PathAI Funding History

PathAI develops AI algorithms and digital pathology platforms used in diagnostics and biopharma research.

Company Overview

PathAI logo

PathAI

  • Healthcare

Founded 2016 · Boston, Massachusetts, US

Official Website

PathAI Funding Timeline

  1. Announced May 18, 2021

    Series C

    US$165 million

    PathAI said the funding would speed product development and support its work with pharmaceutical and diagnostic partners.

    Led by D1 Capital, Kaiser Permanente Ventures.

    Source · pathai.com ↗
  2. Announced Apr 17, 2019

    Series B

    US$60 million

    The initial Series B closing preceded strategic investments from LabCorp and other life-sciences companies later that year.

    Led by General Atlantic, General Catalyst.

    Source · generalatlantic.com ↗
  3. Announced Nov 15, 2017

    Series A

    US$11 million

    PathAI said the financing would support its deep-learning tools for pathology and help it grow the team.

    Led by General Catalyst. Participation included 8VC.

    Source · fiercebiotech.com ↗

PathAI Founding Team

PathAI’s founding team combined clinical pathology and computer vision. The company applies those disciplines to the analysis of digitized tissue samples. The founding team included Aditya Khosla and David Klimstra.

  • Andrew Beck — CEO. MD-PhD pathologist formerly at Beth Israel Deaconess and Harvard.

PathAI Products

  • AISight

    Manage digitized tissue slides and analyze them in a pathology workspace.

PathAI Company Development

Tissue Analysis Enters the Digital Pathology Workflow

PathAI’s early work applied deep learning to pathology images. It extended that work into a platform for viewing and managing images in clinical and research workflows. Roche’s proposed acquisition puts the combination of workflow software and pathology algorithms at the center of the deal.

PathAI Competitive Landscape

Tissue Slides and Radiology Exams

PathAI builds tools for digital tissue slides used in pathology and biopharma. HOPPR develops infrastructure for medical imaging models. A hospital may evaluate both under an imaging AI strategy, but slide interpretation and radiology model development involve different images, workflows and users.