NeuraLog AI does the work of your entire lab support team — so you can focus on the science only you can do.
Join physician-scientists on the waitlist. Free during design partner phase.
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.
View demo →Replaces: Postdoctoral Researcher
Interactive timeline visualization of medication changes, diagnosis evolution, and scale trajectories across visits. Full audit trail, click-through to source.
View demo →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.
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.
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.