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Abstract
Background: The use of artificial intelligence (AI) in cancer treatment attempts to improve precision and customization. This integration could enhance treatment outcomes, reduce side effects, and optimize healthcare resource allocation as cancer continues to climb globally.
Aims: This study examines how AI advances personalized oncology by predicting treatment responses, improving outcomes, and addressing ethical and privacy challenges.
Methods: The study conducted a systematic review of AI applications in personalized oncology, synthesizing research on machine learning (ML) and deep learning (DL) in diagnostics, prognostics, and treatment personalization. It reviewed AI’s role in analyzing multi-omics, clinical, and imaging data for cancer therapy selection. Primary data analysis using Smart PLS software further assessed AI’s effectiveness in treatment recommendations, emphasizing the need for data standardization and validation for clinical integration.
Results: This review found that predictive modeling with biomarkers, multi-omics, and histopathology data enables AI to analyze complex cancer datasets, enhancing diagnostic and treatment outcomes. DL and ML contribute to personalized oncology by predicting patient responses and identifying treatment targets. However, challenges such as data standardization, algorithm transparency, and ethical considerations need to be addressed to ensure the responsible use of AI in this field.
Conclusion: The potential of AI to enhance the precision of cancer treatment and personalize patient care while acknowledging challenges such as data transparency, ethical sharing, and collaboration is highly likely. Ongoing research and integrating various ML methods are crucial for successfully implementing these advancements in clinical practice.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Review Article
ELECTRON J GEN MED, Volume 22, Issue 6, December 2025, Article No: em689
https://doi.org/10.29333/ejgm/17046
Publication date: 01 Nov 2025
Online publication date: 16 Sep 2025
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Article Downloads: 2
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