Limbix (by BigHealth)
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Deciphering the language of biology through multi-modal AI and deep clinical insights.
nference is a premier clinical AI platform that synthesizes longitudinal electronic health records (EHRs), digital pathology images, and multi-omics data into structured, actionable insights. By 2026, nference has established itself as the critical infrastructure for federated learning in the biopharmaceutical sector, primarily through its proprietary nSights platform. The architecture leverages advanced Natural Language Processing (NLP) to convert unstructured clinical narratives into high-fidelity phenotypic datasets, enabling researchers to identify novel biomarkers and accelerate drug development timelines. In partnership with elite academic medical centers like the Mayo Clinic, nference provides a secure environment for retrospective and prospective clinical research while maintaining strict data de-identification standards. Its 2026 market position focuses on the 'Total Patient View,' integrating ECG signals, imaging, and genomic data to model disease progression with unprecedented accuracy. The platform is designed for large-scale enterprise use, supporting complex query operations across petabytes of medical data to facilitate precision medicine and real-world evidence (RWE) generation at scale.
A comprehensive environment for querying multi-modal clinical data using natural language and structured filters.
Evidence-based digital therapeutics for adolescent mental health and behavioral activation.
Predictive clinical and operational intelligence to fight death and waste in healthcare.
Predictive medical data and clinical insights for streamlined enterprise underwriting.
The professional medical network for clinicians, providing HIPAA-compliant AI and telehealth solutions.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Transformer-based models trained on billions of clinical notes to extract phenotypic entities with high precision.
Computer vision models designed for large-scale tissue analysis and automated feature extraction from biopsy slides.
Architecture that aligns genomic sequences with clinical outcomes and lab results.
Protocol that allows model training across multiple institutional datasets without moving raw patient data.
Signal processing AI that identifies silent cardiac conditions from standard 12-lead ECG data.
An end-to-end dashboard for designing retrospective studies that meet regulatory standards for submission.
Identifying new indications for existing drugs using real-world data.
Registry Updated:2/7/2026
Generate evidence for Phase II trials.
Evaluating if a patient population exists that meets complex trial criteria.
Finding genetic or phenotypic signatures that predict treatment response.