We’re delighted to present our finalists for the category of “AI solution of the year”.
Eolas Medical: The AI answer engine for healthcare
Overview: Eolas Medical is an AI answer engine for healthcare, used at the point of care, to help clinicians access the information they need to make clinical decisions. We are trusted by over 500 healthcare teams and over 350,000 healthcare professionals.
What happened? Ask Eolas is a unique computer vision RAG-based search engine that visually grounds the responses to ensure a trusted response, where the clinician can see and easily access the reference source, even the context-specific source within the specific hospital you’re working in. Clinicians face a constant challenge in accessing the most current and relevant medical knowledge needed for informed decision-making. Eolas Medical directly addresses this challenge by centralising and streamlining access to vital information, including local guidelines, pathways, and educational resources, all within a single, easily accessible platform. By providing instant access to critical information, Eolas Medical empowers healthcare professionals to make faster, more informed decisions. 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.
Ally Cares: AI resident monitoring transforming sleep, safety and outcomes in UK care homes
Overview: Ally Cares is an AI driven acoustic resident monitoring system. Replacing routine night checks with intelligent, sleep-protective monitoring, Ally improves safety, reduces falls, enhances sleep, and gives staff time back. Homes report 63 percent less falls, 50 percent more sleep time, up to six hours carer time saved per night.
What happened? Ally Cares is an AI powered acoustic and motion monitoring system designed specifically for UK care homes. Through discreet in-room sensors, Ally identifies restlessness, calling out, coughing, distress, and potential falls by alerting staff when residents need help. It offers contextual audio playback and movement so staff can understand what has happened before choosing whether to enter a room, AI driven resident insights dashboards highlighting things like sleep patterns and disruptions, and integrations with digital care planning systems. One home reduced night-time checks by 64 percent, while another saved over nine staff hours per night across a single unit. Homes report that nights feel “calmer, quieter and more controlled”, with staff freed from routine tasks and able to respond only where required. Ally learns each resident’s typical patterns and flags deviations early, supporting proactive, preventative care. At The Lawns, residents experienced a 61 percent improvement in sleep quality, a 63 percent reduction in falls and up to six hours of staff time saved per night.
Modality Partnership x Liberate AI
Overview: Modality has deployed an agentic AI solution, transforming long-term condition recall from manual, variable processes into a fully digital, standardised, intelligence-driven system. Automated outreach, AI-generated reviews, and EHR integration now deliver faster access, improved consistency, significant workload reduction.
What happened? Modality launched an AI-driven asthma recall system in April 2025, deploying agentic AI LTC at scale to their 87,000 patient population. More than 3,000 asthma patients were automatically contacted and invited to complete a structured digital assessment covering symptoms, control, triggers, medication adherence and red-flag indicators. Responses were stratified and triggered the next step in the LTC review process including clinical appointments and medication reviews. Liberate AI applied condition-specific clinical logic to patient responses, producing structured triage recommendations, draft clinical reviews, standardised coding, and clear follow-up actions. API development allows two-way data transfer, structured codes insertion, templated clinical summaries, and automatic event creation. Every recommendation required clinician sign-off. There was a 91 percent reduction in manual call volumes and 41 percent reduction in booked asthma review appointments. AI-enabled recall improved data quality and standardisation, with a 100 percent reduction in coding inconsistencies.
Clinrol: Clinrol Connect: AI-powered patient identification and engagement for clinical trials
Overview: Clinrol is an AI-enabled platform improving patient identification, screening and engagement for clinical trials. By supporting research sites with intelligent workflows and clearer communication for patients, Clinrol reduces administrative burden, lowers screen failures and improves access to clinical research.
What happened? Clinrol uses AI-supported analysis of referral and patient information to assist with early eligibility assessment. It supports referral validation and pre-screening workflows, reducing duplication and administrative effort for coordinators. Research sites using the platform report meaningful time savings in early screening and follow-up. Final eligibility and enrolment decisions always remain with qualified research site staff. AI-supported communication workflows help ensure patients receive timely, consistent and clear information, including eligibility updates, appointment reminders and responses to common questions. Research sites report improved patient responsiveness and fewer missed appointments, contributing to stronger retention over time. To date, Clinrol has supported more than 100 clinical trials across Australia, Europe, Asia, the United States and other regions, including studies involving complex and chronic conditions. Research sites report improved recruitment efficiency, reduced administrative burden and better visibility across recruitment pipelines.
