AI Tools Offering Cancer Clinical Applications for Risk Predictor, Early Detection, Diagnosis, and Accurate Prognosis: Perspectives in Personalised Care

Richard Khanyile*, Rahaba Marima, Mandisa Mbeje, Shingai Mutambirwa, Daniel Montwedi, Zodwa Dlamini

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

Artificial intelligence (AI) is transforming the medical research and clinical workflow by enhancing oncology clinical applications. AI-based tools are emerging as key role players in advancing precision oncology by improving oncology clinical applications in cancer risk prediction, early detection and diagnosis and accurate prognosis. Although there are challenges with every newly developed technology, efforts and significant investments have been placed to ensure the success of this technology. Additionally, the introduction of sophisticated AI-medical devices demonstrates the fundamental role that AI holds to offer in oncology. Several AI-tools have illustrated high performance towards cancer care and management in various parts of the world. While risk prediction, early detection, diagnosis and accurate prognosis are a work in progress in some cancer types, this remains a challenge in various cancers. However, AI-based tools can advance human efforts with the overall aim of improving oncology patient outcome through personalised care. This chapter will focus on AI-based tools in advancing oncology personalised care by improving risk prediction, early detection and diagnosis, and accurate prognosis. Challenges in the application of AI-based tools from bench to bedside will also be discussed, while providing an overview of AI-based tools for predicting clinically relevant parameters in advancing precision oncology.

Original languageEnglish
Title of host publicationArtificial Intelligence and Precision Oncology
Subtitle of host publicationBridging Cancer Research and Clinical Decision Support
PublisherSpringer Nature
Pages293-312
Number of pages20
ISBN (Electronic)9783031215063
ISBN (Print)9783031215056
DOIs
Publication statusPublished - 1 Jan 2023
Externally publishedYes

Keywords

  • Accurate prognosis
  • Artificial intelligence
  • Clinical applications
  • Deep learning (DL)
  • Diagnosis
  • Early detection
  • Precision oncology

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