Ghana
Healthcare
Implementing Organisation
MinoHealth AI Labs
Ghana, Accra, Ashongman Estates
Implementing Point of Contact
Darlington Akogo
Founder and CEO
Contributor of the Impact Story
Carnegie India
Year of implementation
2024
Problem statement
Africa’s healthcare system faces critical challenges, primarily driven by severe shortages of healthcare professionals and diagnostic services. In Ghana, the doctor-to-patient ratio is 1:6500, while the radiologist-to-patient ratio is a staggering 1:300,000. This shortage contributes to delayed diagnoses and worsened outcomes for diseases like cancer and cardiovascular conditions. Africa also has the world’s highest breast cancer mortality rate and records high maternal mortality, with 499 deaths per 100,000 live births in Ghana. Limited access to diagnostic tools, especially in rural areas, means diseases are often detected at advanced stages, resulting in poor outcomes. Additionally, Africa’s healthcare systems are strained by the dual burden of infectious and non-communicable diseases, while underfunded infrastructure further restricts access to timely and effective care. These gaps highlight the urgent need for improved diagnostic capabilities and healthcare access across the continent. Globally, these gaps mirror wider systemic challenges, though their impact is most severe in low-resource settings. The World Health Organization (WHO) estimates a global shortfall of around 10–11 million health workers by 2030, with the largest deficits in sub-Saharan Africa. Nearly 4.5 billion people worldwide lack access to essential health services (WHO/World Bank), including diagnostics and maternal care. Breast cancer causes approximately 670,000 deaths globally each year, with mortality disproportionately higher in low- and middle-income countries due to late diagnosis (WHO). Maternal mortality has declined worldwide but remains high, with about 260,000 women dying annually from pregnancy-related causes, largely in regions with limited health workforce and diagnostic capacity (WHO/UN). These global trends underscore the urgency of strengthening diagnostic capabilities and healthcare access in Africa as part of a broader global health imperative.
Impact story details
MinoHealth AI Labs is a leading artificial intelligence (AI) health and bio-technology company based in Accra, Ghana. The organization develops advanced AI systems that span healthcare and biological sciences, including multimodal foundation models and agentic AI platforms for automated diagnosis, prognosis, forecasting, and biological discovery. MinoHealth’s work integrates clinical data, medical imaging, and biological signals to support decision-making, accelerate research, and improve health outcomes. Its mission is to democratize access to quality, affordable healthcare and biological intelligence across Africa and beyond by leveraging cutting-edge AI technologies to address critical health and life-science challenges.
AI Technology Used
Generative AI
Key Outcomes
Access
Reach, Inclusion
Equity
Narrative Outcome
MinoHealth's AI-assisted clinical decision support systems help address severe workforce shortages, compress diagnostic report turnaround times and specialists now review two to three times as many cases daily. Early-stage detection has increased substantially, and diagnostic accuracy for imaging-supported conditions has improved meaningfully.
Impact Metrics
Reduction in time taken to generate diagnostic reports through MinoHealth's AI-assisted automation
Baseline Value
It typically takes 3–7 days to generate clinical diagnostic reports Days/Hours
Post-Implementation
MinoHealth's AI-assisted automation has reduced the time it takes to generate diagnostic reports to less than 24 hours Days/Hours
Increase in number of cases reviewed per specialist due to MinoHealth's AI-assisted reporting
Baseline Value
Manually, a specialist can review 5–10 cases/day Cases per day
Post-Implementation
MinoHealth has increased the number of cases reviewed per specialist to 15–30 cases/day Cases per day
Increase in proportion of diseases detected at early stages via use of MinoHealth's AI-assisted automation
Baseline Value
Early-stage detection via manual detection processes Percentage
Post-Implementation
The use of Minohealth has increased early-stage detection by more than 30% Percentage
Improvement in diagnostic accuracy for imaging-supported conditions by use of MinoHealth's AI-assisted automation
Baseline Value
Diagnostic accuracy for such conditions is variable and specialist-dependent Percentage
Post-Implementation
MinoHealth's AI-assisted automation led to more than 10–20% improvement in diagnostic accuracy for image-supported conditions Percentage
Implementation Context
MinoHealth has usage from over 50 countries including: Ghana, Nigeria, Barbados and United States.
All demographics across women and underserved communities
Key Partnerships
International organizations, government
Replicability & Adaptation
1. Configure integrations to align with local EHR systems and hospital IT architecture 2. Ensure compliance with national health regulations, data protection laws, and clinical governance requirements 3. Tailor user interfaces and reporting formats to local clinical practices 4. Provide targeted clinician training to support effective human–AI collaboration and adoption
Supporting Materials
* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.