InventAsia Limited T/As Prescribe Digital: AISA® – Ambient AI that automates the journey from clinic room to final distribution
Overview: AISA® Ambient AI scribe delivers end-to-end clinical documentation. It captures consultations, distinguishes clinician intent, generates structured outputs, and supports full clinician validation. Integrated with EPRs, AISA® reduces documentation time by up to 70 percent while improving safety, accuracy, and experience.
What happened? AISA® distinguishes between patient narrative, clinician reasoning, and clinician instruction. This allows a single consultation to produce multiple targeted outputs such as clinical notes, GP letters, and patient letters. Once a clinician has reviewed and approved the generated documentation, AISA® can write structured data back into the EPR at the field level. When integrated with a capable EPR, clinician instructions can be used to initiate order communications or trigger automated actions such as referrals or follow-up tasks. Low-confidence content is flagged, and all edits are logged with user and timestamp, supporting full medico-legal traceability. Early NHS evaluations show AISA® reduces documentation time by 60–70 percent, saving up to 15 minutes per consultation. This equates to approximately 256 hours, or 32 working days, returned to patient care per clinician each year. Organisations report faster turnaround of outpatient correspondence, reduced transcription dependency, improved EPR data quality, and improvements in clinician wellbeing.
Newcastle upon Tyne Hospitals NHS Foundation Trust & Solventum: Transforming venous thromboembolism care with AI: the Follow-up Finder project
Overview: At Newcastle upon Tyne Hospitals NHSFT, thrombosis nurse specialists struggled to manually review 900 daily scans. In partnership with Solventum, they developed Follow-up Finder-VTE, reviewing radiology reports in real-time and generating accurate EPR patient lists. The results include a 48 percent increase in VTE diagnosis.
What happened? Follow-up Finder-VTE is an AI tool reviewing radiology reports in real-time that generates accurate EPR patient lists. The innovation aimed not only to address a pressing clinical challenge but also demonstrate how AI could enhance patient safety, empower frontline staff, and optimise existing NHS resources. By integrating seamlessly into the EPR, the solution was designed to minimise disruption, ensure rapid adoption, and create a scalable model for replication. The results include a 3.5 times increase in hospital-acquired thrombosis (HATs) detection, a 40 percent reduction in avoidable HATs, £400,000+ efficiency savings, and two hours of nursing time released daily. For patients, this means faster initiation of anticoagulation, earlier access to thrombectomy or thrombolysis where required, and reduced harm from delayed or missed diagnoses. A HAT dashboard now enables clinical boards to monitor and investigate potentially preventable events, supporting continuous quality improvement.
Lantum: AI-driven rota optimisation transforming workforce planning and wellbeing at Whittington Health NHS Trust
Overview: Whittington Health NHS Trust partnered with Lantum to co-design an AI-powered rota system “In-Genius”, replacing complex, manual rostering with an automated solver. The tool reduced shift gaps, ordinarily filled by bank or agency by over 40 percent, and delivered £624k annualised savings in year one.
What happened? The trust partnered with Lantum to co-develop In-Genius, an AI-powered rota optimisation engine designed to automate scheduling. The solver runs through hundreds of thousands of potential rota configurations, balancing a wide range of constraints including resident doctor contract rules and European Working Time Directive limits. It delivers optimised rotas in minutes rather than weeks. Clinicians were engaged through forums and workshops to define preferences and test early prototypes. Lantum partnered with Medical Workforce to map and redesign workflows, including payroll and compliance checking. This involved upgrading to a new system that interoperated with In-Genius. The solution was first piloted in paediatrics and neonates, achieving £370k in agency and bank spend savings. Over the following year, In-Genius expanded across 17 of 25 eligible rotas across the trust’s specialties. In the first year of rollout, the trust achieved £624,000 in cash-releasing savings from reduced bank and agency gaps. The system is now live or in pilot at 13 other NHS trusts.
Outcomes Matter Consulting x HGS: CHC+: restoring trust and reducing pressure in Continuing Healthcare
Overview: CHC+ uses generative AI to create CHC checklists from case records, supporting adult social care experts to evidence need more clearly, consistently and quickly. Co-developed with Ealing Council, Outcomes Matter Consulting and HGS UK, it is reducing admin time, rework, and conflict.
