Rare disease data is often unavailable, fragmented or difficult to analyse, limiting its value for all stakeholders. Advances in AI and digital tools are beginning to change this. These technologies are helping transform scattered information into structured insights and opening new possibilities for research, clinical care and health system planning. Private AI and digital health companies play a vital role in powering these opportunities. One example is Huma, a United Kingdom-based company that has evolved from a rare disease specialist into a provider of regulated, AI-enabled digital health platforms used across healthcare systems worldwide. Its Huma Cloud Platform allows partners to build and launch AI-powered health tools quickly and securely, including for rare conditions where traditional software development approaches may be commercially unviable. Huma’s technology is actively applied in rare disease contexts, including through projects with global pharmaceutical companies UCB for myasthenia gravis and Pfizer for haemophilia, which improve symptom tracking, patient engagement and real-time data sharing. Its federated design also enables analysis across multiple data sources in small and geographically dispersed patient populations without moving or exposing sensitive information, helping protect patient privacy while expanding the reach and coordination of care. Other collaborations between industry and technology firms are expanding the use of AI to detect and manage rare diseases. Pangaea Data and Alexion, AstraZeneca Rare Disease have partnered to develop an AI clinical decision support system to detect hypophosphatasia in adults, a rare metabolic disorder that is often missed due to its diverse, non-specific symptoms. Complementing this effort, Alexion, AstraZeneca Rare Disease has developed deciphEHRTM, a suite of educational resources and toolkits that help healthcare organizations make better use of their EHR systems to identify patients potentially affected by rare diseases. By using relevant patient history, disease codes and suspect patient lists, deciphEHRTM supports clinicians in triaging patients for further evaluation. Public-sector organizations are also applying AI to strengthen rare disease data and improve health system intelligence. Genomics England’s Clinical Variant Ark, a secure knowledge base integrating genomic and clinical data from tens of thousands of rare disease families, has already facilitated more than 100 new diagnoses by enabling experts to build on prior evaluations.39 Foresight, a generative AI trained on de-identified records from 57 million people in England’s National Health Service Secure Data Environment, uses national-scale data to forecast major health events across all demographics, including those living with rare conditions.40 While private and public organizations are making important progress independently, some of the most powerful opportunities emerge through public–private partnerships that combine the reach and governance of public systems with the innovation capacity of private actors. These collaborations strengthen data infrastructures, accelerate responsible use of AI and ensure that new tools are designed with patients at the centre.2.5 Use AI and digital tools to address evidence gaps Making Rare Diseases Count: How Better Data Can Unlock a Multitrillion-Dollar Opportunity 21

Making Rare Diseases Count 2026