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Oncological Applications of Quantum Machine Learning.

Milad Rahimi1, Farkhondeh Asadi1

  • 1Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Technology in Cancer Research & Treatment
|December 18, 2023
PubMed
Summary
This summary is machine-generated.

Quantum machine learning (QML) shows promise in oncology, improving cancer diagnosis and treatment. This review highlights QML

Keywords:
cancermachine learningoncologyquantum computerquantum machine learning

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Oncology

Background:

  • Cancer remains a major global health challenge.
  • Significant advancements in machine learning (ML) and quantum computing (QC) are emerging.
  • Quantum machine learning (QML) demonstrates potential advantages over classical ML in healthcare.

Purpose of the Study:

  • To review and report the specific applications of QML in oncology.
  • To synthesize current research on QML's role in cancer care.

Main Methods:

  • Comprehensive literature search conducted in March 2023 across major scientific databases (PubMed, Scopus, Web of Science, IEEE, Cochrane).
  • Screening of retrieved articles based on titles and abstracts, followed by full-text examination.
  • Systematic review of selected studies focusing on QML in oncology.

Main Results:

  • Nine relevant articles were included after an initial retrieval of 207 studies.
  • Most included studies were published from 2020 onwards, indicating recent progress.
  • Identified QML applications include mammography image noise reduction, breast cancer edge detection, radiotherapy clinical decision support, and cancer classification.

Conclusions:

  • The integration of quantum science and ML offers significant potential to enhance patient care and clinical outcomes in oncology.
  • Further research is needed to explore QC-ML integration and develop novel algorithms for improved cancer prognosis, diagnosis, and treatment planning.