Identify Your Responders Before Trial Ends
The only AI platform that predicts patient response trajectories and benchmarks outcomes to published trials. Built by a physician. Used by elite clinical researchers to stratify cohorts, guide interventions, and accelerate discovery.
Predict patient outcomes at the next visit. Identify rapid responders, partial responders, non-responders. Benchmark individual trajectories against published clinical trials.
Automatically cluster patients into clinically meaningful subgroups. Understand heterogeneous treatment response patterns. Identify protocol violations and unexpected outcomes early.
Predict trial success before completion. Detect enrollment barriers. Quantify responder rates for sponsors. Generate real-world evidence that pharma uses for regulatory decisions.
$299/month, rate locked for life. The cost of one hour of research coordinator time.
How It Works
Step 1
Drop PDFs, EHR exports, or plain text. NeuraLog AI handles format variability across clinical systems.
Step 2
PHQ-9, HAM-D-17, MADRS, YMRS — each extraction source-cited to the exact sentence, confidence-scored.
Step 3
k-means clustering reveals patient responder archetypes across the full longitudinal course.
Step 4
Methods section, participant table, and clinical interpretation — ready for PI review in seconds.
Step 1
Drop PDFs, EHR exports, or plain text. NeuraLog AI handles format variability across clinical systems.
Step 2
PHQ-9, HAM-D-17, MADRS, YMRS — each extraction source-cited to the exact sentence, confidence-scored.
Step 3
k-means clustering reveals patient responder archetypes across the full longitudinal course.
Step 4
Methods section, participant table, and clinical interpretation — ready for PI review in seconds.
Step 1
Drop PDFs, EHR exports, or plain text. NeuraLog AI handles format variability across clinical systems.
Step 2
PHQ-9, HAM-D-17, MADRS, YMRS — each extraction source-cited to the exact sentence, confidence-scored.
Step 3
k-means clustering reveals patient responder archetypes across the full longitudinal course.
Step 4
Methods section, participant table, and clinical interpretation — ready for PI review in seconds.
From the Field
“The trajectory clustering showed me four patient subgroups I hadn't seen in two years of manual analysis.”
— Physician-Scientist, Academic Medical Center
“The methods draft alone saves me 3-4 hours per paper.”
— Assistant Professor, Neurology
“Finally a tool that knows the difference between HAM-D-17 and HAM-D-21.”
— Clinical Researcher, Psychiatry
Platform Capabilities
Each module replaces a distinct staff function in the NIH-funded research lab — without sacrificing reproducibility, source citation, or audit readiness.
Replaces: Research Coordinator
Extracts PHQ-9, HAM-D-17, MADRS, YMRS, and 40+ validated scales from unstructured clinical notes. Source-cited to the sentence level with confidence scoring.
Open extractor →Replaces: Postdoctoral Researcher
Interactive timeline visualization of medication changes, diagnosis evolution, and scale trajectories across visits. Full audit trail, click-through to source.
Open browser →Replaces: Biostatistician
Generates publication-ready methods paragraphs from extracted cohort data — participant characteristics, measurement instruments, visit cadence, and data provenance.
Open writer →Replaces: Medical Writer
Drafts Results and Discussion sections from your extracted dataset. NEJM-caliber clinical language. Structured for rapid PI review and revision.
Open generator →Replaces: Grants Manager
Generates Specific Aims, Research Strategy, and Significance sections from your cohort data — calibrated to NIH review criteria and formatted for rapid submission.
Open writer →Replaces: Regulatory Affairs
Generates consent form amendments, adverse event narratives, and IRB progress reports directly from extracted clinical data and protocol specifications.
Open builder →The Problem
Clinical notes contain PHQ-9 scores, medication changes, and diagnoses — buried in free text, inconsistently formatted, and inaccessible to analysis.
Research coordinators spend hundreds of hours per cohort copying numbers from PDFs into spreadsheets. It is slow, error-prone, and not reproducible.
When a collaborator asks "how did you get this HAM-D score?" you cannot easily point to the source. Reproducibility suffers and IRBs notice.
Provenance
I built NeuraLog AI because I have spent hundreds of hours manually extracting data from clinical notes for research — copying HAM-D scores, tracking medication changes, flagging severity bands, all by hand from PDFs. There had to be a better way.
Every extraction is tied to the exact sentence it came from. Every score carries a confidence rating. Every dataset is fully auditable — because that is what R01 data deserves.
Pricing
Simple, transparent pricing. Cancel anytime.
Founding Researcher
Only 7 founding spots remaining
Researcher
Lab
Institution
From $3,000/month