PathAI

PathAI builds AI-powered pathology tools for diagnostic labs and pharmaceutical companies, analyzing digitized tissue slides to detect cancer and grade disease severity. $255M total funding; $165M Series C co-led by D1 Capital and Kaiser Permanente. Partnerships with LabCorp, GSK, and BMS. Revenue model split between diagnostics software and pharma R&D services.

PathAI is the defining company in AI pathology — a subsector that digitizes the last major analog diagnostic modality in medicine — with $255M raised and partnerships with LabCorp and major pharma.

pathai.com Pathology AI

United Statesprivate

At a glance

Total raised

$251M

last round series c

Headcount

201-500

approximate · public range

Headquarters

Boston

United States

Founded

2016

10 years active

Status

Private

Pathology AI

Patents on file

USPTO grants

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Funding history

Total raised

$251M

Rounds tracked

Latest stage

Series C

Date	Stage	Amount	Valuation	Lead investors
May 19, 2021	Series C	$165M	—	D1 Capital Partners, Kaiser Permanente
Apr 17, 2019	Series B	$75M	—	General Atlantic, General Catalyst
Nov 3, 2017	Series A	$11M	—	General Catalyst

Founder backgrounds

Aditya Khosla

Co-founder & former Chief Technology Officer

Khosla earned a BS in computer science and electrical engineering from Caltech, an MS from Stanford, and a PhD in computer vision and machine learning from MIT under Antonio Torralba. His doctoral work focused on large-scale visual recognition and deep learning interpretability, including co-authoring a paper on deep learning for identifying metastatic breast cancer that directly anticipated PathAI's diagnostic focus. He also co-created the Places image database for scene understanding. Before founding PathAI in 2016 alongside Andy Beck, he held research internships at Microsoft Research and eBay. As CTO, he built and led PathAI's engineering, ML, product, and recruiting teams through its growth into a leading AI pathology platform.

Andrew H. Beck (Andy Beck)

Co-founder & Chief Executive Officer

A Brown University-trained pathologist (BA in history, MD) and Stanford PhD in biomedical informatics (2013, advised by Daphne Koller), Beck built one of the first machine-learning systems for cancer pathology during his doctorate. Before co-founding PathAI in 2016, he was an Associate Professor of Pathology at Harvard Medical School and Beth Israel Deaconess Medical Center, authoring over 110 papers on cancer biology and computational pathology. Beck routinely describes himself as "mediocre in two things" — medicine and machine learning — "but being right at the interface of those two things," a framing that underscores his career spent bridging clinical pathology with AI.

AI bull / bear

Bull case

PathAI occupies the pole position in a market that is finally tipping. Pathology remains the last major analog holdout in diagnostics, and the infrastructure for whole-slide imaging is now reaching critical mass in US labs. With $255M in total funding and a $165M Series C co-led by Kaiser Permanente, PathAI has both the balance sheet and a built-in deployment channel into one of America's largest integrated health systems. The dual revenue model de-risks the business: pharma R&D contracts with GSK and Bristol Myers Squibb generate near-term cash while algorithmic diagnostics build toward a recurring software revenue stream. LabCorp's partnership signals that scaled commercial labs see PathAI as the standard-bearer, not a vendor to be swapped out. As FDA frameworks for digital pathology AI mature, PathAI's first-mover data moat — built across millions of annotated slides — becomes nearly insurmountable for late entrants.

Bear case

PathAI's last funding round closed in May 2021. Nearly four years without fresh capital, in a sector where competitors continue raising aggressively, raises hard questions about valuation, burn rate, and growth trajectory. The clinical diagnostics path remains gated by slow FDA clearance timelines and conservative pathologist adoption; most revenue today likely skews toward pharma services, a lumpier and less scalable business than the diagnostic platform narrative suggests. Paige.AI already secured FDA authorization for prostate cancer detection, establishing a regulatory lead that PathAI must now chase. LabCorp and Quest Diagnostics wield enormous buyer power and could internalize AI capabilities through acquisition or in-house development. Worse, whole-slide imaging adoption in community hospitals — where most biopsies are read — remains low, capping the addressable market. A winner-take-most outcome is far from assured, and the burn rate required to maintain data and algorithm leadership may outpace revenue growth for years.

