Abstract
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.
Funder
Cancer Institute NSW
Kuni Foundation
Innovate UK
National Cancer Institute
NIHR Leicester Biomedical Research Centre
Association of Breast Surgery
Rapid Applied Research Translation initiative
UK National Institute for Health Research
Reference125 articles.
1. Miliard M . Google, Verily Using AI to Screen for Diabetic Retinopathy in India. Healthcare IT News 2019. Available: https://www.healthcareitnews.com/news/asia/google-verily-using-ai-screen-diabetic-retinopathy-india [accessed 14 May 2023].
2. Real-world experience with artificial intelligence-based triage in transferred large vessel occlusion stroke patients;Morey;Cerebrovasc Dis,2021
3. Hwang AB , Schuepfer G , Pietrini M , et al . External validation of EPIC’s risk of unplanned readmission model, the LACE+ index and Sqlape as predictors of unplanned hospital readmissions: a monocentric, retrospective, diagnostic cohort study in Switzerland. PLoS ONE 2021;16:e0258338. doi:10.1371/journal.pone.0258338
4. Kang J , Rancati T , Lee S , et al . Machine learning and radiogenomics: lessons learned and future directions. Front Oncol 2018;8:228. doi:10.3389/fonc.2018.00228
5. Micheel C , Nass SJ , Omenn GS , et al . Committee on the review of Omics-based tests for predicting patient outcomes in clinical trials. In: Evolution of Translational Omics: Lessons Learned and the Path Forward. National Academies Press, 2012. doi:10.17226/13297