Artificial intelligence and machine learning in cancer imaging

Dow-Mu Koh; Nickolas Papanikolaou; Ulrich Bick; Rowland Illing; Charles E. Kahn; Jayshree Kalpathi-Cramer; Celso Matos; Luis Martí-Bonmatí; Anne Miles; Seong Ki Mun; Sandy Napel; Andrea Rockall; Evis Sala; Nicola Strickland; Fred Prior

doi:10.1038/s43856-022-00199-0

Communications Medicine (Oct 2022)

Artificial intelligence and machine learning in cancer imaging

Dow-Mu Koh,
Nickolas Papanikolaou,
Ulrich Bick,
Rowland Illing,
Charles E. Kahn,
Jayshree Kalpathi-Cramer,
Celso Matos,
Luis Martí-Bonmatí,
Anne Miles,
Seong Ki Mun,
Sandy Napel,
Andrea Rockall,
Evis Sala,
Nicola Strickland,
Fred Prior

Affiliations

Dow-Mu Koh: Department of Radiology, Royal Marsden Hospital
Nickolas Papanikolaou: Department of Radiology, Royal Marsden Hospital
Ulrich Bick: Charité – Universitätsmedizin Berlin
Rowland Illing: Department of Surgery & Interventional Science, University College London
Charles E. Kahn: Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania
Jayshree Kalpathi-Cramer: Centre for machine learning, Massachusetts General Hospital/Harvard Medical School
Celso Matos: Champalimaud Foundation
Luis Martí-Bonmatí: Department of Radiology, Hospital Universitari i Politècnic La Fe
Anne Miles: Department of Psychological Sciences, Birkbeck University
Seong Ki Mun: Arlington Innovation Center for Health Research, Virginia Tech
Sandy Napel: Department of Radiology, Stanford University
Andrea Rockall: Department of Radiology, Imperial College Healthcare NHS Trust
Evis Sala: Department of Radiology, Cambridge University
Nicola Strickland: Department of Radiology, Imperial College Healthcare NHS Trust
Fred Prior: Department of Biomedical Informatics and Department of Radiology, University of Arkansas for Medical Sciences

DOI: https://doi.org/10.1038/s43856-022-00199-0
Journal volume & issue: Vol. 2, no. 1
pp. 1 – 14

Abstract

Read online

Koh, Papanikolaou et al. discuss the application of artificial intelligence in cancer imaging. The authors highlight opportunities for exploiting machine learning algorithms in this field, and outline barriers in their implementation and how these might be addressed.

Published in Communications Medicine

ISSN: 2730-664X (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Medicine
Website: https://www.nature.com/commsmed/

About the journal