DP, which initially delineated the process of digitizing whole slide images (WSIs) using advanced slide scanning technology, is now a generic term that includes artificial intelligence (AI)–based approaches for detection, segmentation, diagnosis, and analysis of digitalized images [1]. WSI indicates digital representation of an entire histopathologic glass slide at microscopic resolution [2]. Over the last two decades, WSI technology has evolved to encompass relatively high resolution, increased scanner capacity, faster scan speed, smaller image file sizes, and commercialization. The development of appropriate image management systems (IMS) and a seamless interface connection between existing hospital systems such as electronic medical records (EMR), picture archiving communication systems (PACS), and laboratory information systems (LIS) (also referred to as the pathology order communication system) has stabilized, cheaper storage systems have been established, and streaming technology for large image files has been developed [3,4]. Mukhopadhyay et al. [5] evaluated the diagnostic performance of digitalized images compared to microscopic images on specimens from 1,992 patients with different tumor types diagnosed by 16 surgical pathologists. Primary diagnostic performance with digitalized WSIs was not inferior to that achieved with light microscopy-based approaches (with a major discordance rate from the reference standard of 4.9% for WSI and 4.6% for microscopy) [5].

Computer-aided pathology (CAP, also referred to as computeraided pathologic diagnostics, computational pathology, and computer-assisted pathology) refers to a computational diagnosis system or a set of methodologies that utilizes computers or software to interpret pathologic images [2,6]. The Digital Pathology Association does not limit the definition of computational pathology to computer-based methodologies for image analysis, but rather as a field of pathology that uses AI methods to combine pathologic images and metadata from a variety of related sources to analyze patient specimens [2]. The performance of computeraided diagnostic tools has improved with the development of AI and computer vision technology. Digitalized WSIs facilitate the development of computer-aided diagnostic tools through AI applications of intelligent behavior modeled by machines [7].

Machine learning (ML) is a subfield within AI that develops algorithms and technologies. In 1959, Arthur Samuel defined ML as a “field of study that gives computers the ability to learn without being explicitly programmed” [8]. Artificial neural networks (ANNs) are a statistical learning algorithm inspired by biological neural networks such as the human neural architecture in ML. ANN refers to a general model of artificial neurons (nodes) that form a network by synapse binding and have the ability to solve problems by changing synapse strength through learning [9]. Deep learning (DL), a particular approach of ML, comprises multiple layers of neural networks that usually include an input layer, an output layer, and multiple hidden layers [10]. Convolutional neural networks (CNNs) are a type of deep, feedforward ANN used to analyze visual images. CNN is classified as a DL algorithm that is most commonly applied to image analysis [11]. Successful computer-aided pathologic diagnostic tools are being actively devised using AI techniques, particularly DL models [12].

DP covers all pathologic activities using digitalized pathologic images generated by digital scanners, and encompasses the primary diagnosis on the computer monitor screen, consultation by telepathology, morphometry by image analysis software, multidisciplinary conferences and student education, quality assurance activities, and enhanced diagnosis by CAP.

Recently, primary diagnosis on computer monitor screens using digitalized pathologic images has been practically approved by the Food and Drug Administrations (FDA) of the United States of America, the European Union, and Japan [13-15]. Dozens of validation studies have compared the diagnostic accuracy of DP and conventional microscopic diagnosis during the last decade [5,16]. Although most studies have demonstrated no inferiority of the diagnostic accuracy of whole slide imaging compared to conventional microscopy, study sample sizes were mostly limited, and the level of evidence was not high enough (only level III and IV). Therefore, an appropriate internal validation study for diagnostic concordance between whole slide imaging and conventional microscopic diagnosis should be performed before implementing DP into individual laboratories according to the guidelines suggested by major study groups and leading countries.

