Peer-reviewed publications and papers under peer review from Axiomera — on semantic classification, cross-source data harmonization, federated clinical machine learning, and oncology AI. Every paper here is published in full: complete text, figures, results, and a plain-English note on what it means for the platform we build.
Spotting pedestrians in surveillance video reliably, even with occlusion and noise.
Read the full paper →Classifies raw healthcare fields against shared clinical ontologies, adapting to each customer's data.
Read the full paper →Synthesizes mappings between healthcare data standards in both directions.
Read the full paper →Keeps multi-site healthcare data harmonized continuously, repairing drift as sources change.
Read the full paper →Orchestrates the classification, mapping and harmonization models as one system at production scale.
Read the full paper →Operators on a concept Hilbert space that enrich themselves as they classify.
Read the full paper →AI that evolves its own network design to process massive datasets faster.
Read the full paper →Self-healing AI that writes and repairs its own code.
Read the full paper →AI that keeps learning and rewires itself without retraining.
Read the full paper →Predicting 30-day readmissions across 47 institutions without moving patient data.
Read the full paper →A brain-inspired architecture for integrating data at any scale.
Read the full paper →A self-healing clinical data network that harmonizes records and predicts outcomes.
Read the full paper →Turning messy hospital records into clinically validated insights across 492,542 encounters.
Read the full paper →Turning Epic Clarity warehouse records into structured clinical concepts.
Read the full paper →Neuromorphic edge AI for real-time coordination of robot swarms.
Read the full paper →Detecting and identifying people from a partial walking gait.
Read the full paper →Many-worlds, quantum-inspired harmonization for population-scale health data.
Read the full paper →Discovery of a long non-coding RNA that drives liver-cancer growth.
Read the full paper →Reversing drug resistance in advanced lung cancer through epigenetic reprogramming.
Read the full paper →Reading MRIs to grade brain tumors and predict molecular subtype before surgery.
Read the full paper →The mathematics of how clinical data gets mapped and classified.
Read the full paper →A vendor-neutral overlay that makes any two FHIR systems understand each other.
Read the full paper →Forecasting population-scale drug demand by separating true need from realized use.
Read the full paper →Turning aircraft vibration data into fleet-wide predictive maintenance intelligence.
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Everything Axiomera does in production — resolving meaning, binding to standards, harmonizing across sources and clouds — traces back to the work published here. See how it runs on your data.