Affiliations 

  • 1 Department of Pharmacy, Al Rafidain University College, Baghdad, Iraq
  • 2 School of Pharmacy, Monash University Malaysia, Subang Jaya, Malaysia
  • 3 School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
  • 4 College of Pharmacy, Gulf Medical University, Ajman, United Arab Emirates
  • 5 Institut Coeur et Cerveau de l'Est Parisien (ICCE), UPEC-University Paris-Est, Creteil, France
Front Oncol, 2025;15:1475893.
PMID: 39990683 DOI: 10.3389/fonc.2025.1475893

Abstract

BACKGROUND: Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications in chemotherapy development, cancer diagnosis, and predicting chemotherapy response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) and deep learning (DL).

OBJECTIVE: This review aims to explore the role of AI in forecasting outcomes related to chemotherapy development, cancer diagnosis, and treatment response, synthesizing current advancements and identifying critical gaps in the field.

METHODS: A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and Cochrane databases up to 2023. Keywords included "Artificial Intelligence (AI)," "Machine Learning (ML)," and "Deep Learning (DL)" combined with "chemotherapy development," "cancer diagnosis," and "cancer treatment." Articles published within the last four years and written in English were included. The Prediction Model Risk of Bias Assessment tool was utilized to assess the risk of bias in the selected studies.

CONCLUSION: This review underscores the substantial impact of AI, including ML and DL, on cancer diagnosis, chemotherapy innovation, and treatment response for both solid and hematological tumors. Evidence from recent studies highlights AI's potential to reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing treatment plans, and improving therapeutic outcomes. Future research should focus on addressing challenges in clinical implementation, ethical considerations, and scalability to enhance AI's integration into oncology care.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.