Research Platform

Empowering Pathologists, Microbiologists, and Researchers to address growing cancer healthcare challenges. We increase the speed, analytical capacity, and consistency of exploratory data analysis.

About

We enable better, faster, and more informed research discoveries by analyzing, interpreting, linking, and validating diverse data sources relevant to cancer detection models.

A cloud-based software solution specifically designed to bring the benefits of Generative AI, Knowledge Engineering, and Digital Pathology to pathologists, research laboratories, and teaching hospitals.

Platform Components

Control Center

A centralized platform to manage, access, research, retrieve, and document evaluation cases at any time.

Data Manager

Continual enrichment of analytical datasets, seamlessly aggregating data from the Human Genome project, the NIH, and proprietary research systems.

Report Manager

Generates comprehensive and coherent Research Reports and data summaries of a given medical image.

Image Annotations
Scheduled Compilation
Ad-Hoc Reporting

Workflow Manager

Minimizes lost administrative time, increases operational efficiency, optimizes study analysis, and significantly increases laboratory analytical capacity.

Ecosystem Integration Partners

System-agnostic architecture built to interface directly with laboratory microscopes and external research tools via secure APIs.

Health Care Providers Partner Network

Configured to securely ingest de-identified datasets from Hospital Management Systems (including patient history contexts) and multi-department image records—such as cytology, microbiology, urinalysis, and pap smears—for deep retrospective validation and analytical tracking.

Knowledge Engine

Dynamic exploration and discovery of diverse data sources relevant to multi-dimensional research problems. We utilize pre-trained knowledge architecture to help achieve greater analytical efficacy, reliability, and generalization across diverse exploratory domains using fully de-identified data.

Foundational Model

Built upon secure, Federated Multimodal Large Language Model (MMLLM) learning architectures, providing detailed data enrichment, collaborative sharing, and secure cloud-based storage with 24/7 access control. This environment allows real-time, remote research collaboration for authorized experts on any internet-enabled device.

Our foundational model provides a solid base for various machine learning tasks by serving as the building blocks for more specialized models. MMLLM models integrate textual and visual data from pathology images, enhancing diagnostic capabilities and treatment recommendations. Our federated learning approach ensures data privacy and model security across distributed healthcare systems.

By exploring complex data from diverse sources (including real-world evidence, structured databases, unstructured sensor feeds, and external data streams), we uncover valuable insights from sources that were once invisible to conventional analytical methods.

Our exploratory ecosystem uniquely grows and improves by leveraging previous analytical models, expanding the scope of discovery, and increasing evidence quality.

Learn how genomics, Knowledge Engineering, and our digital twin architecture can precisely delineate the molecular mechanisms of diseases, characterize potential disease progression parameters, optimize exploratory research models, and facilitate the swift identification of novel therapeutic pathways.

The ROSEAId Research Platform enhances analytical decision-making, accelerates the research pipeline, and improves data visibility to ensure no crucial piece of information is overlooked in the global pursuit of cancer solutions.

ROSEAId Research Platform

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