Artificial intelligence in cancer imaging: Clinical challenges and applications

Bi, Wenya Linda; Hosny, Ahmed; Schabath, Matthew B.; Giger, Maryellen L.; Birkbak, Nicolai J.; Mehrtash, Alireza; Allison, Tavis; Arnaout, Omar; Abbosh, Christopher; Dunn, Ian F.; Mak, Raymond H.; Tamimi, Rulla M.; Tempany, Clare M.; Swanton, Charles; Hoffmann, Udo; Schwartz, Lawrence H.; Gillies, Robert J.; Huang, Raymond Y.; Aerts, Hugo J.W.L.

doi:10.3322/caac.21552

reviewCA A Cancer Journal for CliniciansFeb 5, 2019DIAMOND OA

Artificial intelligence in cancer imaging: Clinical challenges and applications

WLWenya Linda Bi AHAhmed Hosny MBMatthew B. Schabath MLMaryellen L. Giger NJNicolai J. Birkbak

Brigham and Women's Hospital · Harvard University · +14 more institutions

PubMed

Indexed incrossrefpubmed

Abstract

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI)…

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Topics

Keywords

Medicine
Context (archaeology)
Workflow
Generalizability theory
Medical physics
Precision medicine
Disease
Medical imaging

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