Generated by deepseek-v4-pro · May 2, 2026Refreshes quarterly · see disclosures

In the news

Watch

Leadership

Andrew H. Beck (Andy Beck)

Co-founder & Chief Executive Officer

Aditya Khosla

Co-founder & former Chief Technology Officer

—

Brandon Eldredge

Chief Financial Officer

—

Elizabeth (Liz) Storti

Chief People Officer

—

Eric Walk, MD, FCAP

Chief Medical Officer

—

Tiffany Freitas

Chief Operating Officer

—

Matt Grow

President of Biopharma & Chief Business Officer

—

Products

AISight Dx
AISight Dx is PathAI's FDA-cleared, cloud-native digital pathology image management system designed for primary diagnosis in clinical settings. It centralizes case and image management, streamlines diagnostic workflows through intelligent case management and high-performance slide review, and enables precision collaboration among pathologists. The platform seamlessly integrates AI algorithms directly into the slide viewer for case prioritization, automated assignment, assisted reporting, and quality assurance, making it a single digital workplace for modern pathologists in anatomic pathology labs.
AISight Clinical Trials
AISight Clinical Trials is a secure, cloud-based SaaS platform that enables biopharma companies and clinical research organizations to deploy AI-powered algorithms and facilitate digital pathology reads in prospective and retrospective clinical trials. It supports eligibility and stratification use cases, response monitoring, quality control, and exploratory analysis with highly configurable workflows. The platform allows maximum flexibility for leveraging digital pathology in clinical research, helping accelerate drug development through AI-assisted tissue analysis.
AIM Biomarker Quantification Suite
The AIM Biomarker Quantification Suite is a collection of AI-powered algorithms for automated, reproducible biomarker scoring in tissue samples. Products include AIM-PD-L1 for PD-L1 scoring and the AIM-IHC Breast Panel for breast cancer biomarker assessment. These algorithms deliver rapid, accurate, and consistent quantification of immunohistochemistry biomarkers, helping pathologists and researchers improve diagnostic consistency and accelerate drug development workflows through objective, standardized analysis of digitized tissue slides.
PathAssist Research Tool Suite
PathAssist is PathAI's AI-assisted research tool suite that streamlines pathology research workflows. It includes PathAssist Derm, an AI-based histopathology product specifically designed for dermatopathology research. The suite accelerates skin tissue analysis by automating routine assessments and providing quantitative insights, helping researchers and drug developers analyze complex tissue morphology more efficiently and consistently in preclinical and translational research settings.

Most recent patents

PathAI, Inc.·2025-08-12
Systems And Methods For Assessing Liver Pathology
US12383191
Abstract
Systems And Methods For Assessing Liver Pathology
US12383191
Abstract In some aspects, the described systems and methods provide for a method for training a deep learning model to assess liver pathology, including accessing annotated liver pathology images associated with a group of patients in one or more randomized controlled clinical trials of nonalcoholic steatohepatitis therapy, each of the annotated liver pathology images including at least one annotation describing one or more tissue characteristic categories for a portion of the image, and training the deep learning model based on the annotated liver pathology images to predict the tissue characteristic categories, selected from a group comprising steatosis, lobular inflammation, hepatocyte ballooning, and fibrosis stage.
View on Google Patents
PathAI, Inc.·2024-12-24
Systems And Methods For Classifying Biomedical Image Data Using A Graph Neural Network
US12175666
Abstract
Systems And Methods For Classifying Biomedical Image Data Using A Graph Neural Network
US12175666
Abstract Techniques for classifying biomedical image data using a graph neural network are disclosed. In one particular embodiment, the techniques may be realized as a method for classifying biomedical image data comprising generating an annotated representation of biomedical image data; identifying a plurality of pixel clusters based on the biomedical image data; constructing a graph based on the plurality of pixel clusters; determining at least one biomedical feature for at least one node of the graph based on the annotated representation of the biomedical image data; and processing a graph neural network to classify the biomedical image data based on the at least one biomedical feature.
View on Google Patents
PathAI, Inc.·2024-08-27
Systems And Methods For Training A Model To Predict Survival Time For A Patient
US12073948
Abstract
Systems And Methods For Training A Model To Predict Survival Time For A Patient
US12073948
Abstract In some aspects, the described systems and methods provide for a method for training a model to predict survival time for a patient. The method includes accessing annotated pathology images associated with a first group of patients in a clinical trial. Each of the annotated pathology images is associated with survival data for a respective patient. Each of the annotated pathology images includes an annotation describing a tissue characteristic category for a portion of the image. Values for one or more features are extracted from each of the annotated pathology images. A model is trained based on the survival data and the extracted values for the features. The trained model is stored on a storage device.
View on Google Patents

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PathAI

At a glance

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Funding history

Founder backgrounds

Aditya Khosla

Andrew H. Beck (Andy Beck)

AI bull / bear

In the news

STAT+: Roche to buy PathAI for $750 million

Morning Headlines 5/8/26

Roche to acquire digital pathology firm PathAI for up to $1.05B

Watch

Leadership

Products

AISight Dx

AISight Clinical Trials

AIM Biomarker Quantification Suite

PathAssist Research Tool Suite

Most recent patents

Systems And Methods For Assessing Liver Pathology

Systems And Methods For Assessing Liver Pathology

Systems And Methods For Classifying Biomedical Image Data Using A Graph Neural Network

Systems And Methods For Classifying Biomedical Image Data Using A Graph Neural Network

Systems And Methods For Training A Model To Predict Survival Time For A Patient

Systems And Methods For Training A Model To Predict Survival Time For A Patient

Open roles

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