Digital pathology is moving from isolated algorithms to regulated drug-development infrastructure
Digital and AI pathology have outgrown the narrow role of image analysis tools. The central question is no longer whether these systems can classify tissue patterns, but whether the surrounding infrastructure can support them across biobanks, translational research, clinical trials and regulated diagnostics. This structured narrative review places the field where it now belongs: inside the machinery of drug development, not on its margins.
The review synthesised peer-reviewed literature and regulatory documents published between January 2019 and April 2026, with 78 sources retained. Its scope runs from AI-ready tissue biobanks and preclinical pathology through patient screening, endpoint adjudication, central pathology review, Contract Research Organization workflows and US regulatory pathways for in vitro diagnostics, software as a medical device and companion diagnostics. The practical message is immediate. Digital pathology does not scale on image models alone; it scales on governance, interoperability and auditable workflows.
Biobanks, image systems and laboratory data now determine whether pathology AI can reach the clinic
The review frames the enabling ecosystem as a chain of linked systems. Tissue specimens must be collected, annotated and stored in a way that preserves downstream analytic value. Digital slides then need to move through Laboratory Information Systems, Image Management Systems and AI orchestration layers without losing provenance, quality control or traceability. In translational research, weak links in that chain can erase the advantage of the algorithm before the first patient ever enters a trial.
A governed LIS and IMS stack matters because pathology AI depends on reproducible data flow. If specimen metadata are incomplete, if slide handling is inconsistent, or if image retrieval is fragmented across disconnected systems, the model may still generate a prediction while the evidence base around it quietly degrades. The review treats this as a structural problem, not a software inconvenience. The pathology label is only as reliable as the workflow that produced it.
Regulatory pathways hinge on the intended use of the digital pathology product
The review maps US FDA pathways to product category and use case, including 510(k), De Novo and premarket approval. That mapping is important because digital pathology products do not all enter the market through the same route, and their regulatory burden depends on risk, novelty and intended clinical function. Some tools support workflow efficiency, while others influence diagnosis, treatment selection or trial eligibility, where the evidentiary bar rises sharply.
Companion diagnostics sit at the most consequential end of that spectrum. When a pathology assay determines whether a patient qualifies for a targeted therapy, analytical performance is inseparable from treatment access. A brittle algorithm in that setting is not just a technical defect; it can become a gatekeeper to care. The review’s regulatory framing makes clear that companion diagnostics demand alignment between assay validity, software performance and the therapeutic claim attached to the test.
Trial screening, endpoint adjudication and central review depend on the same infrastructure
Translational pathology is often discussed as a discovery tool, yet the review shows how deeply it shapes trial operations. AI-assisted screening can accelerate identification of eligible patients, central pathology review can standardise lesion assessment, and digital workflows can support endpoint adjudication across multicenter studies. Each of those functions depends on the same ingredients: harmonised images, structured reporting, secure data transfer and traceable human oversight.
In this context, algorithmic performance has to survive contact with real-world variation. Slide preparation, scanner differences, staining variability and site-specific practice patterns can all introduce domain shift. An AI model trained in one setting may lose accuracy when transported into another, especially if the external validation environment is underpowered or poorly matched to the development cohort. The review treats that problem as one of the field’s core bottlenecks, alongside annotation cost, interpretability and algorithmic bias.
A model that cannot travel is not yet a clinical tool.
Validation standards and human oversight remain the main guardrails
The review places strong emphasis on reporting and evaluation standards, including TRIPOD-AI, CLAIM and DECIDE-AI. Those frameworks are meant to reduce the familiar gap between promising technical performance and trustworthy clinical deployment. They also force a more disciplined account of how the model was trained, tested and monitored, which is essential in pathology where small differences in labeling strategy can produce large downstream effects.
Human oversight remains central even when automation is embedded into the pipeline. Structured reporting, adjudication workflows and review by experienced pathologists still define the final clinical interpretation in many settings. The review does not portray AI as a replacement for expert review; instead it presents AI as a component that must be validated against professional standards and integrated into regulated decision pathways. In practical terms, this means every stage of the workflow needs auditability, from raw slide ingestion to the final report handed to clinicians or trial monitors.
Reimbursement, bias and cross-jurisdiction regulation may determine adoption more than accuracy alone
Technical performance alone will not determine whether digital pathology becomes routine. The review identifies reimbursement uncertainty, regulatory complexity and cross-jurisdiction alignment as major barriers to scale. Even a well-performing system can stall if payers do not recognize its value, if deployment costs exceed local budgets, or if the regulatory framework changes between research, trial and commercial use.
The same caution applies to bias. Annotation practices, population differences and training-set composition can all influence model behavior, and those effects may be amplified when systems are deployed outside the original development environment. The review argues that real-world cost-effectiveness analysis will be necessary before broad deployment can be justified. Clinical credibility will depend not only on whether a system is accurate, but on whether it is robust, interoperable and financially sustainable in everyday use.
Harmonised infrastructure is the gatekeeper for biobank-to-companion-diagnostic translation
The review’s core conclusion is operational rather than speculative: a regulated, interoperable LIS, IMS and AI ecosystem is a prerequisite for translating digital pathology research into regulator-grade evidence. Without that foundation, the field risks producing impressive models that cannot survive validation, review or deployment.
There is a clear policy direction here. Future progress will depend on external validation against reporting standards, closer harmonisation with the EU AI Act and EU Medical Device Regulation, and explicit attention to cost-effectiveness in real-world settings. The review leaves one practical tension in place: the science is advancing faster than the infrastructure that must govern it, and the bottlenecks now sit less in the classifier than in the system around it.