About GUIDE-AI
Understanding the challenge, the project, and the scientific approach behind AI-powered guideline navigation.
The problem with clinical guidelines
Clinical practice guidelines represent the gold standard of evidence-based medicine. Produced by leading professional societies like ESC, KDIGO, GOLD, GINA, and ECCO, they synthesise thousands of studies into recommendations, designed to improve patient outcomes.
Yet, applying guidelines is far from straightforward, given their length, frequent updates, and time constraints in clinical practice. The result is a variation in guideline-directed care. The combination of individual patient data with guidelines and the role of AI in that process are active areas of research. Decision points in guidelines may contain ambiguity on purpose where sufficient evidence is not available. Our consortium aims to integrate AI into shared decision-making between physicians and patients along guideline-directed care paths.
GUIDE-AI is coordinated by Charité – Universitätsmedizin Berlin and brings together 19 partner institutions across 8 countries in order to directly address this challenge.
Project at a Glance
| Full name | GUIDE-AI |
| Funder | IHI Joint Undertaking |
| Call | IHI Call 9 · Topic 3 |
| Grant No. | 101253015 |
| Budget | €9.5 million |
| Duration | 2025 – 2029 (4 years) |
| Coordinator | Charité – Universitätsmedizin Berlin |
| Partners | 19 institutions · 8 countries |
| Disease areas | HFrEF · CKD · COPD · Asthma · IBD |
| Status | Active (link to IHI factsheet on click) |
Project Objectives
GUIDE-AI targets five interconnected objectives spanning technology development, clinical translation, and scientific dissemination.
Build validated Guideline Navigators
Develop and benchmark LLM-based Guideline Navigators for HFrEF, CKD, COPD, and asthma, capable of mapping patient data to guideline recommendations.
Empower patients and doctors through AI-guided care
Build AI tools that help patients and healthcare providers make treatment decisions together, making it easier to follow the latest clinical guidelines.
Demonstrate clinical safety and effectiveness
Execute a rigorous, inclusive and transparent prospective clinical study at two EU sites. Evaluation spans inpatient and outpatient settings across the complete treatment pathway.
Address ethics, safety, and regulation
Develop a framework for AI safety in clinical guideline applications, addressing hallucination risk, bias, explainability requirements, and the EU AI Act classification of GUIDE-AI tools.
Maximise scientific and societal impact
Publish findings in open-access journals, share validated benchmarks and datasets, engage patient organisations and policymakers, and establish a sustainability pathway for the Guideline Navigator beyond the project lifetime.
How GUIDE-AI works
A four-layer architecture connects clinical knowledge, AI reasoning, safety assurance, and clinical integration.
Guideline Knowledge Base
Clinical practice guideline recommendations from international and national professional societies are used as the knowledge base and context for the GUIDE-AI navigators.
LLM Adaptation & Alignment
Retrieval-Augmented Generation (RAG) and advanced Graph-RAG architectures ensure answer fidelity and cross-guideline reasoning. Navigators compare prescribed therapies against guideline recommendations and alert clinicians to deviations from evidence-based protocols.
Safety & Explainability Framework
Every model output is evaluated by an automated safety layer that checks source traceability, detects conflicting recommendations, and applies uncertainty quantification. Clinician-readable explanations accompany each answer.
Clinical Integration & Evaluation
Guideline Navigators are deployed in Electronic Health Record (EHR) environments at partner clinical sites. Prospective studies measure adherence outcomes, clinician trust, and workflow integration quality.
Empowering Clinical Judgment
In complex medical decisions, guidelines often present a broad spectrum of valid, competing options rather than a single definitive path. Instead of forcing automated recommendations in ambiguous scenarios, our Guideline Navigators systematically map these clinical and structural ambiguities. By distinguishing between clear evidence-based standard care and areas requiring nuanced judgment, the system reduces cognitive friction while preserving the physician's essential role in shared decision-making.