Research.
PriCareHub did not begin with a product specification. It began with a research programme on what actually goes wrong when clinical AI meets primary care.
Seven Design Patterns for Clinical AI
An architectural framework for how clinical AI systems typically fail in real deployments. The seven patterns are the structural backbone of how we think about testing.
Cornell University arXiv
DOI: 10.48550/arXiv.2604.01661
The Cost of Ontological Drift
An economic analysis estimating the annual cost of clinical-AI misclassification in European primary care at EUR 8 to 26 billion. The empirical case for institutional testing infrastructure.
Cambridge Open Engage
DOI: 10.33774/coe-2026-z01rv
https://www.cambridge.org/engage/coe/article-details/69ce72fb4770e67d92957a68
Adversarial Ontology Taxonomy
The first systematic taxonomy of how clinical AI systems can be attacked through their underlying classification structures. Defines the attack surface every healthcare-AI testing programme has to cover.
Cambridge Open Engage
DOI: 10.33774/coe-2026-9kpzm
https://www.cambridge.org/engage/coe/article-details/69d6213ed1922e37d545c760
The Symptom and the Billing Code
How a clinical observation is silently transformed as it passes through layers of administrative coding and why this matters for any AI trained on that data.
Elsevier SSRN
DOI: 10.2139/ssrn.6475038
Artificial Epidemiology
The uncertainty a clinical AI inherits from training-data choices made before it ever saw a patient. Why static benchmarks are not enough.
Cambridge Open Engage
DOI: 10.33774/coe-2026-ssm1q
https://www.cambridge.org/engage/coe/article-details/69ca3557d1922e37d5096bf4
Preserve, Don't Prune
How clinical AI loses signal when rare diagnostic patterns are pruned from training data and a method for preserving them through structured dormancy rather than deletion.
Springer Nature Research Square
DOI: 10.21203/rs.3.rs-9321746/v1
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