GenRARE Africa: Student-Led AI Tool Targets More Inclusive Rare-Disease Diagnosis
University of Lagos (UNILAG) and Obafemi Awolowo University (OAU)
Research & Innovation Spotlight
GenRARE Africa is an AI-powered clinical decision-support concept developed by a three-student team from the University of Lagos (UNILAG) and Obafemi Awolowo University (OAU). The team won the Genomics Health Hackathon 2025 finals held in Lagos on 22 May 2026.
The problem
Rare diseases can be difficult to diagnose, and genomic data from people of African ancestry remain underrepresented in many conventional diagnostic datasets and tools. This creates an important inclusion challenge for data-driven health technologies intended for African populations.
The innovation
The GenRARE Africa team designed an AI-powered clinical decision-support tool intended to incorporate African-ancestry genomic data into rare-disease diagnostic support. According to UNILAG’s official report, the concept seeks to make diagnostic support more representative of African populations.
The innovators
- JohnPaul Ebubechukwu Okeke — 500-level Petroleum & Gas Engineering, University of Lagos
- Chizoba Ononlememen — 500-level Systems Engineering, University of Lagos
- Oluwapelumi Solagbade — 500-level College of Health Sciences, Obafemi Awolowo University
Why it matters
The project sits at the intersection of artificial intelligence, genomics and health equity. Its hackathon success is a useful example of African students combining engineering and health-science perspectives to address a locally relevant diagnostic challenge.
Current stage and next steps
The university report presents GenRARE Africa as a hackathon-winning innovation; it does not provide evidence of clinical validation or regulatory approval. Appropriate next steps would therefore include validation on larger and diverse datasets, rigorous clinical evaluation, ethics and privacy review, and careful integration with clinical workflows before real-world diagnostic use.
Source: University of Lagos official report
AfScholar editorial summary, source checked 9 August 2026.
The Problem
Rare-disease diagnosis is difficult, while African-ancestry genomic data are underrepresented in many conventional diagnostic datasets and tools.
The Research / Innovation
AI-powered clinical decision-support concept designed to use African-ancestry genomic data to support more inclusive rare-disease diagnosis.
Key Findings / Outcomes
The concept won the Genomics Health Hackathon 2025 finals held in Lagos on 22 May 2026. The official university report does not present clinical-validation results.
Research Impact
Potential to support more representative genomic decision support for African populations, subject to rigorous validation and clinical governance.
What’s Next?
Further dataset validation, clinical evaluation, ethics/privacy review, regulatory assessment and health-system integration would be appropriate before clinical deployment.