Hybrid Human-Machine Intelligence

Interactive, LLM-guided analysis where clinicians, pathologists, biologists, patients, and students lead and AI follows. We created a Safe Human-AI Research Environment (SHARE) that puts domain experts directly into the analytic workflow. No coding, no waiting, no black boxes.

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⟩ SEE IT IN ACTION

LLM-Guided Analysis Meets Human Expertise

Watch how clinicians, biologists, and students interact with AI in real time to drive discovery.

⟩ SEE IT IN ACTION

LLM-Guided Analysis Meets Human Expertise

Watch how clinicians, biologists, and students interact with AI in real time to drive discovery.

HOW IT WORKS

The SHARE Convergence Loop

Humans guide. AI computes. Safety gates validate. Every cycle produces auditable, trustworthy results.

BY THE NUMBERS

SHARE vs. Traditional Approach on Spatial Transcriptomics Data

Demo on a ~1 TB spatial transcriptomics dataset. What once required months and a dedicated bioinformatics team now takes minutes.

~1 TB

Dataset Size (Demo)

~10s

Per Computation Step

~5s

AI Response Time

~10 min

End-to-End Analysis

	Traditional OLD	SHARE NEW
End-to-end analysis	3–6 months	~10 minutes
Per computation step	Hours to days	~10 seconds
Response to question	Days (email/meeting)	~5 seconds
Who can run it	Bioinformatics experts	Anyone with domain knowledge
Coding required	R / Python / CLI	Zero — natural language
Reproducibility	Manual, undocumented	Built-in audit trail
Speed-up	—	>1,000×

3–6 months

Traditional

Dedicated bioinformatics team, custom scripts, manual QC, months of iteration between analysts and domain experts.

~10 minutes

With SHARE

Anyone with domain knowledge guides the analysis through conversation. LLMs compute, audit trails are automatic, results validated in real time.

What Makes SHARE Different

01

Interactive, Expert-Driven Analysis

SHARE platforms are interactive workspaces where clinicians, pathologists, biologists, patients, and students guide every analysis step through natural language. LLMs handle the computation; experts make the decisions. Complex workflows that once required dedicated bioinformatics teams and months of effort now take minutes to hours, with zero coding.

>1,000×faster discovery

02

Built-in Safety & Trustworthiness

Every AI-generated result is traceable and reproducible before it reaches a clinical decision. Audit trails, evaluation gates, and fairness checks are embedded directly into the workflow. Clinicians, biologists, and students cross-validate AI outputs in real time, so reliability is a default requirement, not a post hoc add-on.

LLM-Guided Platforms

CONVERGE-AI

Interactive RNA-seq and GeoMx analysis through natural language. Months to minutes, no code required.

RNA-seq

SpatialDraw

Pathologist-drawn annotations drive spatial transcriptomics workflows across Xenium, Visium HD, CosMx, MERFISH. No computational expertise needed.

Spatial

L-MAP

Multimodal Alzheimer's analysis integrating imaging, genomics, clinical, and metabolomic data. 0.97 ensemble AUC.

Multimodal

DrugAI

Computational peptide design and molecular docking for Alzheimer's disease and HCV/HBV antiviral therapeutics.

Drug Discovery

SpatialAtlas

Cross-tissue, cross-disease spatial resource unifying data across five major spatial platforms.

Atlas

Custom Platform

Your research idea, our LLM-guided approach. We design and build tailored analysis platforms for your project.

Your Project

Validated Across

🧬

Oncology

CAR-T prediction, tumor microenvironment

🧠

Neurodegeneration

Multimodal Alzheimer's modeling

🦠

Infectious Disease

Antiviral design for HCV/HBV

🔬

Translational

Regenerative medicine, spatial analysis

Let's Talk

Research collaborations, custom platforms, or just curious about SHARE. Open to clinicians, pathologists, biologists, patients, and students.

jinai.llc888@gmail.com (315) 888-1455 Winston-Salem, NC

Wake Forest University School of Medicine · Jin Lab · JINAI L.L.C.