1. Hypothesis
Frame a testable clinical or operational question with measurable endpoints.
Path 2 • AI Research Accelerator
The In-Silico Investigator
White-label learning flow for clinicians who want to do rigorous research with AI assistance, not AI hype.
Research Pedagogy Loop
Teach methodology first, then use AI as an accelerator with clear evaluation checkpoints.
2. Methodology
Define cohort, inclusion criteria, confounders, and statistical guardrails.
3. Data
Prepare tabular, note, and literature data while maintaining governance discipline.
4. Experiment
Run AI-assisted experiments and benchmark against baseline methods.
5. Conclusion
Report findings with limitations, reproducibility notes, and deployment implications.
Experiment Lab Module
The Predictor (Regression)
Estimate continuous outcomes such as length of stay or recovery time.
Experiment Lab Module
The Diagnostician (Classification)
Classify notes into clinically meaningful categories with confidence signals.
Experiment Lab Module
The Reader (NLP Synthesis)
Summarize long papers and extract structured evidence quickly.
How The AI Tutor Works (Clinician View)
This is the teaching sequence we expose to reduce AI anxiety and build infrastructure fluency.
Prompt
Your clinical question is converted to machine-readable tokens.
Embedding
Question meaning is encoded into vectors for semantic retrieval.
Retrieval
Relevant syllabus/manual chunks are fetched from Vectorize.
Generation
Groq model composes an answer constrained by retrieved context.
Review
You validate evidence, reasoning quality, and fit for patient-facing use.