Eolas Medical
Overview: Eolas Medical is an AI knowledge management platform, used at the point of care to streamline access to the information clinicians need to make clinical decisions. It has over 200,000 healthcare professionals registered on the platform in the UK, Ireland and the US. Within the UK, we’re used by over 80 percent of NHS trusts.
Why? Clinicians face a constant challenge in accessing the most current and relevant medical knowledge needed for informed decision-making.
What happened? Eolas Medical was designed and developed by physicians to streamline access to the information you need in seconds, on mobile or desktop. This includes a content management system, allowing streamlining of local content such as guidelines, pathways, education and much more. We reduce the time searching for information for HCPs, using our AI based search, allowing greater efficiency and improve compliance to clinical guideline utilisation, ensuring consistent and safe care. By providing instant access to critical information, Eolas Medical empowers healthcare professionals to make faster, more informed decisions. This leads to improved adherence to best practices.. Eolas Medical significantly reduces the time clinicians spend searching for information (80 percent reduction in time spent searching). This increased efficiency can allow them to focus more on direct patient care, improving productivity and resource allocation within healthcare organisations. The physician-designed platform’s intuitive interface, coupled with its robust content management system, allows for the seamless integration of both global medical knowledge and local guidelines. This unique combination empowers healthcare organisations to deliver consistent, evidence-based care tailored to their specific needs.
Looking ahead. Eolas Medical hopes to continue the platform’s success in scaling across three markets (UK, Ireland, and US), working to address the universal challenge of knowledge management in healthcare.
Dyad Artificial Intelligence Limited
Overview: BetterLetter streamlines GP practice workflows by using AI to automate document processing. Each document is coded with full clinical context for the patient, improving data accuracy and safety while reducing administrative burden.
Why? GP practices receive hundreds of letters each day containing critical information such as new diagnoses, medication changes, referrals, and follow-up tasks. Yet, in many practices, this process remains manual and time-consuming. Coding a single complex document can take up to 10 minutes for an experienced coder.
What happened? BetterLetter was created to address these challenges through a semi-automated approach to clinical documentation. The system harnesses AI to interpret incoming letters, extract clinical information (such as diagnoses, medications, and investigations), and match these details to the appropriate SNOMED codes. A coder then reviews the AI-generated suggestions, approving or refining them with a single click. By analyzing each letter in the context of the patient’s existing record, BetterLetter also flags potential safeguarding concerns, detects contradictions in medications, and highlights urgent updates for immediate action. Over 100 GP practices and Primary Care Networks (PCNs) across England now rely on BetterLetter, collectively processing more than half a million documents to date. The impact has been substantial across time savings, with complex letters completed in 3-4 minutes; data quality, with BetterLetter’s contextual awareness preventing duplicate coding and ensuring new information is accurately linked to existing problems; improved workflow, as once coded, documents are automatically routed to the relevant staff member for follow-up tasks; and enhanced patient safety, as urgent clinical details, medication conflicts, and safeguarding risks are surfaced quickly. Additionally, BetterLetter supports coder training and onboarding with in-app guides, instant chat support, and real-time feedback.
Looking ahead. BetterLetter’s roadmap includes deeper integration with secondary care systems and community-based services. By bridging data gaps across the healthcare ecosystem, the platform could further reduce duplicate tasks and ensure that critical patient information flows seamlessly between clinicians.
VirtTuri
Overview: VirtTuri works alongside the NHS, pharmaceutical companies, and medical charities to deliver clinically compliant AI avatars that enhance patient engagement, recall, and understanding. VirtTuri directly aligns with the NHS England Core20Plus5 framework, ensuring equitable access to critical health information.
Why? To support patient self-management and improve well-being. This proactive approach prevents illness, reduces medication misuse, and minimizes hospital admissions. The use of personalized, relatable avatars has significantly enhanced the effectiveness of digital health interventions.
