Paige AI is a computational pathology platform that helps pathologists detect cancer in digitized tissue slides with FDA-cleared AI models.
Paige AI was spun out of Memorial Sloan Kettering Cancer Center and trains its algorithms on a vast archive of annotated pathology images. Its flagship detection models flag regions of interest on whole-slide images so pathologists can review the most suspicious areas first. The platform supports both clinical use in digital pathology labs and pharmaceutical partnerships for biomarker discovery. Because several Paige products hold FDA clearance, they can be deployed directly in diagnostic workflows rather than as research-only tools.
Paige sells through enterprise and laboratory agreements, typically priced per case or per seat. Clinical products require a validated deployment, so pricing is quoted per institution.
Hospital pathology departments, reference labs and pharmaceutical teams running tissue-based studies. It is built for institutions moving from glass slides to fully digital pathology workflows.
No. Paige is a clinical-grade product sold to labs and health systems; there is no self-serve free tier.
Yes. Paige provides developer and image-analysis interfaces for partners building on its platform and scanner integrations.
Compared with research-grade pathology AI, Paige stands out for its regulatory clearances and its training data heritage from a leading cancer center.
Pathology labs deploy Paige to triage prostate and breast biopsies, letting AI flag suspicious regions so pathologists confirm findings faster and with fewer misses. Digital pathology initiatives use Paige as the diagnostic layer on top of whole-slide scanners already installed in the lab. Pharma partners apply Paige’s biomarker and tissue analysis to quantify expression patterns across trial cohorts, replacing manual scoring with reproducible measurements. Labs also use the platform for second-opinion review and remote sign-out between sites.
Paige requires a digitized pathology workflow, so glass-slide-only labs must invest in scanners and image management first. Coverage concentrates on high-volume cancer types, and deployment demands validation and change management. Ibex Medical Analytics and PathAI offer competing pathology AI, and scanner vendors such as Leica and Philips provide their own integrated algorithms.
Paige deploys alongside digital pathology scanners and LIS/PACS infrastructure, using standard slide image formats so labs avoid proprietary capture pipelines. Integration work covers image transfer, case synchronization and result reporting back into the laboratory system where pathologists already work. Rollouts typically start with one high-volume indication, run in parallel with existing reads for validation, then expand as confidence builds. The platform is cloud-delivered with enterprise security controls, and lab IT teams coordinate network throughput since whole-slide images are large. Training focuses on interpreting AI overlays and maintaining sign-out discipline, and Paige provides implementation support tied to regulatory requirements for each deployed model.
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