Health body launches tender for AI-based PD-L1 pathology tool
Tender seeks an AI solution to support PD-L1 expression assessment in pathology, reflecting a wider shift towards machine learning in diagnostics.
Tender seeks an AI solution to support PD-L1 expression assessment in pathology, reflecting a wider shift towards machine learning in diagnostics.
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An oncology purchasing body is seeking an artificial intelligence system to help pathology teams evaluate PD-L1 expression for Cancer Control Centers, signalling how AI is moving into some of the most specialised corners of cancer diagnostics.
On 19th August 2026, UNICANCER ACHATS published a contract notice for an AI Solution for PD-L1 Evaluation. The notice sets out a requirement to supply and maintain an artificial intelligence solution for evaluating PD-L1 expression in pathology for Cancer Control Centers.
The brief description is high level, but it is clear that the buyer is looking for a specialist tool that can sit within pathology services and support PD-L1 assessment across Cancer Control Centers. The contract combines provision of the software with ongoing maintenance, indicating a continuing relationship with whichever supplier is appointed.
Although no further technical details are given at this stage, the focus on PD-L1 points to a very targeted application of AI, rather than a general-purpose pathology platform. It is one of a cluster of tenders in which buyers are specifying artificial intelligence for a defined diagnostic task.
The UNICANCER ACHATS tender does not stand alone. Health systems and cancer centres in several countries are now procuring AI and digital pathology tools, often as part of wider modernisation of laboratory services.
In February 2026, Zachodniopomorskie Centrum Onkologii launched a contract for the Software for Digital Image Analysis, covering the supply and installation of software for analysing digital images in pathology. That contract includes user training and a 36‑month warranty and forms part of a cross-border innovation project at the West Pomeranian Oncology Center.
Also in February 2026, Národný onkologický ústav v Bratislave went to market for AI-Supported Digital Pathology Software. That contract involves supplying digital pathology software supported by artificial intelligence for twelve healthcare facilities, with the detailed requirements set out in competition documents.
In March 2026, Region Midtjylland issued a contract notice for a Digital Pathology Solution, aiming to implement and consolidate fully digitalised workflows for microscopy and diagnostic evaluation of tissue samples in three pathology departments.
Closer to histopathology practice in the United Kingdom, Cambridge University Hospitals NHS Foundation Trust signalled in July 2026 that it is planning a comprehensive Digital Pathology Solution for its Department of Histopathology and Diagnostic Cytology and Haemato-oncology.
Alongside these projects focused on pathology images, other tenders show AI moving into adjacent areas of cancer care. In March 2026, sihtasutus Põhja-Eesti Regionaalhaigla published a notice for AI Software for Radiotherapy Contouring, seeking software and hardware for automatic contouring of critical organs and lymph nodes in radiotherapy treatment plans at the Regional Hospital.
Taken together, these notices point to a more routine role for AI in image-heavy parts of oncology, from tissue assessment through to treatment planning. Within this recent group of tenders, the UNICANCER ACHATS procurement is distinctive in focusing so directly on PD-L1 expression within pathology.
The PD-L1 tender is notable for its narrow focus. Several other digital pathology procurements are framed around wider goals, such as fully digitalised workflows across pathology departments or rolling out software to multiple facilities. Here, the buyer has identified a single, clearly defined test and is seeking an AI solution centred on that task.
Similar patterns are emerging elsewhere as buyers specify artificial intelligence for particular diagnostic domains. In June 2026, the Department of Veterans Affairs issued a request for information entitled National Digital Telepathology RFI. That notice seeks information from vendors on available AI pathology solutions and cloud storage for a cancer detection system, with the responses intended to shape an acquisition strategy.
Outside pathology, clinical services are also turning to AI for defined diagnostic tasks. The Department of Health and Human Services indicated in February 2026 that it intends to procure an AI Medical Imaging Platform from PaxeraHealth Corporation, covering licensing, deployment and technical support services. In June 2026, Departamento de Salud La Fe set out a contract for Ophthalmological AI Diagnostic Services, covering ophthalmological diagnostic tests based on artificial intelligence.
These procurements underline how buyers are no longer treating AI as a single, generic purchase. Instead, they are carving out distinct use cases and building tenders around specific clinical problems. The UNICANCER ACHATS notice continues that trend, but in a particularly specialised part of cancer pathology.
While the PD-L1 tender concentrates on software, other notices make clear that AI in pathology is emerging alongside investments in core laboratory infrastructure and reagents. Several recent procurements focus on those foundations.
In August 2026, ICS - Hospital Universitari Arnau de Vilanova de Lleida i GAPiC Àmbit Lleida set out a contract for the Supply of Reagents and Equipment for automated immunohistochemistry, pharmacodiagnosis, histochemistry and in situ hybridisation techniques at Arnau de Vilanova University Hospital of Lleida.
Gerencia de Atención Especializada de Medina del Campo has a similar focus in its contract for Pathological Anatomy Laboratory Supplies, which covers consumable materials and maintenance services for automated immunohistochemistry and pharmacodiagnostic techniques.
Other buyers, such as A.O. DEI COLLI with its contract for Diagnostic Systems and Equipment Supply, are procuring suites of diagnostic systems and laboratory equipment for anatomical and histological pathology, including digital pathology.
In this context, the UNICANCER ACHATS requirement for an AI solution to evaluate PD-L1 expression can be seen as part of a broader pattern in which software and algorithms are procured alongside staining systems, reagents and digital infrastructure, rather than in isolation.
The current notice provides only a concise headline description of the PD-L1 project. It does not yet describe how the system should integrate into existing laboratory systems, what performance thresholds are expected, how validation should be handled or how AI outputs would be presented to pathologists.
Even so, the direction of procurement activity is clear. Across recent tenders, from AI-supported digital pathology software in Slovakia to image analysis projects in Poland and fully digitalised workflows in Region Midtjylland, buyers are starting to commission artificial intelligence as part of their diagnostic infrastructure.
For Cancer Control Centers and potential suppliers, the UNICANCER ACHATS procurement will be one to monitor. Its outcome could influence how AI is introduced into highly specialised pathology tests and may inform future tenders that extend similar approaches beyond PD-L1.
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