HistAI offers an AI-powered digital pathology platform for collaborative research and education. It provides a whole slide image viewer with real-time collaboration tools and integrated task management, alongside specialized AI widgets for automated image analysis.
Funding
$1K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Pathology research and education often face limitations due to the lack of accessible, collaborative digital tools and specialized AI for image analysis. This hinders efficient workflow, knowledge sharing, and the development of advanced diagnostic capabilities.
Solution
HistAI provides an AI-powered digital pathology platform designed to facilitate collaborative research and education. The platform offers a robust whole slide image (WSI) viewer, enabling detailed examination of pathology slides. It integrates unlimited collaboration features, allowing multiple users to analyze, annotate, and discuss findings in real-time. Task management tools streamline project workflows and progress tracking within research teams. Furthermore, HistAI incorporates a growing library of specialized AI widgets for automated image analysis, enhancing the efficiency and depth of pathological assessments. The platform is built with HIPAA compliance to ensure secure handling of sensitive patient data.
Target Audience
The primary users are researchers, educators, and institutions in the fields of pathology and medical imaging who require advanced digital tools for collaborative analysis and AI-driven insights.
Features
- Whole Slide Image (WSI) viewer supporting various OpenSlide and cuCIM compatible file formats.
- Real-time, multi-user collaboration suite for simultaneous slide analysis, annotation, and commenting.
- Integrated task management system for workload distribution and progress monitoring.
- Library of AI-powered widgets for automated image analysis, including:
- Full skin tissue segmentation with classification of 22 morphologies.
- Colorectal tissue segmentation identifying 12 morphologies.
- Thorax tissue segmentation classifying 14 morphologies.
- Specialized AI model for melanoma metastasis identification in lymph nodes.
- Cloud-based architecture for accessible, remote research and educational activities.
- HIPAA-compliant data handling and storage for secure research operations.
- Open-source foundation models and datasets available via Hugging Face for broader research application.
- WSI labeling tools with multi-session annotation capabilities and responsive interface optimization for various devices.
- Interoperable annotation export in universal JSON format.