Artificial Intelligence in Healthcare
For over two decades, IntelliSOFT has engineered the digital backbone of healthcare across Sub-Saharan Africa. We bring that same rigour to artificial intelligence — designing and building AI that works inside real clinical workflows, on real health-system constraints, and to real data-protection standards.
AI that respects how health systems actually work
The promise of AI in healthcare is real, but so are the risks — opaque models, hallucinated advice, systems that ignore how clinicians work or how patient data must be protected. Our approach is deliberately grounded: we choose the right technique for each clinical problem, keep every recommendation traceable to verifiable evidence, and build on open standards such as HL7 FHIR, SNOMED CT, and the WHO SMART Guidelines so AI plugs into national digital-health ecosystems rather than standing apart from them.
Where we apply AI in health
Working with ministries of health, cancer institutes, universities, and development partners, we design and build AI across several distinct areas.
Core capability
AI-Powered Clinical Decision Support (CDSS)
We build Clinical Decision Support Systems that deliver diagnostic guidance, clinical-guideline logic, and medication-safety checks — designed to run as a shared, API-accessible service so an EMR, lab system, or mobile health app can connect and receive guidance at the point of care. We combine several kinds of AI deliberately, and keep the safety-critical core deterministic:
- Standards-based rule engines — the majority of decision logic is deterministic, encoded from WHO SMART Guidelines (L1/L2) and national clinical guidelines as versioned, testable decision tables. It is transparent, auditable, reproducible, and does not hallucinate.
- Retrieval-Augmented Generation (RAG) with LLMs — a language layer used for explanation, not decision-making: it answers strictly from a curated, validated guideline knowledge base and cites its sources, flagging uncertainty rather than fabricating it.
- Classical machine learning — focused models that prioritise and target rather than diagnose: predicting loss-to-follow-up, optimising referral and demand, and prioritising screening and recall.
Population health
AI for cancer surveillance & registries
We apply AI to registry and epidemiological data — machine-learning models to identify patterns and trends, and analytics and visualisations that help policymakers, public-health teams, and researchers interpret the data and target prevention.
Evidence & knowledge
Grounded LLM & RAG systems
We build retrieval-augmented pipelines that read across clinical guidelines, registry data, and peer-reviewed literature to answer natural-language questions and surface evidence — engineered so answers stay anchored to validated sources rather than a model’s unaided output.
Predictive analytics
Risk stratification & targeting
We develop machine-learning models that turn routine and registry data into forward-looking signals — stratifying individual risk and helping programme teams direct scarce screening, outreach, and follow-up capacity where it will matter most.
Access & prevention
LLMs for health information
We research and build large-language-model tools that widen access to accurate, accessible health information — including work on how AI can be used safely, acceptably, and equitably in low-resource settings.
How we build AI responsibly
The same principles run through every one of the areas above.
Grounded
Evidence, not guesswork
Recommendations trace back to curated guidelines and verifiable sources, with citation and explainability built in — so clinicians can trust and check what a system suggests.
Human-led
Decision support, never autonomy
AI is positioned as support for a clinician’s judgement. Mandatory review, clear override and escalation paths, uncertainty flags, and full audit trails are designed in from the start.
Interoperable
Standards-native
HL7 FHIR, SNOMED CT, and the WHO SMART Guidelines keep clinical data structured, computable, and portable across national Health Information Exchanges.
Private by design
Data protection at the core
We apply privacy-by-design, pseudonymisation, and encryption, and are registered with Kenya’s Office of the Data Protection Commissioner as a licensed data controller and processor under the Data Protection Act, 2019.
Exploring what AI can do for your health system?
Whether it’s clinical decision support, surveillance, or predictive analytics, we would be glad to think it through with you — from what’s feasible today to what’s worth building next.