Stratipath

A single deep learning model is the entire company. Stratipath Breast reads routine hematoxylin and eosin slides and returns a risk classification, with no viewer, laboratory, or assay of its own. The product exists only because the model extracts prognostic signal from images already produced in standard care, which is the purest expression of the computational pathology thesis in this index.

Produces a risk stratification rather than a diagnosis, which places it alongside the clinician rather than in the diagnostic path. The output is a low or high risk group assignment informing treatment intensity decisions, so the oversight question is less about diagnostic error and more about how much weight a clinician places on an algorithmic prognosis when deciding on chemotherapy. The company positions it as decision support, and the risk grouping format keeps interpretation with the clinical team.

Materially more transparent than most, because the validation is in the peer reviewed literature where methodology can be inspected rather than in a datasheet. The published work states the approach, deep learning whole slide image classification for prognostic stratification, the cohorts used, and the endpoints. Training data composition and model architecture detail are not fully published, and there is no third party head to head benchmarking against molecular assays run by an independent group, but the primary validation is externally readable.

Among the better evidenced single product vendors here. A retrospective multi site validation study covering 2,719 primary breast cancer patients across two Swedish hospitals assessed prognostic performance in two independent cohorts, published in the peer reviewed literature. Two independent cohorts matters more than raw sample size, because prognostic models frequently fail to generalize beyond their development population. The evidence is retrospective rather than prospective, and confined to Swedish cohorts, so generalization to different populations and care pathways remains an open question a buyer should raise.

No specific PHI handling or data governance framework was located in the materials reviewed.

No HIPAA or BAA commitment was located. The product is CE-IVD marked for European clinical use rather than FDA cleared, so a US engagement would require establishing terms directly or through a host platform.

No SOC 2, ISO 27001, or equivalent attestation and no trust center were located.

Described in peer reviewed literature as the first CE-IVD marked AI based image analysis tool for primary breast cancer risk stratification available for routine clinical use, which is a category first rather than a me too certification. No FDA clearance was located. Graded C on the established basis, real European authorization without US clinical clearance, with the note that a prognostic risk tool occupies different regulatory territory than a detection device and its US path would likely differ accordingly.

Validation across two independent hospital cohorts addresses site level generalization, which is the most common failure mode for prognostic models. No formal governance framework, monitoring commitment, or demographic subgroup analysis was located, and the validation population is drawn from two Swedish hospitals, so performance across more diverse populations is undocumented. This matters more than usual for a prognostic tool, since a risk score that behaves differently across populations translates directly into differences in treatment intensity.

Reach comes through platform distribution rather than direct integration engineering. Since 2025 the product is available through PathAI's AISight platform, which is separately indexed here and itself cleared for primary diagnosis in the US and CE-IVD marked in Europe. For laboratories on that platform, adoption is algorithm selection. A broader connector list for other image management systems was not located, so integration beyond partner platforms is undocumented.

No hosting, tenancy, or data residency terms were located. Where the model runs inside a partner platform, residency follows that platform rather than this vendor.

No pricing is published. The commercial logic is nonetheless legible from the clinical positioning: the published rationale explicitly contrasts the tool against molecular multigene assays, which carry long lead times and high cost, so the value case is turnaround time and price relative to those tests. A buyer can therefore benchmark against a known comparator even without published rates, which is more than most vendors here allow.

A single indication, primary breast cancer risk stratification from surgical resection specimens. That focus is the source of its evidence depth, but coverage does not extend to other tumor types, to biopsy specimens, or to diagnostic as opposed to prognostic tasks. Buyers needing breadth will pair it with other tools rather than adopt it as a platform.