The limits of keyword and AI-based PubMed searches
PubMed remains the backbone of biomedical research, but finding the right evidence within it is harder than it looks. Even experienced teams struggle to retrieve a complete and relevant evidence set for complex clinical questions and protocols. Keyword-only searches miss important variants and related concepts, while the Medical Subject Headings (MeSH) thesaurus and general AI-based query expansion improve recall at the cost of precision. The result is a familiar tradeoff: either miss critical studies or pull in large volumes of noise that continue to require manual cleanup and introduce downstream risk.
How clinical terminology improves precision, recall, and consistency
Clinical terminology changes this dynamic. IMO Health’s terminology is built by domain experts to reflect how diseases are documented, studied, and discussed in real clinical practice. Instead of treating search terms as isolated strings, it encodes clinical relationships – synonyms, subtypes, comorbidities, and phenotypes – into a structured knowledge layer that sits between the research objective and the PubMed query. Searches expand intelligently, not indiscriminately, improving both precision and recall from the outset.
This terminology layer also introduces something most literature workflows lack: consistency. Because the same expert-curated terms and concept groupings are applied across projects, teams can standardize how evidence is retrieved across indications, therapeutic areas, and study/publication types. That standardization reduces variability between searches, limits reviewer-dependent differences, and makes results easier to reproduce, audit, and reuse over time.
Our recent white paper, From keywords to knowledge: How IMO Health’s terminology maximizes relevant PubMed evidence, explores this dynamic in greater detail:
WHITE PAPER
A more defensible foundation for systemic reviews
When embedded into IMO Health’s scientific literature review workflow, the impact is measurable. In arrhythmia test objectives described in the white paper, terminology-enhanced searches retrieved nearly 96% more papers than GPT-5–expanded searches alone for a global disease-burden question, and 150% more for a comorbidity and survival objective. Expert relevance sampling confirmed that the additional volume represented meaningful evidence, not just noise, expanding the candidate evidence base while maintaining control.
The difference is not more automation… it’s better grounding. General-purpose AI tools can generate longer lists of search terms, but they lack disease-specific structure and clinical intent. IMO Health’s terminology brings that context directly into the search process, improving retrieval quality while enforcing consistency across teams and therapeutic areas. For evidence groups supporting clinical development, HEOR, and real-world evidence, this means fewer missed studies, less trial-and-error refinement, and a stronger, more defensible foundation for decision-making.
In environments where evidence must stand up to internal governance, health technology assessment, and regulatory scrutiny, finding more papers isn’t the goal. Finding the right ones, consistently and at scale, is. That requires moving from keywords to knowledge.
