Artificial intelligence for clinical oncology

Benjamin H. Kann, Ahmed Hosny, Hugo J. W. L. Aerts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Web of Science)
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Abstract

Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer care. With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of multi-dimensional data, deduce patterns, and predict outcomes to improve shared patient and clinician decision making. While there is high potential, significant challenges remain. In this perspective, we propose a pathway of clinical cancer care touchpoints for narrow-task AI applications and review a selection of applications. We describe the challenges faced in the clinical translation of AI and propose solutions. We also suggest paths forward in weaving AI into individualized patient care, with an emphasis on clinical validity, utility, and usability. By illuminating these issues in the context of current AI applications for clinical oncology, we hope to help advance meaningful investigations that will ultimately translate to real-world clinical use.

Original languageEnglish
Pages (from-to)916-927
Number of pages12
JournalCancer Cell
Volume39
Issue number7
DOIs
Publication statusPublished - 12 Jul 2021

Keywords

  • TUMOR HETEROGENEITY
  • RADIATION-THERAPY
  • CANCER-PATIENTS
  • MEDICINE
  • RADIOTHERAPY
  • INFORMATION
  • PERFORMANCE
  • NETWORK
  • PATIENT
  • RISK

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