Harrison.ai develops AI-driven diagnostic solutions using deep learning to enhance the success rates of in vitro fertilization (IVF) and improve healthcare capacity. By automating medical data analysis, the company addresses the growing demand for efficient clinical diagnosis and treatment in a strained healthcare system.
Funding
$225.9M 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
Healthcare systems face increasing strain due to rising demand and skills shortages, leading to inequality and capacity issues in clinical diagnosis and treatment. This is further compounded by the increasing volume of medical images and the psychological burden on medical professionals.
Solution
Harrison.ai develops AI-powered diagnostic support and workflow solutions for radiology and pathology, aiming to scale global healthcare capacity. Their solutions, including Annalise.ai and Franklin.ai, are designed to assist clinicians in making faster and more accurate diagnoses, which can lead to earlier detection of critical conditions. The company's AI models are trained on diverse, real-world clinical data and are designed to integrate into existing clinical workflows. Harrison.ai's solutions can prioritize critical cases, provide a second set of eyes for clinicians, and support draft report generation.
Target Audience
The primary target audience includes radiologists, pathologists, and other clinicians in hospitals and imaging networks seeking to improve diagnostic accuracy, efficiency, and patient outcomes.
Features
- AI-driven diagnostic support for both radiology (Annalise.ai) and pathology (Franklin.ai)
- Harrison.rad.1: A radiology-specific multimodal large language model trained on millions of DICOM images and radiology reports
- Annalise.ai Critical Care AI: Triage solution for chest X-rays and non-contrast head CTs, identifying up to 12 urgent findings
- Annalise Enterprise CTB: Detects up to 130 findings in non-contrast head CT studies
- Annalise Enterprise CXR: Detects up to 124 chest X-ray findings
- Integration with existing radiology and hospital systems
- Prioritization of critical cases based on AI findings
- Support for draft report generation
- Algorithms trained by fully qualified radiologists with over 280 million labels