Investigating the Application of Quantum Machine Learning in Breast Cancer: A Systematic Review
shahrzad kaveh, Elahe Arezi, Zahra Khedri, Solmaz Sohrabei · Archives of Breast Cancer · 2025
Background: Breast cancer diagnostic data is complex and accompanied by noise. Quantum machine learning can enhance the accuracy, efficiency, and scalability of artificial intelligence algorithms and has applications in various fields such as drug discovery and personalized medicine. Methods: In the systematic review conducted, the databases PubMed, Embase, Scopus, and Web of Science were searched in December 2024. The search strategy included the keywords "Breast Cancer," "Artificial Intelligence," and "Quantum machine learning" along with their synonyms in article titles. Descriptive, qualitative, review, and non-English studies were excluded. The qualitative evaluation of the articles and the assessment of their bias were determined based on the Joanna Briggs Institute (JBI) indicators checklist. Results: Twenty-nine studies utilizing artificial intelligence models for personalized breast cancer management were selected. Seventeen studies employing various deep learning methods achieved satisfactory results in predicting treatment response and prognosis, effectively contributing to the personalized management of breast cancer. Twenty-six studies demonstrated that machine learning methods could enhance the processes of classification, screening, diagnosis, and prognosis of breast cancer. The methods most frequently used in modeling were quantum support vector machine (QSVM), quantum convolutional neural network (QCNN), and quantum neural network (QNN), with an average AUC of 0.91. Additionally, the average accuracy, sensitivity, specificity, and precision indices of the models ranged from 90% to 96%. Conclusion: Quantum computing can address some challenges arising from the increasing complexity and size of artificial intelligence models. Overall, the combination of artificial intelligence and quantum computing can significantly accelerate the drug discovery process and the development of effective drugs.