Chronic diseases are projected to generate trillions in global economic costs by 2030. Yet across many health systems, critical data arrives too late to influence outcomes. By the time patterns are detected, disease has progressed, costs have risen, and opportunities for prevention are limited.
The Future Health Challenge 2026: Building Anticipatory Health Systems through Population Sensing, launched by Future Health – A Global Initiative by Abu Dhabi in collaboration with MIT Solve, was created to accelerate the use of real-time data in detecting risk earlier and improving population-level health outcomes.
Selected from 393 submissions across 68 countries, the finalists reflect a shared ambition: to turn health data into early warnings that improve decision-making. Their solutions span diverse settings, from low-resource environments to advanced digital infrastructures, while addressing another critical gap: accessibility to prevention.
The five finalist teams will present their solutions during a live pitching session at the 79th World Health Assembly in Geneva, Switzerland, competing for a USD 200,000 grand prize and two USD 50,000 distinguished finalists awards:
- Vector Control Innovations (United States)
Predicting vector-borne disease through real-time mosquito surveillance
Vector-borne diseases affect more than half of the world’s population, account for over 17% of all infectious diseases, and cause around one billion infections each year, yet many health systems still lack timely visibility into mosquito populations. VectorCam uses offline, on-device AI to identify mosquito species in the field, enabling real-time, localised surveillance before disease transmission occurs. Deployed across multiple countries, including Uganda, Ghana and Kenya, the system has improved data completeness and reporting speed, with evaluations showing data completeness increasing from around 60% to over 90%.
- ThinkMD (Australia)
Generating real-time population health insights from frontline care
With a projected global shortage of 15 million healthcare workers by 2030, many low- resource communities rely on frontline providers with limited training, supervision, or diagnostic tools, contributing to missed or delayed diagnoses. ThinkMD’s mobile platform supports these providers by guiding them through WHO-aligned clinical assessments, functioning fully offline. Each consultation generates structured data, creating a broader population sensing layer that enables health ministries to track disease patterns and service demand. The platform is already in use by more than 9,000 frontline workers across 885 facilities across Africa and the Pacific. ThinkMD’s platform is contributing to more anticipatory system-level decision-making – already demonstrating its effectiveness in detecting symptom patterns months before a cholera outbreak.
- Huna (Brazil)
Using routine blood tests to support early cancer detection and patient engagement
Cancer is projected to impose a $25 trillion economic burden on the global economy by 2050, driven largely by late diagnosis. Huna is rethinking how cancer is detected by focusing on what health systems already produce every day: routine blood exams. Its platform analyses routine blood exam data, such as complete blood counts, to identify subtle risk signals associated with early-stage cancer. The solution combines AI-powered risk triage with a digital navigation layer that ensures people identified as high-risk are guided through appropriate screening and diagnostic pathways. Deployed across public and private healthcare settings in Brazil, Huna’s pilots have screened more than 500,000 patients and detected hundreds of cancer cases, showing that population-level sensing can dramatically improve screening efficiency and shift diagnoses towards earlier, less costly stages of disease, without requiring new infrastructure.
- SOIK Corporation (Japan)
Safeguarding maternal health in fragile settings
An estimated 260,000 women die annually from pregnancy-related causes, with 72% occurring in Sub-Saharan Africa, often due to preventable complications and limited access to timely care. SOIK Corporation’s SPAQ platform is designed for use in low-resource and fragile settings, enabling a single midwife to conduct a complete antenatal consultation using a smartphone, integrating records, screening, risk assessment and referral into one offline workflow. By operating at both community and facility level, SPAQ captures signals such as undetected pregnancies and unmanaged hypertension before they become emergencies. The platform is currently deployed across several countries, having supported more than 25,000 structured antenatal screenings in approximately 50 facilities across four countries and has plans to expand its service to 500 facilities, with a potential reach of more than 600,000 pregnant women each year. Through government partnerships, SOIK is converting early detection into tracked interventions – a core principle of anticipatory health systems.
- Arkangel AI (Colombia)
Transforming the intelligence hidden in clinical text into early warning signals
Up to 80% of clinically relevant information is captured in unstructured formats, but is rarely analysed at scale. Most health systems rely on structured data which often reflect disease only after it has progressed. Arkangel addresses this gap by analysing unstructured clinical notes at scale, extracting early risk signals and disease trajectories that conventional coding-based systems overlook. By converting clinical narratives into structured, actionable insights, the platform enables health systems to identify emerging risks 6 – 12 months earlier than traditional approaches. Operating across more than 300 hospitals in 11 countries, Arkangel makes clinical narratives readable as system-level signals, supporting earlier intervention for chronic and underdiagnosed conditions, particularly in fragmented health systems.
Building the next generation of anticipatory health systems
Together, these finalists show how earlier detection of health risks can be achieved for real- world impact using data that already exists in health systems and communities. Their approaches demonstrate how routine data can be analysed and used in time to inform decisions, improving prevention, diagnosis and resource allocation across different healthcare settings.
Whether through community surveillance, frontline care, routine diagnostics or clinical text, each finalist expands what health systems can detect and how early they can respond. The Challenge provides a platform to support them in further testing, validating and scaling their solutions across health systems, with the aim of moving from pilot use to broader implementation.
Attendees at the Abu Dhabi Future Health Summit (20 to 22 October 2026) will also have access to see the innovations and hear from the teams, with Semi-finalists and selected Honourable Mention teams invited to showcase their innovations in Abu Dhabi.