2022
DOI: 10.3390/diagnostics12112794
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Application of Artificial Intelligence in Pathology: Trends and Challenges

Abstract: Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in… Show more

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Cited by 54 publications
(31 citation statements)
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“…Insufficient data in the development of algorithms leads to errors in performance. 16,[19][20][21][22][23][24][25][26][27][28][29][30] Underrepresentation of disease entities or population groups is likely to result in some entities not being identified correctly by the algorithm, meaning it is likely to underperform in such groups. This creates bias and an increased risk of errors in these subgroups.…”
Section: Missing Data Leading To Bias and Hidden Stratificationmentioning
confidence: 99%
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“…Insufficient data in the development of algorithms leads to errors in performance. 16,[19][20][21][22][23][24][25][26][27][28][29][30] Underrepresentation of disease entities or population groups is likely to result in some entities not being identified correctly by the algorithm, meaning it is likely to underperform in such groups. This creates bias and an increased risk of errors in these subgroups.…”
Section: Missing Data Leading To Bias and Hidden Stratificationmentioning
confidence: 99%
“…48 In order to address these potential sources of error, the datasets used to train and test algorithms must be large and diverse enough to represent the different populations, environments, diseases, and data acquisition methods in which they will be used. 8,15,30,46,49 In pathology, the developmental data should encompass different hospitals, scanners, batches, patient groups, disease entities, and severities. Artificial techniques such as data augmentation (modifying existing images to create new data) can be used to increase input image variation, such as colour, contrast, and orientation, mimicking the input from different pathology laboratories.…”
Section: Distributional Shift and Lack Of Generalisabilitymentioning
confidence: 99%
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“…1,2 The development of high-resolution scanning devices has made it possible to digitalize conventional glass slides to produce digital slides -a process referred to as whole-slide imaging (WSI) -opening the door to the era of digital pathology. 3,4 In turn, digital pathology has made it possible to streamline the workflow of pathologists, to make remote collaboration between colleagues easier, and to reduce sign-out time, all of which help to improve the performance of anatomic pathology services. 5 The shift to digital slides, along with the availability of large datasets, has enabled the development and integration of artificial intelligence (AI) models, especially deep learning (DL) models tailored specifically for image analysis and computer vision, into digital pathology workflow.…”
Section: Introduction -Background and Significancementioning
confidence: 99%
“…These AI models are designed to assist pathologists with routine, time-consuming tasks, such as cell counting and screening large numbers of biopsies, as well as those with limited reproducibility, such as tumor grading and immunohistochemistry scoring. 3,4,6 As such, AI represents a potential solution to address the shortage of pathologists by streamlining the diagnostic workflow. 7,8 Artificial Neural Networks (ANNs) are softwares that mimic the connections in the human brain; put in series, they can be trained to specific tasks, such as recognizing a dog on a picture -or a melanoma on a slide.…”
Section: Introduction -Background and Significancementioning
confidence: 99%