Artificial intelligence in oncology: current applications and future perspectives

British Journal of Cancer, Journal Year: 2021, Volume and Issue: 126(1), P. 4 - 9

Published: Nov. 26, 2021

Abstract Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving management cancer patients. Analysing AI-based devices that have already obtained official approval by Federal Drug Administration (FDA), here we show diagnostics oncology-related area in which AI entered with largest impact into clinical practice. Furthermore, breast, lung prostate cancers represent specific types now are experiencing more advantages from devices. The future perspectives oncology discussed: creation multidisciplinary platforms, comprehension importance all neoplasms, including rare tumours continuous support guaranteeing its growth this time most challenges finalising ‘AI-revolution’ oncology.

Language: Английский

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Beware explanations from AI in health care

Science, Journal Year: 2021, Volume and Issue: 373(6552), P. 284 - 286

Published: July 15, 2021

The benefits of explainable artificial intelligence are not what they appear

Language: Английский

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Swarm learning for decentralized artificial intelligence in cancer histopathology

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(6), P. 1232 - 1239

Published: April 25, 2022

Abstract Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in partners jointly train models while avoiding transfer monopolistic governance. Here, we demonstrate successful use SL large, multicentric gigapixel images over 5,000 patients. We show that trained using BRAF mutational status microsatellite instability hematoxylin eosin (H&E)-stained pathology slides colorectal cancer. on three patient cohorts Northern Ireland, Germany United States, validated prediction performance two independent Kingdom. Our SL-trained outperform most locally models, perform par are merged datasets. In addition, SL-based efficient. future, used to distributed any image analysis task, eliminating need transfer.

Language: Английский

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Machine learning in clinical decision making

Med, Journal Year: 2021, Volume and Issue: 2(6), P. 642 - 665

Published: April 30, 2021

Language: Английский

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A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI

Nature Medicine, Journal Year: 2021, Volume and Issue: 27(10), P. 1663 - 1665

Published: Oct. 1, 2021

Language: Английский

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