# Pathological Digital Biomarkers: Validation and Application
## 5 Discussion  
Digital pathology is being actively developed for numerous reasons, including the increased volume and complexity of histopathological workloads, the pressure on turnaround times, the shortage of staff, the need to expand capacity, the drive toward networks and service mergers, and the drive toward healthcare digitalization [68]. Digital pathology has a number of possible clinical applications, including pathology sample diagnostic test, immunohistochemistry evaluation, MDT/tumor board, and frozen section diagnosis [68]. DP can also improve diagnostic and predictive clinical decision-making in cancer treatment since AI and machine learning can display clinical and pathological characteristics of many clinical samples [10]. Despite these advantages, digital pathology is not easily adopted in clinical practice. Pathologists are working on a number of projects to better comprehend AI-based predictions. However, there are so many variables that influence judgments that humans are unable to fully comprehend them [79]. Because of the lack of understanding, AI-based predictions are prone to bias and misdiagnoses, making AI challenging to apply in clinical practice. As a result, the novel pathological digital biomarker must be rigorously validated. However, because digital pathology is still being developed, validation approaches are currently being debated. This review article discusses validation methods and examples for novel pathological digital biomarkers, based on the known validation approach that biomarkers must go through for clinical application. Another new technological advance in digital pathology is 3D pathology [80]. Three-dimensional pathology aided computational analysis and deep learning such as generative adversarial network to provide multistage features such as nuclei [81; 82], collagen fiber [83], and glands [84]. Three-dimensional pathology with deep-learning-assisted prostate gland-biopsy analysis has opened the possibility of nondestructive, superior prognostic stratification of patient disease, demonstrating the value of computational 3D pathology for clinical decision support, especially for low- to intermediate-risk prostate cancer [84]. In terms of insurance, ensuring the therapeutic utility of pathological digital biomarkers is critical. The difficulty of use, evidence of effectiveness, and financial return on investment are all factors that contribute to AI deployment failures in health care [85]. As previously noted, clinical usefulness is a critical consideration when determining if pathological digital biomarkers may be employed in clinical practice. It would be difficult to employ pathological digital biomarkers clinically until the technology's clinical value is demonstrated through clinical studies, because clinical usefulness is intimately linked to the question of reimbursement. In digital pathology, there is currently no particular procedural code for applying AI techniques for diagnostic or prognostic purposes [17]. The US Food and Drug Administration (FDA) approval will be required for digital biomarkers to demonstrate their clinical utility and be utilized in clinics. A medical image management and processing system is defined by the FDA as a device that provides one or more capabilities related to the review and digital processing of medical images for the purpose of interpretation by a trained practitioner for disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in medical image interpretation and analysis. Image segmentation, multimodal image registration, and 3D visualization are examples of advanced image manipulation functions. Complex quantitative functions may include semiautomated measurements or time-series measurements [86].