AI & Knowledge Graphs
Grounding AI in Ontological Knowledge to Reduce Hallucination in Clinical Contexts
Large language models generate fluent, authoritative-sounding medical text, but they fabricate facts when training data provides insufficient constraint. Grounding AI outputs in a verified ontological knowledge base converts free generation into constrained, provenance-traced fact expression — eliminating the hallucination risk that makes ungrounded AI unacceptable in clinical and regulatory contexts.
AI & Knowledge Graphs
From Knowledge Graph to Explainable Clinical Decision Support
Clinical decision support systems that cannot explain their recommendations are not trusted — and in regulated healthcare contexts, they should not be. Knowledge graph-based reasoning produces recommendations with explicit, traceable justifications that clinicians and regulators can verify.
AI & Knowledge Graphs
How Ontologies Reduce Hallucination in Medical AI
Hallucination — the generation of plausible but factually incorrect content — is the central reliability problem of large language models in clinical and regulatory contexts. Ontological grounding addresses this at three levels: retrieval, generation, and post-hoc verification.
AI & Knowledge Graphs
Knowledge-Driven Prompt Engineering for Pharmaceutical Research
Prompt engineering for pharmaceutical AI applications is not primarily about phrasing — it is about structuring the evidence context that the model receives. Ontology-structured context dramatically outperforms unstructured text injection for precision-dependent clinical and regulatory queries.
AI & Knowledge Graphs
Building an AI Assistant That Understands Your Drug Portfolio
Generic AI assistants answer questions about drugs based on public training data. A portfolio-aware AI assistant answers questions about your specific products, your specific clinical data, and your specific regulatory history — grounded in a structured internal knowledge graph rather than the public internet.
AI & Knowledge Graphs
Retrieval-Augmented Generation for Clinical Evidence Synthesis
Evidence synthesis — the systematic aggregation of clinical evidence from multiple studies to support regulatory or clinical decisions — is one of the most time-consuming tasks in pharmaceutical development. RAG architectures that combine structured knowledge graphs with language model generation are beginning to automate the retrieval and structuring phases without compromising scientific rigour.
AI & Knowledge Graphs
Grounding AI Outputs with Biomedical Ontologies: Techniques and Trade-offs
Grounding is the technical mechanism by which AI outputs are linked to explicit, verifiable knowledge representations. Several grounding approaches are available, each with different precision-recall trade-offs, infrastructure requirements, and suitability for regulated versus exploratory applications.
AI & Knowledge Graphs
Why Large Language Models Need a Knowledge Graph to Be Reliable in Pharma
Large language models produce fluent, confident-sounding pharmaceutical and clinical content — including fluent, confident-sounding errors. The knowledge graph provides the structured factual layer that distinguishes a reliable domain assistant from a sophisticated autocomplete.