What happened? Patients in both hospital and home care settings often struggle to access and understand critical medical information, and in diverse populations, this issue is exacerbated by language barriers and varying levels of health literacy. COPD requires careful management to prevent exacerbations, particularly during periods of lifestyle changes. One critical time of increased risk is during Ramadan, and studies show that COPD exacerbations significantly increase during Ramadan due to changes in medication schedules, dehydration, and altered eating patterns, often leading to higher hospital admissions. Currently used by Mid Yorkshire Hospitals NHS Trust in Bradford, VirtTuri’s AI-driven clinical informatics avatars provide targeted support for COPD patients by delivering culturally tailored education on safe fasting practices for individuals with respiratory conditions, guidance on adjusting medication schedules to align with Suhoor (pre-dawn meal) and Iftar (evening meal), and advice on recognising early warning signs of exacerbations and when to seek medical help. Our analysis shows a 75 percent reduction in hospital admissions among COPD patients who received targeted self-management education. Virt Turi AI technology was first deployed to improve patient engagement and awareness of Infacol. Successes of that deployment include 250,000+ interactions, 24/7 user access to vital product information, and a 95 percent reduction in pharmacovigilance management costs.
Looking ahead. By combining scientific rigor, AI innovation, and real-world impact, VirtTuri is driving meaningful change in healthcare literacy, reducing health disparities, and enhancing patient outcomes worldwide.
PharmaTools.AI
Overview: Patiently AI is an innovative mobile app that transforms complex medical notes into clear, actionable insights. Designed for patients, caregivers, and healthcare professionals, it enhances health literacy by tailoring information to various audiences, styles, and languages, thereby improving communication and understanding.
Why? When doctors write medical notes, they use technical language that’s great for other healthcare professionals but can be really tough for patients to follow. This creates a communication gap that can leave patients feeling lost about their own health care.
What happened? Patiently AI is a mobile app that turns complicated medical notes into clear, simple explanations that anyone can understand. Our app uses artificial intelligence to translate medical jargon into plain language, adapting to match the differing needs of patients, caregivers and healthcare workers, translating medical information into multiple languages, and allowing healthcare providers to speak their notes directly into the app. Imagine getting test results back from your doctor. Instead of staring at confusing medical terms, you could scan the report with Patiently AI and get a clear explanation of what the results mean. The app even suggests questions you might want to ask at your next appointment. Or picture a doctor finishing up with a patient. Rather than typing up notes later, they can speak them into the app, which creates both the official medical record and an easy-to-understand version for the patient right away. While Patiently AI is still new, it’s already making a difference in healthcare, with results showing patients understand their health better and are more likely to follow their treatment plans, family members and caregivers can provide better support because they clearly understand what’s going on, healthcare teams can work together more efficiently, and doctors spend less time on paperwork.
Looking ahead. Patiently AI is changing how we handle health information. By making medical notes clear and accessible, we’re helping create a healthcare system where everyone can communicate better.
X-on Health
Overview: X-on Health’s Surgery Assist AI tool and AOS helped Tudor Lodge Health Centre optimise its telephony, resulting in 50 percent reduced call waiting times and a 65 percent reduction in missed calls. Applied nationally, surgeries could see a reduction of 9.1m calls received each month, alongside a 2.3m drop in missed calls.
Why? Tudor Lodge Health Centre, a GP surgery in London serving 11,000 patients, faced rising demand for appointments and overwhelming telephone traffic during peak hours, particularly the ‘8am rush.’ Demand was outstripping capacity, and the ‘8am rush’ led to a poor patient experience and put a strain on limited staffing resources.
What happened? X-on Health carried out a 12-month Access Optimisation Service (AOS) and applied its Surgery Assist (formerly EDATT) AI software, providing unparalleled insights, automation, and optimisation opportunities. X-on undertook a full telephony call flow audit and data-driven assessment which included call data, online consultation submissions, NHS App uptake, patient survey outcomes, and patient access routes. X-on also carried out a multilingual patient survey at the surgery, with feedback showing 53 percent had never used an online booking solution, and 68 percent found it difficult to book an appointment. Following the audit and survey, X-on implemented a number of changes in collaboration with Tudor Lodge: expanding self-referral pathways and the adoption of digital tools, including
X-on’s Surgery Assist AI software, introducing a streamlined closed-loop call flows (including an out-of-hours
service), improving digital literacy of staff through specific training, implementing personalised call flow and messaging for patients, and promoting patient self-service via the NHS App and AccuRx. Improvements included a decrease of 21 percent in the number of inbound calls, a 65 percent reduction in the number of missed calls, and a 48 percent reduction in calls for appointments. The number of online prescriptions placed increased by 66 percent (from 222 to
368 per month), whilst the average call queue time reduced by 50 percent, (from 180 to 90 seconds).
