse longitudinal data from 565 participants. For social media, it uses natural language processing to analyse 233,000 tweets, achieving high accuracy in predicting anxiety diagnoses. This integration aims to identify mental health issues early, demonstrating the potential for AI-driven mental health interventions.

What: BlueDot employs predictive intelligence to forecast global climatic suitability for Aedes albopictus and Aedes aegypti mosquitoes under various climate change scenarios projected for the next decade. This allows health services to plan for any forecast increases in the incidence of mosquito-borne illnesses. BlueDot's methodology utilises a gradient-boosted regression tree model, integrating data on precipitation, surface temperature, and elevation. Historical mosquito occurrence data informs the model, which predicts suitability at a detailed resolution of 5km by 5km. The model accounts for three climate change pathways: SSP 1-2.6 (best-case scenario), SSP 2-4.5 (most-likely scenario), and SSP 5-8.5 (worst-case scenario) from the 6th Coupled Model Intercomparison Project.