Artificial Intelligence and Digital Pathology: Challenges and Opportunities

By Hamid R. Tizhoosh, Liron Pantanowitz. Journal of Pathology Informatics, 2018

470 citations recorded by the source corpus.

In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e4082122602b47d986de04cdbbc533a83291e77e

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Citations in the corpus

Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, background): “Since the nature of diagnostic tasks causes a lack of transparency, current AI systems used in healthcare are considered as a black box to users, which acts as a barrier to the adoption of AI technology [52].”
Lung and Colon Cancer Histopathological Image Dataset (LC25000) (2019, methodology): “[3][4] ML allows representative images to be used to train a computer to recognize patterns from labeled photographs.”
Digital Pathology and Artificial Intelligence (2019, methodology): “Further, we explore how AI has advanced these areas of digital pathology as well as specific use cases and applications of AI in research, image analysis, CAD, and with a discussion of the techniques used, challenges, and barriers [3].”
Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association (2019, background): “Training a deep learning network on an entire slide image at full resolution is currently very challenging, so it usually operates on a smaller tiled image or patch [33].”
Artificial intelligence (AI) and big data in cancer and precision oncology (2020, background): “These systems also require additional quality processes [110].”
A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis (2019, methodology): “state-of-the-art technique for performing many medical image analysis tasks (Tizhoosh and Pantanowitz, 2018; Shen et al., 2017; Litjens et al., 2017; Suzuki, 2017).”
Emerging role of deep learning‐based artificial intelligence in tumor pathology (2020, background): “Reimbursement of the costs of AI-based diagnostic and prognostic/predictive assays is one of the major issues that affect the application of these assays in clinic [91].”