Digital pathology is the practice of digitising glass slides (tissue samples) using scanners to convert the glass slides into high resolution digital images. This allows pathologists to view, analyze, and share the images on a computer instead of a microscope.

The scanner takes high resolution images of entire glass slides, stitching together thousands of tiny snapshots to create a whole slide image (WSI) that can be viewed digitally.

A high resolution whole slide image (WSI) can range from 500 MB to 5+ GB, depending on scan quality and tissue size.

In many regions, certain systems are approved for primary diagnosis. Regulations vary from region to region.

No, it is a tool to enhance efficiency, not replace human expertise. AI assists, but pathologists make the final call.

There are a number of benefits including:

• Facilitating remote access - Pathologists can review slides from anywhere (with a tablet/computer and internet access), unconfined by having to be present in offices or beat traffic to travel distances between the institutions they serve
• Enabling collaboration - Digital images can easily and securely be shared allowing for multiple experts to view, discuss and collaboratively make diagnoses on the same slide simultaneously
• AI integration - Artificial intelligence algorithms can be developed and integrated to assist in diagnosis

First is cost, scanners, software and digital storage are expensive.

Standardisation also poses a significant challenge, there are currently different scanners and formats which can cause compatibility issues.

Workflow integration is another challenge. There may be resistance to change from legacy systems as well as the need for pathologists, hospitals and labs to undergo training in order to adopt digital workflow systems.

The beauty of digital pathology lies in its scalability and increasing accessibility. As technology advances and costs decrease, we envision a future where even smaller clinics and remote healthcare providers can leverage these tools. Imagine a patient in a rural community benefiting from a specialist's opinion from thousands of miles away, all thanks to digital pathology. It has the potential to democratise access to expert diagnostics.

Digital pathology involves digitising glass histology slides while computational pathology involves using AI and software to analyze these digitised slides.

The integration of histologic and molecular (also called -omics) data. When a tissue sample is taken from a patient, various tests and investigations are done. Imagine being able to use technology, which can analyze and process vast amounts of data, to combine the visual insights from digital pathology with genomic, proteomic, and metabolomic data from the same tissue sample. This creates a holistic, multi-dimensional view of disease, allowing for incredibly precise diagnoses, personalised treatment plans, and even the development of entirely new therapies.

Digital pathology involves scanning physical glass slides to create high-resolution digital images, known as virtual or digital slides. Pathologists can then view and analyze these virtual slides on a computer screen using specialized software. This approach enables remote access, instant sharing, and seamless collaboration among professionals, unhindered by their physical location. Critically, the digital format allows for the integration of AI and machine learning tools, which can enhance diagnostic accuracy through objective analysis and quantification, leading to more streamlined and precise diagnoses. In contrast, traditional pathology relies on the direct examination of physical glass slides under a conventional microscope. This method has geographic limitations, as the pathologist must be physically present with the slides and a microscope to conduct their analysis. Sharing cases for second opinions typically requires shipping these physical slides, which can be a slow process and carries the risk of damage or loss. While foundational, traditional pathology doesn't allow for automated analysis and can introduce workflow inefficiencies due to its manual handling and storage requirements.