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.
- computerised image analysis
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Twitter @Caldera60373705, @jnkath
Contributors JC and JNK drafted the manuscript and revision for its intellectual content.
Funding JC is supported by Fondation Bristol-Myers-Squibb pour la Recherche en Immuno-Oncologie and Fondation de l’Avenir.
Competing interests JC receives consulting fees from Owkin (New York, New York, USA) and Crosscope (San Francisco, California, USA). JNK has an informal, unpaid advisory role at Pathomix (Heidelberg, Germany) which does not relate to this research.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
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