Innovations in Artificial Intelligence-Driven Breast Cancer Survival Prediction: A Narrative Review

Mehwish Mooghal, Saad Nasir, Aiman Arif, Wajiha Khan, Yasmin Abdul Rashid, Lubna M. Vohra

Research output: Contribution to journalArticlepeer-review

Abstract

This narrative review explores the burgeoning field of Artificial Intelligence (AI)-driven Breast Cancer (BC) survival prediction, emphasizing the transformative impact on patient care. From machine learning to deep neural networks, diverse models demonstrate the potential to refine prognosis accuracy and tailor treatment strategies. The literature underscores the need for clinician integration and addresses challenges of model generalizability and ethical considerations. Crucially, AI’s promise extends to Low- and Middle-Income Countries (LMICs), presenting an opportunity to bridge healthcare disparities. Collaborative efforts in research, technology transfer, and education are essential to empower healthcare professionals in LMICs. As we navigate this frontier, AI emerges not only as a technological advancement but as a guiding light toward personalized, accessible BC care, marking a significant stride in the global fight against this formidable disease.

Original languageEnglish
JournalCancer Informatics
Volume23
DOIs
Publication statusPublished - 1 Jan 2024

Keywords

  • artificial intelligence
  • Breast cancer
  • machine learning
  • prognostic models
  • survival prediction

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