Looking ahead. The approach has already been successfully replicated in several other primary care settings, and X-on has projected that by applying Surgery Assist and the AOS model across GP practices in England, the NHS could see a reduction of 9.1 million monthly calls received, alongside a 2.3 million drop in missed calls.
Lilli
Overview: Lilli’s home monitoring technology provides care teams with AI-driven insights generating efficiencies across health & care. By using Lilli, carers can: generate additional carer hours through right-sizing support; spot health decline early to enable early intervention; accelerate waiting lists for care assessments & discharge times from hospital.
Why? To help people live safely and independently at home for longer, generating significant efficiencies across both the health and social care systems.
What happened? Through small, unobtrusive sensors in a person’s home, Lilli tracks everyday activities like moving around, eating, sleeping, and using the bathroom. Our AI learns each person’s normal routine and spots unusual changes, alerting carers so they can step in quickly when needed. Additionally, our system continuously monitors patterns of behaviour to determine someone’s level of independence and provide a detailed, AI-driven care assessment. This helps carers not only intervene early to avoid emergencies but understand care needs at the front-door and adjust care packages over time based on each person’s evolving needs. This is leading to better outcomes and significant resource efficiencies and cost savings. By having Lilli in place, carers are able to clearly evidence based on data where care is most needed so their stretched resources can be allocated effectively and waiting times for assessments can be accelerated. North Tyneside Council deployed Lilli across various pathways, including frailty, hospital discharge and reablement, to understand people’s care needs.
Looking ahead. Economic analysis suggests that by 2035, our AI’s benefits could be equivalent to employing 9,700 full-time carers in the UK and could save 2.3 million bed days for the NHS over the next 10 years.
Medtronic
Overview: The AccuRhythm AI platform is an artificial intelligence system that applies deep learning algorithms to LINQ II insertable cardiac monitor (ICM) data flowing into the CareLink™ network.
Why? False alerts in the ICM space creates workload for clinicians viewing the data and can lead to ambiguity and potential mis diagnosis.
What happened? The algorithms reduce false alerts from the two most common sources of ICM false alerts — Atrial Fibrillation (AF) and Pause.2-4. The AccuRhythm AI AF algorithm enhancement further reduces AF false alerts, preserves sensitivity, (true events) and delivers actionable alerts. Accurhythm AI will look at the episodes that the device, LINQ or LINQ II thinks is an arrhythmic episode and will further decide if the devices assumption is correct or not. If correct, it sends the information onto the clinic, otherwise it is removed from the clinician being able to see it. Accurhyhm AI reduces false diagnostic alerts by 97.4 percent for pause episodes and 88.2 percent in Atrial fibrillation alerts. Evidence has shown this has lead to 200 hours reduction in clinic time for an average clinic annually. AccuRhythm AI algorithms were rigorously trained and developed based on over one million professionally adjudicated ECGs to preserve sensitivity and provide data-driven insights free from bias. 1,5-7 Bypass logic ensures that clinically relevant events still are sent to to clinicians. The power of AccuRhythm AI, is built on 25 years of experience, robustly trained, fine-tuned, and rigorously validated with data from real-world ICM patients.
Looking ahead. Cloud-based enhancements are implemented seamlessly. The update is applied via the cloud to benefit existing and future Reveal LINQ and LINQ II ICM patients.
West London NHS Trust/Healthy io
Overview: The implementation of the Minuteful for Wound AI digital wound management solution enables standardised measurement and documentation of wounds and monitoring over time, allowing West London NHS Trust to have remote clinical oversight and improved data insights driving earlier intervention and better patient outcomes.
Why? At West London NHS Trust, the management of acute and chronic wounds accounts for a significant proportion of the community nursing caseload and cost. Community nurses used manual tape measures for wound measurement, and unregistered nursing staff often needed guidance for complex wounds, leading to delays and inconsistent care.
What happened? Clinicians complete a bespoke wound assessment using their smartphone, and the AI driven algorithms calculate automated wound area and depth measurements, identifying the wound bed tissue types, and also giving percentages of the distribution. MfW offers a ‘live caseload review’ portal to provide central access to wound data. This allows for early diagnosis and treatment, appropriate referral, MDT reviews and virtual senior/specialist review to support clinical decision making. Senior clinicians can use the MfW portal to review patients on the caseload; prioritising the wounds that are in need of the most urgent attention. The senior clinician can remotely optimise the treatment plan and provide advice and guidance for frontline nurses, as well as identifying patients that need specialist intervention, potentially preventing a wound from deteriorating. The impact has included increased quality of care, improved outcomes for patients, improved consistency of reporting, reduction in unwarranted variation in wound care, standardised clinical data collection and reporting, and increased community nursing capacity. Since July 2022, MfW has supported 55,902 assessments and 3,162 patients. Using MfW clinical signs of infection were documented 96.4 percent of the time, representing a 67.9 percent increase from the traditional documentation method pre MfW.
