Building Africa's first chest X-ray AI benchmark from Nigerian hospital.
What happens when AI trained on American chest X-rays is deploy in a Nigerian hospital? No one has rigorously measured how well they work on our patients.
I'm 9 weeks into a research project to find out.
Week 10/48. This week I audited our entire NLP pipeline.
The question: should you use rules or transformers for medical text?
The answer surprised me. Thread.
Also verified negation detection across 5,517 reports.
0 failures out of 134 negated mentions.
And reviewed 617 reports with 3 labels — 89.9% of all label assignments supported by text evidence.
The lesson: don't choose rules OR transformers.
Use both. Rules give you interpretable precision. BERT gives you semantic reach.
Our ensemble: rules primary on 11 labels, BERT primary on 2.
495 reports ready for radiologist validation.
#MedicalAI#NLP#ChestXray#Africa#BuildInPublic
The pipeline: two extractors working in parallel.
Rule-based: 447-phrase bilingual dictionary with negation detection
BioClinicalBERT: fine-tuned transformer, Normal as 14th label
Rules handle 11 labels. BERT handles 2 that rules can't detect.
They vote. Flag disagreements.
The dataset: 5,517 chest X-rays from FMC Ebute-Metta, Lagos.
Free-text radiology reports. English mixed with local medical terms. 48% had no section headers.
Step 1: turn those reports into structured labels 13 pathologies Normal.
That took Weeks 1–8.