Peer-reviewed clinical AI research
We bridge the gap between complex algorithmic research and frontline clinical utility. Our open-source models undergo rigorous validation to ensure safety, equity, and reliability in low-resource public health settings.
Peer-reviewed publications
Our research is published in leading medical and machine learning journals. We focus on algorithmic equity, clinical validation, and explainable AI models designed for real-world healthcare deployment.
Algorithmic equity in diagnostic triage
Explainable AI for clinical triage
An empirical evaluation of clinical AI performance across diverse demographic groups in under-resourced NHS trusts, demonstrating robust model generalisation and bias mitigation strategies.
A validated framework for deploying interpretable deep learning models in community clinics, ensuring healthcare workers can verify model recommendations in real-time.
Technical whitepapers
We publish detailed implementation guides, ethical frameworks, and clinical validation protocols to support the safe integration of artificial intelligence into public health systems.
Algorithmic safety and governance
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Our comprehensive guide outlines the mathematical and clinical standards used to evaluate model safety, data privacy, and demographic equity before deployment in active healthcare settings.
Of our validation protocols, clinical trial datasets, and algorithmic architectures are peer-reviewed and publicly accessible.
Low-resource deployment protocols
Technical specifications for running lightweight, high-accuracy diagnostic models on mobile devices and tablet computers in clinics with limited internet connectivity.
Open-source repositories
Clinical AI must operate as a public good. All our codebases, model weights, and validation datasets are open-source and hosted publicly for global scientific collaboration.
Reproducible clinical science
By sharing our complete pipeline—from raw data preprocessing to final model validation—we enable independent researchers and NHS trusts to verify, audit, and deploy our tools safely.