Looking ahead. West London NHS Trust and Healthy io will continue to drive earlier intervention and improved patient outcomes using the MfW digital wound management solution.
AILIS
Overview: AILIS integrated breast health monitoring system combines AI with Deep Neural Networks (DNN), Parametric Dynamic Imaging and telemedicine. The system performs a breast examination in just 4 minutes and provides the examination result after 10 minutes in the app, so the whole procedure takes about 15 minutes.
Why? Breast cancer constitutes about 30 percent of all malignancies among women in European countries. It accounts for 16.6 percent of cancer deaths in this population. It is often detected at a late stage, when its cure is difficult or impossible.
What happened? AILIS was designed by Michal Matuszewski, who, after a family tragedy (caused by cancer), decided to confront breast cancer. For the project, he engaged leading Polish scientists, doctors and programmers. Together they worked on the solution for 8 years, and it is now in the clinical trial phase. The market launch is planned for 2025/2026 (after certification). As a novel device for breast cancer diagnosis, AILIS combines dynamic parametric imaging, artificial intelligence and telemedicine, allowing for a non-contact examination within a few minutes and providing high-quality diagnostic data. A breast exam with results takes only 15 minutes. Thanks to AI, the device examines breasts without contact and without radiation, taking 21,000 measurements in 4 minutes. It sees cancer earlier (than other methods) because it can image the functioning of the tissue under examination, where an abnormality can appear before the tumor structure becomes visible. This is because the system collects information about how the tissue under study functions and allows it to distinguish an area of increased activity from a normal background. To date, nearly 1,500 ladies both healthy and with suspected cancer (BI-RADS 4b, 4c) have already been examined with the AILIS device. The project’s partners include the Marie Sklodowska-Curie National Cancer Institute.
Looking ahead. In the future, it is hoped that the examination can be performed during a coffee break or while shopping, which would eliminate the unnecessary stress of a visit to a medical facility.
Kingston and Richmond NHS Foundation Trust
Overview: In early 2024, Kingston and Richmond NHS Foundation Trust partnered with Skin Analytics to deploy AI as a Medical Device, DERM, into their post-referral service for suspected skin cancer. The innovative dermatology team leveraged DERM to address demand, drive early skin cancer diagnosis for patients and improve service sustainability.
Why? The trust receives around 5,400 urgent suspected skin cancer (USSC) referrals annually. Limited clinical capacity and a national shortage of consultant dermatologists (1 in 4 posts unfilled) meant KRFT faced challenges meeting demand. Compounding this, their skin cancer conversion rates were below the national average.
What happened? In early 2024, KRFT partnered with Skin Analytics to implement DERM into their USSC pathway. DERM assesses, screens and triages lesions that are suspicious for skin cancer; it does this by analysing dermoscopic images of skin lesions and classifying the most common malignant (cancerous), pre-malignant (pre-cancerous) and benign (harmless) skin lesions – in seconds, with comparable accuracy to dermatologists. For KRFT’s dermatology team, this looks like removing patients with benign lesions from dermatology case load, reserving specialist capacity while expediting those with suspected skin cancers to their care: finding cancers, increasing capacity and reducing wait times. Q3 discharge rate overall was 29 percent, with consistent 28-Day FDS of above 91 percent achieved throughout July – Dec 24. The average percentage of patients seen within 7 working days across Q2 & Q3 was 84.3 percent, and Q2 and Q3 also saw a face-to-face avoidance rate of 81 percent. DERM has been implemented safely, meeting key targets including >95 percent target sensitivity for Melanoma and SCC and >90 percent for BCC, Bowen’s and AK. Across all outcomes, the capacity gains seen from the implementation of DERM mean more specialist time to see and treat patients in greatest need – safely speeding up access to critical diagnosis and making the best use of clinician time.
Looking ahead. Skin Analytics is on a mission to build a world where no one dies from skin cancer, and KRFT is playing a crucial part of it.
