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This review systematically surveys current AI technologies for cancer diagnostics and treatment, covering AI-enabled imaging across CT, MRI, PET, ultrasound, and digital pathology, with an emphasis on deep learning for early-stage detection. It also examines AI roles in genomics, biomarker discovery, liquid biopsies, and non-invasive diagnostics, alongside clinical decision support, treatment planning, drug discovery, radiation therapy, robotic surgery, patient management, survival prediction, remote monitoring, and AI-facilitated trials. 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Molecular Cancer (2025) 24:159 [https://doi.org/10.1186/s12943-025-02369-9](https://doi.org/10.1186/s12943-025-02369-9)  \nMolecular Cancer  \n REVIEW Open Access  \nCurrent AI technologies in cancer diagnostics and treatment  \nAshutosh Tiwari1,2, Soumya Mishra3 and Tsung‑Rong Kuo 1,4,5*  \nAbstract  \nCancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of can‑ cer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI‑facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early‑stage cancers. We also explore applica‑ tions of AI in genomics and biomarker discovery, liquid biopsies, and non‑invasive diagnoses. In therapeutic interven‑ tions, AI‑based clinical decision support systems, individualized treatment planning, and AI‑facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI‑facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recom‑ mend future directions that involve the use of federated learning, synthetic biology, and quantum‑boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized.  \nKeywords Cancer, Artificial intelligence (AI), Machine learning (ML), Cancer diagnosis, Deep learning (DL), Precision oncology  \n*Correspondence: Tsung‑Rong Kuo [trkuo@tmu.edu.tw](trkuo@tmu.edu.tw)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/.)[.](http://creativecommons.org/licenses/by/4.0/.)  \nTiwari etal. Molecular Cancer (2025) 24:159 Page 2 of 41  \nGraphical Abstract  \nThis graphical abstract schematically illustrates the progressive role of artificial intelligence in the cancer treatment continuum.  \nIntroduction  \nCancer, an illness that can affect people from all walks of life, is an intricate worldwide health concern that continues to require attention. Cancer is a disease that affects people regardless of age and causes suffering all around the world. Cancer is the second most prevalent cause of mortality worldwide, accounting for one in six deaths in 2020, according to the World Health Organization (WHO) [1]. Through a gradual accumulation of biological and therapeutic knowledge which accelerated with the development of molecul","cbCaiv0wP70iENBx","https://ap.wps.com/l/cbCaiv0wP70iENBx","pdf",3062989,41,"English","# Abstract\n# Introduction\n## The role of artificial intelligence (AI) in modern oncology","[{\"question\":\"What areas of cancer care does the review cover regarding AI technologies?\",\"answer\":\"The review covers AI-enabled imaging diagnostics, AI applications in genomics and biomarker discovery, liquid biopsies and non-invasive diagnoses, and AI in therapeutic decision-making and support across multiple treatment domains.\"},{\"question\":\"How does the review describe AI use in diagnostics?\",\"answer\":\"It highlights AI-facilitated imaging diagnostics across CT, MRI, PET, ultrasound, and digital pathology, emphasizing deep learning for detecting early-stage cancers, along with non-invasive approaches.\"},{\"question\":\"What challenges and future directions does the review identify for AI in oncology?\",\"answer\":\"The review discusses challenges including data privacy, interpretability, and regulatory issues, and proposes future directions such as federated learning, synthetic biology, and quantum-boosted AI.\"}]","Current AI technologies in cancer diagnostics and treatment | PDF",1790183356,103]