🩺 CHAIRII

Centre for Health AI Research, Innovation & Implementation

Building African-led AI for better healthcare.

CHAIRII is a health artificial intelligence research, innovation and implementation centre based in Nairobi, Kenya. We develop, evaluate and deploy clinically grounded, locally relevant AI systems for African health systems and other resource-constrained settings.

Our work spans clinical decision support, speech and language AI, medical computer vision, health datasets, AI evaluation, responsible AI and implementation science.

We believe the next generation of Health AI for Africa should not simply be imported. It should be built with African data, African languages, African clinicians and African health systems in mind.

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🔬 What We Build

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🚀 Selected Projects

🎙️ SikizaCare — African Clinical Speech AI

SikizaCare — from sikiza, “to listen” — is CHAIRII's programme for developing locally adapted automatic speech recognition and voice AI for healthcare.

The programme is initially focused on Swahili and Kikuyu, with expansion into other Kenyan and African languages.

Our work includes fine-tuning and evaluating open speech architectures including Whisper and Meta MMS, developing medical vocabulary and contextual correction pipelines, and creating clinician-validated speech datasets.

SikizaCare is designed to support:

Clinical transcription · Ambient documentation · Telemedicine · Medical dictation · Voice-enabled clinical decision support

Our longer-term objective is to combine speech recognition, clinical reasoning and decision support into a unified African clinical voice AI stack.

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🔬 TAMBUA-TB — AI-Assisted Tuberculosis Microscopy

TAMBUA-TB explores how computer vision can improve tuberculosis diagnosis using AI-assisted microscopy.

The project focuses on building vision models capable of identifying acid-fast bacilli from Ziehl-Neelsen microscopy images and supporting outputs aligned with routine TB microscopy workflows.

The broader objective is to make AI-assisted TB screening accessible in resource-constrained laboratories using low-cost imaging and smartphone-assisted microscopy.

The work combines:

Computer vision · Microscopy · Diagnostic AI · Smartphone imaging · Clinical validation

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👁️ AI for Retinopathy of Prematurity

CHAIRII is developing computer vision approaches for Retinopathy of Prematurity (ROP) screening, an important cause of preventable childhood blindness.

Our research includes retinal vessel segmentation and methods for reducing the dependency on expensive expert-labelled datasets.

One strand of this work has evaluated Frangi-Hessian pseudo-labelling for retinal vessel segmentation, combining classical image-processing approaches with deep learning to support scalable development of ROP screening models.

The goal is to develop clinically useful AI approaches that can expand access to ROP screening in settings where specialist ophthalmology capacity is limited.

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🩺 Chunguza — Cervical Cancer Clinical Decision Support

Chunguza is CHAIRII's work on AI-supported cervical cancer screening and clinical decision support.

The programme explores how structured clinical guidelines, machine learning and AI can support healthcare workers through screening, triage, referral and follow-up pathways.

Our approach emphasizes guideline fidelity, explainability, safety and integration into routine clinical workflows, rather than treating the language model itself as the clinical guideline.

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🗣️ Localised Ambient Clinical AI

CHAIRII is developing locally adapted ambient-scribe and clinical-language technologies designed for African healthcare environments.

Through clinical and telemedicine programmes, we are exploring the creation of multi-turn clinician–patient datasets representing real healthcare workflows and local language use.

Priority clinical domains include:

Maternal & child health · Vaccination · Outpatient care · NCDs · Diabetes · Primary healthcare

The programme connects three layers:

Speech → Clinical documentation → Clinical decision support

Our objective is to reduce documentation burden while creating the infrastructure needed for locally relevant clinical AI.

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🌍 African Health AI Data

High-performing Health AI requires representative data.

CHAIRII is developing approaches for responsibly creating and validating African health datasets, particularly in areas where global AI datasets remain limited.

This includes:

African-language clinical speech datasets

Multi-turn healthcare conversations

Medical terminology and contextual vocabulary

Clinical decision-support benchmarks

Medical image datasets

Model safety and performance evaluation datasets

Where appropriate, datasets are developed together with clinicians, health institutions, professional associations and research partners.

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🤗 Open Health AI

We believe open science can accelerate Health AI innovation across Africa.

CHAIRII is building an open-to-deployment pathway in which selected research models, benchmarks, datasets, evaluation tools and early-generation technologies can be released under appropriate open licences.

This allows researchers and developers to:

Reproduce our research → benchmark new models → fine-tune locally → identify limitations → build better African Health AI systems

Where technologies progress toward regulated clinical deployment or contain protected clinical data or intellectual property, separate controlled or commercial versions may be developed.

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🧪 Our Development Philosophy

Clinical first

Models must solve genuine clinical and health-system problems.

Local by design

African languages, workflows, infrastructure and patient populations should be represented from the beginning.

Small models where possible

Not every healthcare problem requires a frontier-scale generative model. We explore classifiers, specialised models, compact language models and deterministic clinical logic where these approaches are safer and more efficient.

Evidence before deployment

Clinical AI requires benchmarking, safety testing, external validation and continuous monitoring.

Open standards

We support interoperable approaches including FHIR, SMART Guidelines, structured clinical knowledge and standards-based clinical decision support.

Human-in-the-loop

AI should strengthen clinical decision-making rather than silently replace healthcare professionals.

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🧩 Our Hugging Face Repository

This organisation will host CHAIRII's growing portfolio of:

Repositories will be accompanied by documentation describing intended use, limitations, evaluation methodology, licensing and responsible-use considerations.

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🤝 Collaborate With Us

We collaborate with health ministries, hospitals, universities, researchers, professional associations, technology organisations and global health partners working to advance responsible AI for health.

We are particularly interested in collaborations involving:

African clinical datasets · African-language AI · Clinical decision support · Medical computer vision · AI evaluation · Responsible AI · Digital health interoperability · Health AI implementation

email: fred.mutisya@chairii.org

CHAIRII

Centre for Health AI Research, Innovation & Implementation

📍 Nairobi, Kenya
🌍 African-led. Clinically grounded. Open where possible. Built for real-world health systems.

Research · Innovation · Implementation