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<p>The use of Artificial Intelligence (AI) and Machine Learning (ML) has rapidly gained popularity in the treatment of oncology, bringing about a significant shift in approaches to initial screening, cancer detection, therapy, and control. This study aims to discuss basic developments in AI and ML, specifically Deep Learning (DL), Natural Language Processing, Radiomics, and multi-omics analysis in Oncology. The use of DL in the diagnosis of various medical images and genomic data has greatly improved diagnostic results and treatment planning. Radiomics contains detailed features of the tumor and its response to the treatment. Multi-omics analysis, which encompasses genomics, proteomics, transcriptomics, and metabolomics, provides a comprehensive understanding of cancer biology and facilitates the development of personalized medicine therapies. However, the use of AI in oncology also presents some ethical and societal concerns, including patients’ privacy, biased algorithms, and the implementation of technology. Addressing these problems is imperative to help in better implementation of the use of AI in Healthcare. The current review also foregrounds the ethical, clinical, and societal implications of AI adoption, emphasizing the need for robust governance, representative datasets, and interdisciplinary collaboration. The future research can be addressed towards the explainability of developed AI models and improvement in the quality of data. Overcoming the mentioned challenges, the efficient use of AIML can offer various benefits such as enhanced accuracy, better treatment and timely intervention for the patients worldwide.</p> Graphical abstract <p></p>

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A Review on the Applications and Implications of Artificial Intelligence and Machine Learning in Oncology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月30日

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  • Kunal Hiwase,
  • Prateek Verma,
  • Nikita Zade,
  • Praveen Kumar,
  • Induni Nayodhara Weerarathna,
  • Swapnil Gundewar

摘要

The use of Artificial Intelligence (AI) and Machine Learning (ML) has rapidly gained popularity in the treatment of oncology, bringing about a significant shift in approaches to initial screening, cancer detection, therapy, and control. This study aims to discuss basic developments in AI and ML, specifically Deep Learning (DL), Natural Language Processing, Radiomics, and multi-omics analysis in Oncology. The use of DL in the diagnosis of various medical images and genomic data has greatly improved diagnostic results and treatment planning. Radiomics contains detailed features of the tumor and its response to the treatment. Multi-omics analysis, which encompasses genomics, proteomics, transcriptomics, and metabolomics, provides a comprehensive understanding of cancer biology and facilitates the development of personalized medicine therapies. However, the use of AI in oncology also presents some ethical and societal concerns, including patients’ privacy, biased algorithms, and the implementation of technology. Addressing these problems is imperative to help in better implementation of the use of AI in Healthcare. The current review also foregrounds the ethical, clinical, and societal implications of AI adoption, emphasizing the need for robust governance, representative datasets, and interdisciplinary collaboration. The future research can be addressed towards the explainability of developed AI models and improvement in the quality of data. Overcoming the mentioned challenges, the efficient use of AIML can offer various benefits such as enhanced accuracy, better treatment and timely intervention for the patients worldwide.

Graphical abstract