Chris Melson
Associate Director of Research and Policy, The Health Policy Partnership
Computational pathology is powering faster and more accurate diagnoses to help tackle rising cancer rates and workforce shortages.
Using artificial intelligence to analyse digital imaging is enabling pathologists (specialists who examine tissue samples and are essential to the diagnostic process) to carry out faster, more accurate diagnoses. This holds enormous promise in cancer detection, where early diagnosis is key but challenged by rising cases and strained healthcare resources.
Computational pathology addresses workforce gap
One of computational pathology’s most useful applications is in improving efficiencies to help address the global shortfall of trained pathologists. In the UK, four out of five pathology departments have vacancies they are struggling to fill. Furthermore, a quarter of pathologists are over 55; with training taking up to 15 years, there are concerns of an impending retirement crisis. The workload of each pathologist is expected to intensify as both the number and complexity of cases increase.1
As demands grow, computational pathology offers a scalable solution. When supported by technology, pathologists can analyse slides twice as quickly, speeding up the diagnostic process and alleviating bottlenecks in patient care.2
Despite the benefits of computational
pathology, global uptake remains limited.
Supporting more accurate diagnosis
Computational pathology can also offer a more comprehensive understanding of cancer. Its ability to detect biomarkers and predict treatment response makes it critical for personalised cancer care. It can also analyse tissue slides in greater detail than the human eye, detecting up to 20% more tumours than conventional methods.3 This enhanced clarity not only improves diagnosis accuracy, particularly for hard-to-spot cancers, but may also provide opportunities for innovation.
Improving implementation
Despite the benefits of computational pathology, global uptake remains limited. The first step to addressing this is ensuring the widespread digitalisation of pathology departments; digitalisation is still rare, but it is a prerequisite for the implementation of computational pathology. Greater investment is needed – not just in digital infrastructure, but in training, research and workflow integration. Embedding computational pathology seamlessly into care pathways is key to making it a practical and powerful tool for clinicians globally.
[1] The Royal College of Pathologists. 2018. Meeting pathology demand: Histopathology workforce census.
[2] Retamero, JA. et al. 2024. Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases. The American Journal of Surgical Pathology.
[3] Yun L. et al. 2017. Detecting Cancer Metastases on Gigapixel Pathology Images. Computer Vision and Pattern Recognition.
