Clinical Trial Matching with LLMs and Knowledge Graphs
Clinical trial recruitment is hindered by complex eligibility criteria, making it difficult to match patients to trials.
Technologies: LLMs, Knowledge Graphs, Ontologies (SNOMED CT, RxNorm, MedDRA), EHR Integration
Target Users: Clinical Research Organizations (CROs), Hospitals and Healthcare Providers, Pharmaceutical Companies
Impact: Increases trial enrollment rates, reduces recruitment time, and accelerates drug development.
RAG-Based: No
AI-Driven Clinical Decision Support (CDS) with RAG
Clinicians struggle to access up-to-date medical knowledge, leading to variability in treatment decisions and medical errors.
Technologies: RAG Models, EHR Integration, Explainable AI, Medical Knowledge Bases (UpToDate, PubMed)
Target Users: Healthcare Providers (Hospitals, Clinics), Health IT Companies, Medical Practitioners
Impact: Improves treatment accuracy, reduces medical errors, and enhances patient outcomes.
RAG-Based: Yes
Intelligent Clinical Documentation Assistant (RAG-Powered)
Clinicians spend excessive time on documentation, leading to burnout and reduced patient interaction.
Technologies: RAG Models, EHR Integration
Target Users: Hospitals and Clinics, Health IT Vendors
Impact: Reduces clinician workload, decreases burnout, and increases time for patient care.
RAG-Based: Yes
Personalized Patient Education Tool
Generic patient education materials fail to address individual needs, leading to poor health literacy and treatment non-adherence.
Technologies: RAG Models, EHR Integration, Clinical Guidelines Integration
Target Users: Healthcare Providers, Patient Advocacy Groups, Health IT Companies
Impact: Improves patient understanding, engagement, and treatment adherence.
RAG-Based: Yes