Clinically-grounded language models. Privacy-preserving architectures. Academic-grade research deployed at scale across Africa.
Four open problems our team is pushing forward right now.
Fine-tuning large language models on African clinical narratives, local languages, and pidgin to improve diagnostic accuracy, interpret region-specific diseases, and deliver context-aware AI for safer, scalable healthcare.
Predicting adverse drug events and interactions in low-resource pharmacy settings — accounting for polypharmacy in patients without formal medical records.
Reconstructing patient journeys across fragmented EMR systems without processing identifiers — enabling cohort research at population scale.
Training fraud detection on Africa-specific claims patterns that differ substantially from Western fraud typologies used in commercial solutions.
Our performance claims are held to the same bar as peer-reviewed medicine — reproducible methodology, disclosed cohorts, open scrutiny.
A controlled evaluation of Syncorix's clinical intelligence against published baselines — covering diagnostic accuracy, adverse-event detection, and response latency on African patient cohorts. Methodology, cohort composition, and raw metrics are disclosed in full.
Every model and pipeline is designed around these standards from day one.
