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Artificial intelligence across oncology specialties: current applications and emerging tools

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posted on 2024-07-09, 08:46 authored by John Kang, Kyle Lafata, Ellen Kim, Christopher Yao, Frank Lin, Tim Rattay, Harsha Nori, Evangelia Katsoulakis, Christoph Ilsuk Lee
Oncology is becoming increasingly personalised through advancements in precision in diagnostics and therapeutics, with more and more data available on both ends to create individualised plans. The depth and breadth of data are outpacing our natural ability to interpret it. Artificial intelligence (AI) provides a solution to ingest and digest this data deluge to improve detection, prediction and skill development. In this review, we provide multidisciplinary perspectives on oncology applications touched by AI—imaging, pathology, patient triage, radiotherapy, genomics-driven therapy and surgery—and integration with existing tools—natural language processing, digital twins and clinical informatics.

Funding

Rapid Applied Research Translation initiative (RARUR000125)

Cancer Institute NSW (2021/CBG003)

Artificial Intelligence for Improved Breast Cancer Screening Accuracy: External Validation, Refinement, and Clinical Translation

National Cancer Institute

Find out more...

Population-Based Evaluation of Artificial Intelligence for Mammography Prior to Widespread Clinical Translation

National Cancer Institute

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NIHR; grant code CL 2017-11-002

History

Citation

BMJ Oncology 2024;3:e000134

Author affiliation

College of Life Sciences Genetics & Genome Biology

Published in

BMJ Oncology

Volume

3

Issue

1

Publisher

BMJ

eissn

2752-7948

Acceptance date

2024-01-03

Copyright date

2024

Available date

2024-07-09

Language

en

Deposited by

Dr Tim Rattay

Deposit date

2024-07-08

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