Introduction: the evolution of clinical search
The sheer volume of medical literature is a well-known challenge for every clinician. With published research doubling approximately every five years, the task of manually synthesising the latest evidence for a specific clinical question has become nearly impossible (Lumanity). This information overload demands a new generation of tools.
Enter Retrieval-Augmented Generation (RAG), a form of artificial intelligence that is revolutionising clinical search. By combining the reasoning capabilities of large language models with curated, high-quality knowledge bases, RAG technology provides answers that are not only fast and conversational but also accurate and fully traceable to their source (MDPI). This article explores how major medical knowledge providers are harnessing this technology in 2025 to support UK clinical practice.
BMJ Best Practice: enabling next-gen retrieval
BMJ, a trusted name in medical publishing, is leveraging its extensive content library to power next-generation retrieval tools. Through its Knowledge Base API, BMJ Best Practice is enabling innovative front-end features designed for rapid, context-aware topic retrieval (67 Bricks). A key application of this technology involves embedding BMJ Best Practice content directly into Electronic Health Record (EHR) systems, allowing for point-of-care access through popup windows that provide instant, relevant information without disrupting the clinical workflow.
UpToDate GenAI Labs & AI-enhanced search
Wolters Kluwer's UpToDate, a global leader in clinical decision support, is actively prototyping new capabilities through its AI Labs. The aim is to enhance their traditional search function with conversational queries that can understand context and retain information from previous questions (Wolters Kluwer). A key feature of their Enterprise Edition is the ability to surface verbatim passages from the source material directly within the AI-generated answer. This is a crucial trust-building measure, designed to reassure clinicians on the quality and integrity of the underlying evidence (Business Wire).
Dyna AI in EBSCO platforms
EBSCO is integrating Dyna AI across its health platforms to provide natural-language answers at AI speed. The system uses a RAG architecture that draws exclusively from their trusted knowledge bases, DynaMedex® and Dynamic Health (EBSCO). This ensures that answers are grounded in evidence-based content curated for clinicians. A powerful use case has been observed in rural UK trusts, where implementing this technology has reportedly reduced the time-to-answer for complex formulary and medication queries by as much as 40%.
NICE’s position on AI in evidence generation
The National Institute for Health and Care Excellence (NICE) is a key voice in ensuring that any new technology is adopted safely and effectively in the UK. In a recent position statement, NICE outlined clear methodological standards for the use of AI in the generation of evidence, particularly in the screening and reporting phases of systematic reviews (NICE). Their core recommendation is to pilot AI-augmented systematic reviews under close human oversight, signalling a cautious but clear endorsement of AI's potential to accelerate evidence synthesis when governed correctly.
TripDatabase AskTrip: instant, referenced Q&A
The well-regarded Trip Medical Database has entered the generative AI space with its AskTrip feature. This tool is designed to return evidence-linked answers to clinical questions in under 30 seconds. It operates on a freemium model, offering 3 free queries per month, with unlimited access for Pro users (LinkedIn, Trip). A key feature of AskTrip is its robust filtering capability, which allows clinicians to narrow results by clinical area and evidence quality, ensuring both relevance and rigour in the answers provided.
Future directions & considerations
The clear trend is towards deeper integration of these RAG-based tools into the core clinical workflow. The next wave of innovation will likely focus on single-sign-on capabilities, seamless EHR integration, and the creation of clear audit logs for clinical governance. However, significant challenges remain. Addressing potential algorithmic bias, ensuring true explainability of AI-generated answers, and navigating data governance under MHRA and NHS guidelines are critical for responsible and widespread adoption.
This trend of AI-powered knowledge retrieval validates the approach iatroX has taken from the start. By focusing on a curated, UK-specific knowledge base and a transparent, RAG-based architecture, we are committed to delivering fast, traceable, and reliable answers that meet the unique needs of frontline UK clinicians.