What happened? CHC+ is a secure AI-enabled tool that generates a CHC checklist from existing documents, including adult social care assessments, discharge summaries, care provider logs, and family and carer information. The tool supports workers by producing draft narratives of need across all checklist domains, highlighting evidence drawn from uploaded documents, allowing practitioners to review, amend and add to the draft. The tool is framed clearly as “decision support”, not “decision making”, and has been co-designed with practitioners to fit real-world workflows. A structured co-design and pilot process with Ealing Council mapped the current CHC pathway, ran iterative testing cycles, built in quick wins, and developed training and guidance focused on ethics, good documentation, and how to use the tool to enhance professional judgement. Early benefits include checklist completion time reducing by over 80 percent for all social workers. Workers describe feeling more organised, less overwhelmed, and more able to focus on conversations with people and families.
Stroud Green Medical Centre: Using AI to improve care planning quality and consistency in a GP practice
Overview: An AI-assisted care-planning initiative looked to improve care planning quality and consistency at Stroud Green Medical Practice, highlighting clinical workflow integration, outcomes, learning, and assurance.
What happened? An AI-enabled care plan drafting tool was developed that uses de-identified clinical information to generate a structured draft care plan for patients with respiratory, metabolic, dementia, and mental health conditions. The system accepts a de-identified extract from the EMIS record, uses a large language model via a secure API to draft a structured care plan, incorporates NICE guidance, North Central London (NCL) pathways, and local service information, and presents outputs strictly as drafts for clinician review. A GP reviews every output and makes substantive edits before the plan is finalised or shared with the patient. In practice, clinicians alter well over 50 percent of generated drafts, reinforcing that clinical responsibility remains entirely with the clinician. Early findings include significant reductions in clinician time spent drafting care plans, improved consistency in care-plan structure across LTC cohorts, positive clinician feedback on usability and reduced cognitive load.
Briya: Briya AIRE: The clinical-AI research environment
Overview: Briya AIRE accelerates biomedical and epidemiological research and supports researchers with designing study cohorts, testing hypotheses, analysing structured and unstructured clinical data – producing publication-ready outputs in a natural language interface.
What happened? The platform allows users to design and validate cohorts instantly, explore hypotheses with natural language, generate statistical summaries and visual outputs, combine structured data with unstructured notes, and prepare publication-ready insights without writing a single line of code. AIRE’s architecture enables hospitals to securely connect and maintain governance over their existing data systems while giving researchers the ability to explore structured and unstructured clinical data in near real time. This is achieved through advanced modelling techniques, dynamic data connectors, and a privacy-preserving engine that ensures no identifiable information leaves the health system’s control. AIRE’s infrastructure is built to meet the strictest global standards, including HIPAA, GDPR, and region-specific health data regulations. For hospitals and health systems, it enables clinicians and researchers to investigate patterns, identify risks, and validate hypotheses directly using their system’s own clinical data.
AZmed: Artificial Intelligence Rayvolve
Overview: Rayvolve automatically detects and localises fractures on X-rays in adults and children. Integrated directly into hospital PACS, it delivers results in seconds, helping clinicians make faster, safer decisions. It reduces missed fractures, repeat imaging, and ED delays while improving diagnostic accuracy and patient outcomes.
What happened? Rayvolve is a CE MDR Class IIa certified medical device that uses AI to automatically detect and localise suspected fractures on plain X-rays of adults and children. Its intended purpose is to assist clinicians in the identification of bone fractures on radiographic images. When an X-ray is taken, the image is sent as usual to the hospital’s PACS or RIS. Rayvolve processes the image in the background and, within seconds, returns a heatmap and a structured result directly into the existing viewer. This highlights regions likely to contain a fracture and provides an accompanying text summary. Clinicians—radiologists, emergency doctors, or radiographers—retain full responsibility for interpretation and final reporting. Deployment is flexible: it can run on-premise through a virtual machine inside the hospital network or via a secure, GDPR-compliant cloud server. Training involves a short onboarding session for radiology and ED staff. The solution integrates with existing governance and audit systems and complies with DTAC and information-governance standards.
