Verisium is a medical evidence intelligence platform that helps healthcare organizations extract, analyze, and act on clinical evidence. The platform transforms unstructured medical data into actionable insights, enabling faster, more informed clinical decisions. It supports evidence-based workflows across research, regulatory, and clinical care settings.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare organizations struggle to keep pace with the rapidly growing volume of medical literature, clinical trial data, and real-world evidence. Manually reviewing and synthesizing this information is time-consuming, error-prone, and often results in delayed or incomplete clinical decisions.
Solution
Verisium provides a medical evidence intelligence platform that automates the collection, analysis, and interpretation of clinical evidence. The platform uses advanced natural language processing and machine learning to extract key findings from research papers, trial results, and patient data, then organizes them into structured, searchable insights. Clinicians and researchers can query the system to surface relevant evidence, compare outcomes, and track changes in medical knowledge over time. This enables faster literature reviews, more robust regulatory submissions, and evidence-based care decisions supported by up-to-date data.
Target Audience
Primary users are clinical researchers, medical affairs teams, and healthcare providers who need reliable, current evidence to support research, regulatory, and patient-care decisions.
Features
- AI-powered extraction of study endpoints, patient populations, and treatment outcomes from unstructured medical documents
- Semantic search across full-text articles, abstracts, and clinical trial registries
- Automated evidence grading and quality assessment based on study design and methodology
- Longitudinal tracking of evidence trends and emerging treatment patterns
- Exportable summaries and citation-ready reports for research and regulatory use
- API access for integration into electronic health records and clinical decision support systems