Multimodal Medical AI Platform

Multimodal AI for Precision Medicine

OmniMedAI connects imaging, pathology, clinical data, radiomics, habitat modeling, multimodal fusion, machine learning, and clinical evaluation into a modular research-grade platform.

Explore Workflow View GitHub
9 Platform modules
4+ Medical data types
AI Modeling workflow
QC Validation focused
OmniMedAI Clinical Research Console Case Ready
MRI / CT ROI Review Mask QC 0.94
Risk Stratification
0.82
1,500+ Radiomics features
WSI Pathology analysis
AUC Model evaluation
MIL Fusion roadmap
Visual Workflow

From raw medical data to validated AI insight.

Explore how OmniMedAI organizes multimodal medical data, feature engineering, model development, validation, and clinical translation into a connected research workflow.

End-to-End Flow

OmniMedAI Platform Workflow

A complete medical AI workflow from data ingestion and preprocessing to modeling, interpretation, reporting, and multi-center collaboration.

Data ingestion to clinical translation Designed for reproducible multimodal medical AI studies
OmniMedAI end-to-end platform workflow
Platform workflow visualization for the OmniMedAI end-to-end medical AI pipeline.
Module Map

OmniMedAI Modular Workflow Map

A module-level view showing how preprocessing, radiomics, segmentation, pathology, habitat, fusion, modeling, and evaluation work together.

Module-level workflow architecture Reusable components for imaging, pathology, fusion, modeling, and evaluation
OmniMedAI modular workflow map
Module workflow visualization for the OmniMedAI architecture.
Clinical AI

AI-Assisted Diagnosis Review

A clinical decision-support scenario where multimodal evidence, imaging review, pathology context, and risk modeling are presented together for expert interpretation.

Clinical review with AI-generated evidence panels Designed for expert-in-the-loop diagnostic and prognostic workflows
OmniMedAI AI-assisted clinical diagnosis scene
AI-assisted clinical diagnosis visualization for OmniMedAI.
Multimodal Analysis

Precision Medicine Research Workstation

A research workstation view for combining radiology, pathology, clinical tables, genomic markers, model outputs, and interpretability results in a single analysis environment.

Radiology, pathology, clinical, and genomic context Built for reproducible biomarker discovery and cohort studies
OmniMedAI multimodal AI research workstation
Multimodal research workstation visualization for OmniMedAI.
Validation

Validation & Clinical Translation

A validation workflow connecting cohort definition, model development, internal testing, external validation, clinical utility, interpretation, and translation-ready reporting.

Internal validation to multi-center external testing Evidence packaging for clinical AI translation
OmniMedAI validation and clinical translation workflow
Validation and clinical translation workflow visualization for OmniMedAI.
Platform Modules

A modular SDK for translational medical AI.

Each module can be used independently or chained into a full multimodal modeling workflow for clinical research and AI validation.

Data Processing

Convert DICOM/NIfTI, normalize images, resample volumes, and prepare ROI data for feature extraction.

Radiomics

Extract reproducible first-order, texture, and shape features from image-mask pairs with PyRadiomics.

Segmentation

Route cases through 2D or 3D ROI segmentation workflows and post-process mask outputs.

Pathology AI

Extract classical image features and deep pathology representations for WSI-driven research.

Habitat Analysis

Compute local radiomics, cluster tumor subregions, and characterize intratumoral heterogeneity.

Modeling and Evaluation

Fuse multimodal features, train baseline models, and summarize predictive performance.

onem_process preprocess
onem_radiomics features
onem_segment ROI
onem_path pathology
onem_habitat heterogeneity
onem_torch deep AI
onem_fusion fusion
onem_modeling modeling
onem_eval validation
Research Pipeline

Designed for reproducible clinical AI studies.

OmniMedAI organizes medical AI development into auditable stages: preprocessing, feature engineering, modeling, validation, and translation.

01

Prepare Cohorts

Standardize images, masks, pathology slides, clinical tables, and patient identifiers.

02

Extract Features

Generate radiomics, pathomics, habitat, and deep features with documented settings.

03

Build Models

Fuse modalities, train baseline models, and preserve modeling-ready feature tables.

04

Validate Evidence

Report AUC, F1, calibration, external validation, interpretation, and clinical utility.

Applications

Built around real medical AI research needs.

The platform is intended for translational research, biomarker discovery, clinical prediction modeling, and multi-center validation.

Oncology Imaging

MRI/CT radiomics for molecular status prediction, lymph node risk, and treatment response studies.

Digital Pathology

Feature extraction and deep representations for histology-driven model development.

Habitat Biomarkers

Intratumoral heterogeneity mapping through local features and clustering-based subregions.

Clinical Translation

Evaluation workflows for external validation, reporting, and privacy-preserving collaboration.

Roadmap

From modular toolkit to clinical AI operating layer.

Current work focuses on stabilizing the SDK foundation while extending modeling, evaluation, and collaboration capabilities.

Near Term

  • Unified dependency management and reproducible demos.
  • Stable CLI entry points for common radiomics and modeling workflows.
  • Cross-validation, feature selection, calibration, and confidence intervals.
  • Improved documentation for pretrained segmentation weights.

Platform Extensions

  • Clinical table processing and genomic feature ingestion.
  • Survival modeling, MIL fusion, and attention-based multimodal fusion.
  • Experiment tracking, model versioning, and report generation.
  • Federated learning and privacy-preserving multi-center validation.
Contact

Build validated medical AI workflows with OmniMedAI.

For professional or collaboration inquiries, contact the OmniMedAI team. The GitHub repository and email channel are kept unchanged for continuity.

Contact Email GitHub