Clinical research collaboration
Define the target disease or illness, clinical question, endpoints, covariates, available data, feasibility, and study structure.
Professional services
End-to-end clinical research collaboration
Healthcare WithAI partners with physicians and institutions that have clinically meaningful questions but limited time or AI/ML bandwidth.
Collaboration
Engagement flow
1. Discovery
2. Data Review
3. Modeling
4. Validation
Model discovery and validation
Run EnsembleMDs, evaluate candidate models, select robust champions, construct weighted blenders, and quantify model reliability.
Interpretation and reporting
Generate explainable model summaries and physician-reviewed WithAI CareReport™ outputs for clinician-facing and patient-friendly communication.
Publication and deployment
Support analysis summaries, figures, tables, manuscript drafting, publication workflows, and clinic-ready diagnostic-support pipeline planning.
Example
Example research pipeline: post-operative delirium risk assessment
Post-operative delirium risk assessment can use preoperative clinical variables such as age, procedure context, cognitive screening, and grip strength. In the attached research draft, EnsembleMDs identified a weighted ensemble model and quantified patient-level uncertainty so clinicians can interpret not only predicted risk, but also how reliable the estimate is for each patient.
Any displayed metrics from internal research examples require external validation before clinical or commercial use.
Example report components
Workflow
Engagement flow
01Discovery
02Data Review
03Modeling
04Validation
05Manuscript/Report
06Clinic Deployment
Next step
Bring a clinical question. We will help shape the AI workflow around it.
The best starting point is a narrow, clinically meaningful endpoint and a realistic view of the available data.