arXiv subjects

Dimeji Olawuyi

Publications and source records attributed to Dimeji Olawuyi.

2 recordsLinked to original sources

arXiv2026

IyawoBench v2.0: Extended Diagnostic Evaluation of Large Language Model Clinical Triage in Nigerian Primary Care

Large language models are being deployed as clinical triage tools in low and middle income countries where trained physicians are scarce. Existing safety metrics, however, produce misleading confidence: models scoring 100% on binary "did not send an emergency home" safety measures may nevertheless exhibit systematic failure modes that render them undeployable at scale. We present IyawoBench v2.0, an extended diagnostic evaluation of large language model clinical triage on 200 synthetic vignettes derived from 1,200 real patient encounters at 19 Nigerian primary health centres. We introduce a formal mathematical framework comprising fourteen definitions and two theorems that decompose triage safety into three distinct failure modes: Conservative Escalation Bias, Systematic Downgrade Bias, and Middle-Tier Instability. We propose the Escalation Bias Index and Expected Deployment Cost as novel metrics that expose failure modes hidden by conventional accuracy and sensitivity scores. Evaluated on three frontier models (Claude Sonnet 4.6, Llama 3.3 70B, Llama 3.1 8B) plus five naive baselines, we show that: (1) all three models exhibit at least one formal failure mode; (2) traditional sensitivity metrics conceal a 77 percentage point under-triage gap in Llama 3.1 8B; (3) the optimal model varies across three deployment scenarios (Emergency-Focused, System-Sustainability, Balanced), demonstrating that single-ranking benchmarks are inadequate for LMIC clinical AI selection. IyawoBench v2.0 provides both a rigorous benchmark and a diagnostic framework transferable to any triage-style clinical AI evaluation. All code, data, and analysis pipelines are publicly available.

cs.CY↗

arXiv2026

Safety That Does Not Transfer: Cross-Lingual Clinical Correctness Drift in Deployable Medical Language Models

Safety evaluation of large language models is conducted predominantly in English and predominantly on frontier systems. Neither condition describes how such models are encountered in low-resource health settings, where small quantised systems are run locally and queried in local languages. We ask whether clinical safety established in English transfers to Hausa, and whether any failure is attributable to the language, the clinical task, or the class of model that low-resource deployment admits. Matched English-Hausa question pairs were built for three conditions of high burden in northern Nigeria: malaria, sickle cell disease, and tuberculosis, probing knowledge recall, emergency triage, a leading question inviting a contraindicated action, and a traditional-remedy claim. Six models were evaluated: five locally deployable systems of 4-9 billion parameters, two medically fine-tuned, and one frontier system. All 128 responses were scored against Nigerian national treatment guidelines by two fluent Hausa speakers working independently and blind to one another. Among locally deployable models, mean clinical correctness fell from 1.57 in English to -0.03 in Hausa, on a scale where 2 denotes a correct answer and -1 an actively harmful one. The frontier model moved from 2.00 to 1.75 and produced no response judged harmful in either language. Drift was consistent across all three conditions. Inter-rater agreement was substantial for clinical correctness (kappa = 0.70); agreement on harm was initially poor (kappa = 0.22) and is examined in detail. Because a frontier model answers the same questions competently in Hausa, the deficit is a property neither of the language nor of the clinical material, but of the deployable tier.

cs.CL↗