Telepathology primarily indicates a system that enables pathologic diagnosis by transmitting live pathology images through online connections using a microscopic system with a remotecontrolled, motorized stage. The limited meaning of “telediagnosis” can be used in certain clinical situations in which a pathologic diagnosis is made in a remote facility without pathologists. Due to recent advances in whole slide imaging technology, faster and more accurate acquisition and sharing of high quality digital images is now possible. Telepathology has evolved so that DP data can be easily used, shared, and exchanged on various systems and devices using cloud systems. Because of this ubiquitous accessibility, network security and deidentification of personal information have never been more important [13].

Morphometric analysis and CAPD techniques will be further accelerated by implementation of DP. Ki-67 labeling index is traditionally considered one of the most important prognostic markers in breast cancer, and various image analysis software programs based on ML have been developed for accurate and reproducible morphometric analysis. However, DL is more powerful for more complex pathologic tasks such as mitosis detection for breast cancers, microtumor metastasis detection in sentinel lymph nodes, and Gleason scoring for prostate biopsies. Furthermore, DP facilitates the use of DL in pathologic image analysis by providing an enormous source of training data.

DP is also a new opportunity for life-changing advances in education and multidisciplinary conferences. It enables easy sharing of pathologic data to simplify preparation of education and conference materials. DP mostly uses laboratory automation systems and tracking identification codes, which reduces potential human errors and contributes to patient safety. DP also simplifies pathologic review of archived slides. By adopting CAPD tools based on DL to review diagnosis, quality assurance activities can be performed quickly and with less effort. DL can be used to assess diagnostic errors and the staining quality of each histologic slide.

When combined with other medical information, such as the EMR, hospital information system (HIS), public health information and resources, medical imaging data systems like PACS, and genomic data such as next-generation sequencing, DP provides the basis for revolutionary innovation in medical technology.

Recent technological advances in WSI systems have accelerated the implementation of digital pathology system (DPS) in pathology. The use of WSI for clinical purposes includes primary diagnosis, expert consultation, intraoperative frozen section consultation, off-site diagnosis, clinicopathologic conferences, education, and quality assurance. The DPS for in vitro diagnostic use comprises whole digital slide scanners, viewing and archiving management systems, and integration with HIS and LIS. Image viewing software includes image analysis systems. Pathologists interpret WSIs and render diagnoses using the DPS set up with adequate hardware, software, and hospital networks.

Most recent WSI scanners permit high-speed digitization of whole glass slides and produce high-resolution WSI. However, there are still differences in scanning time, scan error rate, image resolution, and image quality among WSI scanners. WSI scanners differ with respect to their functionality and features, and most image viewers are provided by scanner vendors [4]. When selecting a WSI scanner for clinical diagnosis, it is important to consider the following factors: (1) volume of slides, (2) type of specimen (eg, tissue section slides, cytology slides, or hematopathology smears), (3) feasibility of z-stack scanning (focus stacking), (4) laboratory needs for oil-immersion scanning, (5) laboratory needs for both bright field and fluorescence scanning, (6) type of glass slides (e.g., wet slides, unusual size), (7) slide barcode readability, (8) existing space constraints in the laboratory, (9) functionality of image viewer and management system provided by vendor, (10) bidirectional integration with existing information systems, (11) communication protocol (e.g., XML, HL7) between DPS and LIS, (12) whether image viewer software is installed on the server or on the local hard disk of each client workstation, (13) whether the viewer works on mobile devices, and (14) open or closed system.

It is crucial to fully integrate the WSI system into the existing LIS to implement DPS in the workflow of a pathology department and decrease turn-around-time [14]. Therefore, information technology (IT) support is vital for successful implementation of DPS. Pathologists should work closely with IT staff, laboratory technicians, and vendors to integrate the DPS with the LIS and HIS. The implementation team should meet regularly to discuss progress on action items and uncover issues that could slow or impede progress.

Resistance to digital transformation can come from any level in the department. Documented processes facilitate staff training and allow smooth onboarding. Regular support and training should be provided until all staff understand the value of DPS and perform their tasks on a regular basis.

