Mirae, a continuous care platform for autoimmune disease and other complex chronic conditions, has raised £4 million in funding from Oxford Science Enterprises. Mirae captures what patients experience between visits and converts it into a continuous, structured view of disease that clinicians can act on at the point of care, combining patient-reported data with clinical context and peer-reviewed evidence to support more informed, timely treatment decisions.
Designed to fit into daily life, Mirae lets people describe symptoms, daily behaviours, medications and changes in plain language as they happen, without needing to structure or organise anything themselves. Conversational AI asks follow-up questions, tracks patterns and builds a continuous record of how a condition is evolving, helping patients understand what tends to trigger symptoms, how they have responded to treatment and what has changed. For clinicians, an AI copilot brings symptom trajectories, treatment history, lab data and other inputs into a single, patient-specific picture that can be reviewed before or during an appointment, shifting time away from reconstructing a patient's history and towards evaluating treatment options and next steps.
Autoimmune disease and other complex chronic conditions place a high burden on both patients and health systems because they evolve continuously but are managed episodically. Chronic disease could cost the US up to $47 trillion over the next 15 years, with medical and productivity losses contributing to nearly $13,000 per American, yet care is still delivered through infrequent visits that provide only limited snapshots of a patient's condition.
Research from the University of Oxford's Computational Health Informatics Lab underpins the platform, where co-founder David Clifton has led work in modelling disease progression using large-scale, longitudinal clinical data. Initial focus is on inflammatory bowel disease, a condition marked by unpredictable flares, complex medication decisions and highly variable treatment responses. Deployment is under way with a leading US health system, providing ongoing access to real-world patient data to support model development and validation.
Where you live should not dictate the quality of care you receive. A lot of the variability in care and outcomes comes from the fact that clinicians are working without a full view of what has happened between visits. The limitations of AI in understanding and modeling complex disease comes from the fact that patients are living with their disease every day, but that experience rarely makes it into the clinical record in a usable way. When you combine experiential data with clinical data, you begin to understand and model disease more effectively and have a path towards true precision medicine. Every patient deserves access to world class decision-making for their care and the tools to take ownership over their condition between visits.
AI is moving quickly into healthcare, but much of that activity remains broad, generalized, and disconnected from the clinical decisions that determine outcomes. Its greatest impact will come from areas where decisions are complex, conditions evolve, and the cost of getting those decisions wrong is high. Mirae is building for this deeper layer of medicine, where patient context, clinical evidence, and workflow need to come together to support a higher standard of care.
