Our Research Approach
ClinexaOS operates a dedicated clinical AI research division staffed by radiologists, data scientists, and biostatisticians. All models undergo a standardised validation protocol before deployment — including retrospective performance analysis, prospective clinical trials, and independent third-party review.
We measure performance against established clinical reference standards and report all metrics transparently, including sensitivity, specificity, AUC, and calibration curves per specialty and demographic subgroup.
Benchmark commitment: We do not selectively report metrics. Every published study includes full performance tables across all tested subgroups, including cases where our models underperform.
Selected Publications & Studies
Multi-centre validation of AI-assisted chest CT interpretation across 6 hospital systems
Performance of ClinexaOS v2.1 was evaluated across 42,000 chest CT studies in six academic medical centres. Mean AUC: 0.963 for pathology detection.
Reducing radiologist reporting time through structured AI pre-reads: a prospective cohort study
Radiologist reporting time reduced by 38% when using ClinexaOS structured pre-reads, without increase in error rate.
Specialty-aware AI inference: adapting diagnostic language models to clinical subspecialties
Technical methodology paper describing our specialty-context injection architecture and its effect on diagnostic accuracy.
Comparative evaluation of AI confidence calibration in diagnostic imaging tasks
ClinexaOS confidence scores were found to be well-calibrated across modalities, with mean Expected Calibration Error (ECE) of 0.031.
Training Data Methodology
Our 300M+ case training corpus was assembled through partnerships with academic medical centres, national health archives, and curated open datasets. All training data is:
- De-identified using HIPAA Safe Harbour and Expert Determination methods
- Annotated by board-certified specialists with minimum 5 years clinical experience
- Reviewed for demographic representation and bias mitigation
- Subject to ongoing data quality audits and retrospective labelling review
- Sourced under data sharing agreements compliant with applicable law
Research Areas
Pulmonology & Thoracic
Cardiology
Neurology & Neuro-radiology
Musculoskeletal
Pathology & Histology
Ophthalmology
General Diagnostics
Research Partnerships
Six academic medical centre partners across three continents for prospective study access.
Independent third-party model audits conducted annually by certified clinical AI evaluators.