Reply to the letter to the editor: ‘Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images’

Hekler, Achim; Utikal, Jochen; Solaß, Wiebke; Schmitt, Max; Klode, Joachim; Schadendorf, Dirk; Sondermann, Wiebke; Franklin, Cindy; Bestvater, Felix; Krahl, Dieter; Kalle, Christof von; Fröhling, Stefan; Brinker, Titus J.

doi:10.1016/j.ejca.2019.12.024

European Journal of Cancer

2020

DOI: 10.1016/j.ejca.2019.12.024

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Reply to the letter to the editor: ‘Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images’

Achim Hekler

Jochen Utikal

Wiebke Solaß

et al.

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2020

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Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

Caldéraro

Kather

2020

Gut

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Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complementing human observers, AI allows an in-depth analysis of digitised histological slides of GI and liver cancer and offers a wide range of clinically relevant applications. First, AI can automatically detect tumour tissue, easing the exponentially increasing workload on pathologists. In addition, and possibly exceeding pathologist’s capacities, AI can capture prognostically relevant tissue features and thus predict clinical outcome across GI and liver cancer types. Finally, AI has demonstrated its capacity to infer molecular and genetic alterations of cancer tissues from histological digital slides. These are likely only the first of many AI applications that will have important clinical implications. Thus, pathologists and clinicians alike should be aware of the principles of AI-based pathology and its ability to solve clinically relevant problems, along with its limitations and biases.

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Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

Caldéraro

Kather

2020

Gut

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Reply to the letter to the editor: ‘Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images’

Cited by 1 publication

References 6 publications

Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers

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