Digital Pathology and Its Implications for Clinical Practice and Research

Digital Pathology is a rapidly expanding field in which software applications incorporate artificial intelligence (AI) to provide an array of computationally based analysis solutions for the analysis, interpretation, and presentation of histological images. These AI-based applications have the potential to revolutionize and streamline the workflow in pathology, enabling clinicians to access the fundamental prognostic data embedded in the images that can be used to identify the likely diagnosis, the aggressiveness of tumors, and patient outcomes.

Image analysis algorithms can detect significant areas of tissue that may be unnoticeable to the human eye, detecting abnormalities in tissue distribution or cellularity, and indicating discrepancies in reports. This can reduce malpractice, increase quality control and efficiency in pathology, and enable the generation of more precise and reliable tissue-derived readouts.

Global Digital Pathology Market is estimated to be valued at US$ 666.0 million in 2022 and is expected to exhibit a CAGR of 7.7% during the forecast period (2022-2030).

Digital pathology is an emerging area in histology that can have significant implications for clinical practice, research, and training. It offers many advantages for the training and education of new and existing pathologists including the ability to share slides worldwide, the flexibility to work remotely at any time, and reducing the need for costly and inefficient slide preparation methods.

The use of AI-based software applications is not restricted to routine clinical practice and has the potential to revolutionize research in immuno-oncology and drug development by offering an array of tools for deciphering complex pathophysiology, uncovering novel biomarkers and drug targets, and identifying the optimal treatment regimens. Training and validation are crucial to ensure that AI algorithms have been optimized for a particular clinical application. To achieve this, a wide range of images of different stains, modalities, and tissues should be gathered to maximize generalizability and the level of concordance between manual and automated diagnoses. In addition, AI-based software should be validated in a controlled environment with a washout period to eliminate recall bias and ensure a high level of inter-observer reliability.


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