BOSTON, Mass. and ZURICH, Switzerland, Jan. 08, 2026 (GLOBE NEWSWIRE) — PathAI, a global leader in AI-powered pathology, today announced a strategic collaboration with University Hospital Zurich (USZ) to deploy AISight® Dx1, PathAI’s CE-IVD digital pathology platform, together with AIM-TumorCellularity (AIM-TC)2 to support tumor cell content (TCC) quantification in routine molecular workflows. The collaboration marks one of the first implementations of an AI-based tool in daily pathology operation in Switzerland.
USZ will use AIM-TC to support the routine assessment of TCC prior to next-generation sequencing (NGS), which requires a minimum amount of tumor cells in a sample to allow NGS to accurately detect actionable biomarkers that guide therapy decisions for the patient. The PathAI algorithm provides a stable, quantitative TCC estimate to increase consistency and confidence in the quality of samples selected for molecular diagnostics. Human pathologists’ TCC assessment is time-consuming, resource intensive, and subject to limited availability, which can delay the initiation of molecular testing until a pathologist is able to review the case. Manual TCC assessment is also plagued by low accuracy and reproducibility; inaccurate TCC quantification can lead to failed sequencing and destruction of tissue, a significant challenge for patients with limited tissue available for further testing. A study entitled “The estimation of tumor cell percentage for molecular testing by pathologists is not accurate” found that pathologists overestimated tumor cell percentage in 38% of cases, possibly causing false negative results3.
USZ selected PathAI following what is considered the most comprehensive analytical evaluation to date of an AI-based TCC algorithm. Preliminary results from this evaluation have been presented at the 2024 European Congress of Pathology, the 2025 European Congress of Digital Pathology, and the 2025 Swiss Society of Pathology Annual Meeting, with a detailed scientific publication forthcoming.
“USZ’s thorough and scientifically rigorous evaluation of AIM-TC reflects exactly the spirit of evidence-driven innovation we strive for at PathAI,” said Eric Walk, MD, Chief Medical Officer of PathAI. “We are proud that AIM-TC meets the exceptionally high analytical standards of USZ, and we look forward to advancing future projects together.”
“AIM-TC showed strong and reliable performance throughout our evaluation, with accuracy that held up even in difficult real-world samples,” said Professor Holger Moch, Director of the Department of Pathology and Molecular Pathology at USZ. PD Dr. Bettina Sobottka, Director of the Department’s Division of Molecular Pathology, added, “We were impressed by the consistency of the results and by PathAI’s scientific rigor, which gave us confidence in bringing this technology into routine molecular diagnostics and in choosing PathAI as a partner for the years to come.”
About PathAI
PathAI is a leading provider of AI-powered pathology solutions that aim to improve the accuracy of histology assessment and accelerate drug development. PathAI’s platform leverages advanced artificial intelligence to analyze and interpret pathology images, providing valuable insights to pathologists, researchers, and pharmaceutical companies. PathAI is headquartered in Boston, MA. For more information, please visit www.pathai.com and follow us on LinkedIn.
About University Hospital Zurich
The University Hospital Zurich (USZ) is one of Europe’s leading academic medical centers, offering cutting-edge clinical care and internationally recognized diagnostic expertise. The Department of Pathology and Molecular Pathology is a pioneer in digital and molecular pathology innovation. For more information, please visit www.usz.ch.
1 AISight Dx is CE-IVD-marked for primary diagnosis in the EEA, CH, and UK.
2 AIM-TumorCellularity is an AISight Dx workflow tool and not intended to be used for diagnostic purposes.
3 Smits et al. The estimation of tumor cell percentage for molecular testing by pathologists is not accurate. Modern Pathology (2014) 27, 168–174.
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