As many have already pointed out, the current AI-based CAP has several limitations. Tizhoosh and Liron have listed a number of problems that CAP should address, including vulnerability to adversarial attacks, capability limited to a certain diagnostic task, obscured diagnostic process, and lack of interpretability [47]. Specifically, lack of interpretability is unacceptable to the medical society. Other literature lists practical issues which may apply to pathologic AI: slow execution time of CAP, opacity of CAPD development process, insufficient clinical validation of CAP, and limited impact on health economics [48]. Another substantial issue mentioned was frequent workflow switching induced by limited integration of CAPD applications within the current pathology workflow. Beyond disease detection and grading, CAP should be able to provide an integrated diagnosis with a number of analyses on various data [49]. Rashidi et al. [50] emphasized the role of pathologists in developing CAPD tools, especially in dealing with CAP performance problems caused by lack of data and model limitations. These limitations can be summarized as follows: (1) lack of confidence in diagnosis results, (2) inconvenience in practical use, and (3) simplicity of function (Table 1). These limitations can be overcome by close collaboration among AI researchers, software engineers, and medical experts.

Confidence in diagnostic results can be improved by formalizing the CAP development and validation processes, expanding the size of validation data, and enhancing understanding of the CAPD.

CAPD products can only enter the market as medical devices with the approval of regulatory agencies. The approval process for medical devices requires validation of the medical device development process, including validation of CAPD algorithms if applicable [51]. It is notable that regulatory agencies are already preparing for regulation of adaptive medical devices that learn from information acquired in operation. To formalize the validation process, it is desirable to assess the performance change of the CAP based on input variation induced by several factors, such as quality of tissue samples, slide preparation process, staining protocol, and slide scanner characteristics. This is similar to the validation process for existing in vitro diagnostic devices such as reagents. Validation data that covers diverse demographics is desirable. The use of validation data covering multiple countries and institutions increases the credibility of validation results [43]. Regulatory agencies require data from two or more medical institutions in clinical trials for medical device approval, so CAPDs introduced in the market as approved medical devices should be sufficiently validated.

The ability to explain CAP is a hot topic in current AI research. According to a recently published book on the interpretability of ML, which comprise the majority of current AI, interpretation approaches to complex ML models are largely divided into two categories: visualization of important features in the decision process and example-based interpretation of decision output [52]. Important feature visualization in ML models includes Visualizing Activation Layers [53], SHAP [54], Grad-CAM [55], and LIME [56]. These methods can be used to interpret a model’s decision process by identifying parts of the input that have played a role. In applications to pathologic image analysis, an attention mechanism was used to visualize epithelial cell areas [57]. Arbitrary generated counterfactual examples can be used to describe how input change affects to model output [58]. Case-based reasoning [59,60] can be combined with the interpretation target model to demonstrate consistency between model output and reasoning output of the same input [61]. Similar image search can also be used to show that AI decisions are similar to human annotations on the same pathologic image, and systems such as SMILY can be used for this purpose [62].

Inconvenience in practical use can be solved by accelerating execution of AI and integrating it with other existing IT systems in medical institutions. In addition to hardware that provides sufficient computing power, software optimization must be achieved to accelerate AI execution. This includes removal of data processing bottlenecks and data access redundancy as well as model size reduction. Medical IT systems include HIS, EMR, and PACS, which are integrated with each other and operate within a workflow. CAPD systems, digital slide scanners, and slide IMS should participate in this integration so they can be easily utilized in a real-world workflow. The problem of CAPD systems being limited to specific data and tasks can be relieved by adding more data and more advanced AI technologies, including multi-task learning, continual learning, and reinforcement learning.

DP and CAP are revolutionary for pathology. If used properly, DP and CAP are expected to improve convenience and quality in pathology diagnosis and data management. However, a variety of challenges remain in implementation of DPS, and many aspects must be considered when applying CAP. Implementation is not limited to the pathology department and may involve the whole institution or even the entire healthcare system. CAP is becoming clinically available with the application of DL, but various limitations remain. The ability to overcome these limitations will determine the future of pathology.