Journal of Ai ML DL
Journal of Ai ML DL
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Journal of Ai ML DL

Volume 1 Number 1 2025

Article Contents

REVIEWS   (Open Access)
Contents Vol 1 (1)

Machine learning in cancer biology: transforming diagnosis, prognosis, and treatment in modern medical research

Tufael1*, Md. Moyen Uddin PK2, 3

+ Author Affiliations

Journal of Ai ML DL 1 (1) 1-10 https://doi.org/10.25163/ai.1110405

Submitted: 30 June 2025 Revised: 16 September 2025  Published: 24 September 2025 

Abstract

Machine learning (ML) has emerged as a transformative tool in cancer biology, profoundly impacting diagnosis, prognosis, and treatment. ML algorithms make it possible to identify patterns and predictive markers in complex datasets from imaging, genomics, multiomics, and clinical records that are more accurate and efficient than those found with conventional techniques. In order to aid in early detection, tumour segmentation, and risk stratification, machine learning models have been applied to DNA sequences, mammograms, and histopathological images in the diagnosis of cancer. The integration of genetic, clinical, and tumor-specific data has improved prognosis, survival prediction, and recurrence risk models, enhancing patient stratification and guiding tailored interventions. Precision oncology has been made possible by ML-driven treatment applications that have expedited drug discovery, improved clinical trial design, and allowed for customised therapeutic approaches based on molecular tumour profiles. Significant obstacles still exist despite these developments, such as model interpretability, algorithmic bias, data privacy issues, and constraints in the healthcare system. For fair and successful clinical implementation, these problems must be resolved by strong regulatory frameworks, cooperative research projects, human-AI integration, and ongoing model evaluation. The ethical application of ML technologies in oncology, generalisability, and transparency are key future directions. With the potential to improve clinical decision-making, patient outcomes, and healthcare delivery disparities, machine learning (ML) has been positioned as a major force behind the shift towards data-driven, precision cancer care. The current status, difficulties, and exciting prospects of machine learning in contemporary cancer research and clinical practice are highlighted in this review.

Keywords: Machine learning, cancer diagnosis, cancer prognosis, precision oncology, artificial intelligence

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