Specialized Clinical AI
Rigorous machine learning engineering, from scientific foundations to real-world deployment.
Model Development & Validation
Custom machine learning architectures built for clinical relevance, interpretability, and regulatory alignment.
Capabilities
- Medical imaging (Segmentation, Classification)
- Clinical risk prediction & stratification
- Wearable sensor & time-series analysis
- Multimodal EHR pipelines
Technical Stack
Full-cycle development from raw data preprocessing to deployed inference endpoints.
Clinical Data Analysis & Epidemiology
Statistical rigor for observational studies, large-scale biobanks, and clinical research programs.
Analysis Types
- Survival analysis & Competing risks
- Causal inference & Confounding adjustment
- Cohort construction & Phenotyping
Values
- Reproducible analysis pipelines
- Publication-ready visualizations
- Transparent methodology
AI Strategy & Advisory
Guidance for clinical groups, startups, and research teams navigating the complexity of medical AI.
Advisory Areas
- Feasibility & Readiness Assessment
- Clinical Validation Strategy
- Study Design & Scientific Review
Why It Matters
Avoiding "AI for AI's sake" in favor of solutions that solve real clinical problems with measurable impact.
How I Work
Every engagement follows a structured, scientific methodology to ensure validity and robustness.
Start a ConversationAudit & Define
Clarifying the clinical question, auditing available data, and defining success metrics.
Design & Assess
Selecting appropriate methodologies, assessing bias, and planning validation strategies.
Build & Validate
Iterative model development with rigorous internal and external validation loops.
Interpret & Deploy
Focusing on explainability, integration, and monitoring for long-term reliability.