Utilizes deep learning algorithms to analyze histopathological images for abnormalities and patterns.
Supports various digital pathology image formats including SVS, TIFF, JPEG, and DICOM.
Enables pathologists to share cases, annotate images, and discuss findings in real-time with team members.
Adheres to healthcare regulations like HIPAA and GDPR with encrypted data storage and transfer.
Allows users to train and deploy custom AI models using their own datasets for specialized applications.
Generates detailed reports with visual annotations and exports to formats like PDF, CSV, and integration APIs.
Integrates with laboratory information systems (LIS), electronic health records (EHR), and other healthcare software.
AI helps identify cancerous cells in biopsy samples, improving diagnostic accuracy and reducing oversight.
Automates the grading of tumors based on histopathological features, ensuring consistency across evaluations.
Used as a training tool for medical students and residents to learn pathology through AI-assisted case studies.
Facilitates large-scale analysis of pathology images for clinical trials and research studies, accelerating insights.
Ensures consistency in pathology reports by cross-verifying AI and human analyses for quality assurance.
Enables remote diagnosis by sharing AI-analyzed images with experts, improving access to specialist opinions.
Assists in evaluating tissue responses to new drugs in clinical trials, aiding in efficacy and safety assessments.
Analyzes margins in surgical specimens to ensure complete tumor removal, supporting surgical decisions.
Identifies pathogens in tissue samples, aiding in the diagnosis and management of infectious diseases.
Uses AI to correlate pathology findings with patient outcomes for tailored treatment plans and prognostics.
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