Artificial Intelligence and Digital Pathology: A Narrative Review on Advancements and Opportunities for Improved Diagnosis and Treatment

Abstract

Recent advancements in Digital Pathology (DP) have empowered pathologists to provide more accurate diagnosis through digital means. Whole-slide Imaging (WSI) technology has enabled the digital scanning, representation, and preservation of numerous tissue slides, while Artificial Intelligence (AI), image analysis, and Machine Learning (ML) have enhanced disease diagnosis accuracy. There is a growing acceptance of the transition from traditional glass slide histopathological diagnosis to AI-assisted diagnosis using digital slides, driven by the substantial data accumulation that demands computer-aided analysis. Maximising the potential of AI breakthroughs in DP is critical, offering significant research opportunities across related fields. DP offers significant potential for telepathology, second opinions, and educational purposes. Additionally, it presents substantial research opportunities in image computing due to its vast reservoir of data. Pathologists have discerned characteristics beyond the naked eye’s perception by analysing “sub-visual” images using DP. The flexibility of workflow provided by DP will be the main reason for its widespread adaptation and acceptance. DP also has the potential to be an essential tool to maintain operations of the pathology department in case of public health emergencies, which will streamline, fast-track, and improve patient care. Given the expanding accessibility and prevalence of the internet, it is crucial to develop innovations like DP. This technology catalyses enhancing patient care, opening avenues for further advancements in healthcare delivery. The purpose of present review was to bring to light the great potential that DP encompasses to improve diagnosis and treatment planning, which will ultimately lead to better patient care. However, integrating DP systems necessitates collaboration from various stakeholders beyond the Pathology Department. Despite evident advantages, several challenges must be addressed for the successful implementation and mass acceptance. Therefore, this narrative review aimed to illuminate the substantial potential inherent in DP for enhancing both diagnosis and treatment planning processes, consequently fostering improvements in patient care and also understanding DP, highlighting its challenges and opportunities. It also delves into the role of AI, image analysis, and ML in aiding disease diagnosis and reporting. With social distancing measures in place during the Coronavirus Disease -2019 (COVID-19) pandemic, pathologists were able to remotely access and analyse DP images, further cementing the importance of DP in current scenarios.