Executive summary
For artificial intelligence to be genuinely useful in healthcare, its accuracy cannot be an accident; it must be by design. The trustworthiness of any clinical AI depends entirely on where its knowledge comes from and how that knowledge is retrieved and presented. The most robust architecture for this today is Retrieval-Augmented Generation (RAG), which dramatically improves factual consistency by grounding a model's outputs in a library of external, vetted documents. When this is combined with hybrid search techniques, the risk of error is further reduced (MDPI).
For UK adopters, this technical architecture must be paired with a rigorous assurance framework. Any deployment should align with the NHS DTAC, NICE evidence standards, and the principles being established in the MHRA’s AI Airlock for novel medical devices (NHS Transformation Directorate). This article breaks down the technical and governance layers required for accurate clinical AI and provides a transparent case study of how the iatroX engine is built on these principles, using a citation-first, UK-centric pipeline to minimise hallucinations and shorten time-to-answer.
What “accuracy” means for clinical AI
General-purpose large language models (LLMs) are notorious for "confabulating" or "hallucinating"—inventing plausible but entirely false information. In a clinical setting, this is an unacceptable risk. The World Health Organization's guidance for large multimodal models in health is clear: trustworthiness hinges on transparency, mandatory human oversight, and the ability to cite sources (World Health Organization).
A truly accurate clinical AI system, therefore, is one built on a clear, auditable workflow: curated sources → robust retrieval → grounded generation → uncertainty handling → a clear audit trail.
Curate the truth: gated, domain-specific knowledge bases
The single most important principle for clinical AI accuracy is to control the AI’s “reading list.” An AI's output is a reflection of its knowledge source. Therefore, the foundation of a trustworthy system is a "walled-garden" knowledge base, restricting the AI to only authoritative, up-to-date content. For UK practice, this means sources like NICE guidance, SIGN guidelines, the BNF, and SPS medicines advice.
The iatroX Knowledge Centre is built on this principle, routing users to trusted UK sources with explicit citations and last-review dates. Our "walled-garden" ingestion approach limits our AI model to a pre-vetted library of guidance and peer-reviewed research, protecting it from the unreliable information of the open internet.
Find the right page first: hybrid algorithmic search
Before an AI can generate an answer, it must first find the right information. The most effective way to do this is with hybrid algorithmic search. This combines:
- Lexical search (e.g., BM25): Good at finding exact keywords and phrases (e.g., a specific drug name or guideline number).
- Dense retrieval (vector search): Good at understanding the semantic meaning or "intent" of a query.
Combining these two methods improves the chances of retrieving the most relevant passages for tricky clinical queries. This RAG healthcare approach, powered by hybrid retrieval, is increasingly recommended to reduce hallucinations in medical Q&A (arXiv, MDPI).
Generate only what you can cite: RAG with provenance
Once the right information is retrieved, Retrieval-Augmented Generation (RAG) provides the next safety layer. The mechanism is simple but powerful: the AI model is given the retrieved passages and is strictly constrained to generate its answer only from those facts. It then attaches inline citations and links back to the original source. This process materially reduces the risk of fabricated information (MDPI).
The iatroX platform is built on this citation-first principle, with answers that link directly to their sources, alongside visible review dates to ensure transparency.
Say “I don’t know”: uncertainty and abstention policies
A safe AI knows its own limits. If the initial retrieval process does not find relevant, high-confidence information within its knowledge base, the system should not guess. A critical safety feature is an "abstention policy," where the AI will either state that it cannot provide a confident answer or will direct the user to the primary sources to review for themselves. This is a documented feature of the iatroX engine.
Keep it current: updates, versioning and audit trails
Clinical guidance changes. A trustworthy AI must have a clear process for version control and updates. This is a core expectation of the NHS AI Knowledge Repository and NICE's evidence standards. At iatroX, we provide date-stamps, explicit source links, and maintain transparency on how our engine parses and updates its knowledge base from UK guidance at scale.
Assure before you scale: the UK governance stack
A technically accurate model must also be supported by a robust governance framework. For any NHS AI deployment, this includes:
- DTAC: The national procurement baseline covering information governance, cyber security, and clinical safety.
- NICE ESF & EVA: The frameworks for assessing a tool's clinical and economic evidence.
- MHRA AI Airlock: The regulatory sandbox for testing novel AI as a Medical Device (AIaMD).
- Global risk frameworks: The NIST AI Risk Management Framework complements local assurance by highlighting GenAI-specific risks.
Putting it together: iatroX’s accuracy stack
- Gated Corpus: Our knowledge base is restricted to UK-focused guidance (including NICE and SIGN) and peer-reviewed research, reducing noise and the scope for error.
- Hybrid Search → RAG: We use algorithmic retrieval to feed our citation-first generation model, which is designed to abstain when uncertain.
- Operational Transparency: We provide source links, date-stamps, and a clear explanation of our "walled-garden" approach.
- Roadmap Alignment: We are committed to aligning with NHS AI assurance standards and future interoperability goals.
Measurement: how to prove your AI is accurate
- Offline technical metrics: Exact-match Q&A tests, citation correctness, and retrieval precision/recall against a "gold standard" dataset.
- Online real-world metrics: User-verified answer rates, abstention rates, time-to-answer, and click-through rates to cited sources.
- Governance artefacts: A completed DTAC pack, a DCB0129/0160 clinical safety case, and clear change-control logs.
FAQs
- Does RAG really reduce hallucinations?
- Yes, peer-reviewed studies show that RAG significantly improves factual consistency by grounding AI outputs in retrieved, verifiable documents.
- What UK standards apply to clinical AI?
- The key frameworks are the DTAC for procurement, the NICE ESF/EVA for evidence, and the MHRA AI Airlock for novel medical devices, all underpinned by WHO principles on transparency and human oversight.
- How does iatroX ensure accuracy?
- Through a multi-layered approach: a UK-gated knowledge base, hybrid algorithmic search, RAG with mandatory citations, a strict abstention policy, and visible update stamps.