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Scaling Up Generative AI for Clinical Decision Support Across Ghana

Ghana flag

Ghana

Healthcare

High

Implementing Organisation

MinoHealth AI Labs

Ghana, Accra, Ashongman Estates

Private Sector

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

AI/ML

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

Internal Monitoring·Jan 2025 - Dec 2025

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

Internal Monitoring·Jan 2025 - Dec 2025

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

Internal Monitoring·Jan 2025 - Dec 2025

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

Internal Monitoring, Independent Evaluation·Jan 2025 - Dec 2025

Implementation Context

Deployed

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

High

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

* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.