Digital Pathology: Roadmap to the Future of Medical Diagnosis

Digital Pathology encompasses the use of optmized pathology workstations, whole slide imaging, image analysis, image management, laboratory information management system, use of machine learning, data handling and storage. Pathology is one of the most important fields in medicine, as the service provides investigation and validation platform in lieu of a medical condition of a patient. Traditional methods of pathology involves slide preparation, traditional microscopy, result interpretation from microscopy by pathologist onto an information management system followed by physical storage of slides for a prescribed timeline. According to numerous research and surveys conducted across the world, the request for pathology services are increasing by a healthy percentage year-on-year which can be attributed to the increase in cancer prevalence across the globe, ageing population amongst a number of other factors. Factoring in to the above the number of experienced pathologists available at the moment and ones about to retire, the scenario for pathology services may start to look grim. Therefore, an urgent solution is needed to tackle the challenges pertaining increase in diagnosis of patients and unreasonable timelines that pathologists are facing currently with the traditional pathology methods. Proper deployment of Digital Pathology solutions have the demonstrated ability to streamline the workflow of pathology services, and can potentially increase the efficiency of the workflow and decrease the time for diagnosis.

Artificial Intelligence (AI), Machine learning and deep learning are terms that refer to the technology that possesses the ability to recognize patterns from the database that it is initially provided with and matching to it to similar data set thus mapping the results based on past occurrences. The use of AI in digital pathology has gained momentum over the last few years. Notably, the FDA approved an AI-tool as a primary diagnosis tool as recently as in 2017 for wrist fractures. The application of AI for pathology services are innumerable considering the fact the pathological services give rise to a lot of information. Information or data sets that can serve as the basis of creating deep learning digital pathology tools that can act as a diagnosis supplement tool for pathologists, thus increasing the speed of diagnosis, maybe accuracy and certainly providing the potential to prioritize diagnosis cases based on severity for the pathologists to review. The next decade of the medical field is set to witness the transformation of digital pathology services by AI tools.