IyàwóBench: A Benchmark for Evaluating Large Language Model Clinical Triage Accuracy on Undifferentiated Febrile Illness in Nigerian Primary Health Settings
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913155464036352 |
|---|---|
| author | Gabriel, Anthonio Oladimeji Olawuyi, Dimeji Abdulsobur Ajayi, Oloruntoba Aderemi, Temiloluwa |
| author_facet | Gabriel, Anthonio Oladimeji Olawuyi, Dimeji Abdulsobur Ajayi, Oloruntoba Aderemi, Temiloluwa |
| contents | Background. Undifferentiated febrile illness is the leading cause of primary care outpatient visits in Nigeria, yet no validated benchmark exists for evaluating large language model (LLM) clinical triage reasoning in West African primary health settings. Methods. We introduce IyàwóBench v1.0, a dataset of 200 synthetic clinical vignettes across eight febrile illness categories derived from statistical distributions of 1,200 real patient encounters at 19 primary health centres (PHCs) in Oyo State, Nigeria. Six LLMs were evaluated on structured triage classification across two metrics: triage accuracy and safety score. Results. All six models achieved 100% safety scores (95% CI: 96.4-100.0%), never downgrading a critical REFER NOW case to TREAT HERE. Triage accuracy varied substantially: Claude Sonnet (claude-sonnet-4-5) 67.5% (95% CI: 60.8-73.7%), Llama 4 Scout 59.5% (52.5-66.2%), Llama 3.3 70B 43.0% (36.2-50.0%), and Llama 3.1 8B 39.0% (32.4-45.9%). Two models demonstrated near-zero accuracy attributable to structured output non-compliance. Conclusions. Modern LLMs exhibit safe triage behaviour but vary substantially in structured clinical accuracy. Clinically engineered systems with embedded WHO guidelines outperform general-purpose models by up to 28.5 percentage points. IyàwóBench provides the first reproducible evaluation framework for LLM clinical decision support in West African primary care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23465 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IyàwóBench: A Benchmark for Evaluating Large Language Model Clinical Triage Accuracy on Undifferentiated Febrile Illness in Nigerian Primary Health Settings Gabriel, Anthonio Oladimeji Olawuyi, Dimeji Abdulsobur Ajayi, Oloruntoba Aderemi, Temiloluwa Computers and Society Background. Undifferentiated febrile illness is the leading cause of primary care outpatient visits in Nigeria, yet no validated benchmark exists for evaluating large language model (LLM) clinical triage reasoning in West African primary health settings. Methods. We introduce IyàwóBench v1.0, a dataset of 200 synthetic clinical vignettes across eight febrile illness categories derived from statistical distributions of 1,200 real patient encounters at 19 primary health centres (PHCs) in Oyo State, Nigeria. Six LLMs were evaluated on structured triage classification across two metrics: triage accuracy and safety score. Results. All six models achieved 100% safety scores (95% CI: 96.4-100.0%), never downgrading a critical REFER NOW case to TREAT HERE. Triage accuracy varied substantially: Claude Sonnet (claude-sonnet-4-5) 67.5% (95% CI: 60.8-73.7%), Llama 4 Scout 59.5% (52.5-66.2%), Llama 3.3 70B 43.0% (36.2-50.0%), and Llama 3.1 8B 39.0% (32.4-45.9%). Two models demonstrated near-zero accuracy attributable to structured output non-compliance. Conclusions. Modern LLMs exhibit safe triage behaviour but vary substantially in structured clinical accuracy. Clinically engineered systems with embedded WHO guidelines outperform general-purpose models by up to 28.5 percentage points. IyàwóBench provides the first reproducible evaluation framework for LLM clinical decision support in West African primary care. |
| title | IyàwóBench: A Benchmark for Evaluating Large Language Model Clinical Triage Accuracy on Undifferentiated Febrile Illness in Nigerian Primary Health Settings |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2605.23465 |