Current AI technologies in cancer diagnostics and treatment
Abstract Cancer 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...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | Molecular Cancer |
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| Online Access: | https://doi.org/10.1186/s12943-025-02369-9 |
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| author | Ashutosh Tiwari Soumya Mishra Tsung-Rong Kuo |
| author_facet | Ashutosh Tiwari Soumya Mishra Tsung-Rong Kuo |
| author_sort | Ashutosh Tiwari |
| collection | DOAJ |
| description | Abstract Cancer 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 cancer 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 applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, 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 recommend 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. Graphical Abstract This graphical abstract schematically illustrates the progressive role of artificial intelligence in the cancer treatment continuum. |
| format | Article |
| id | doaj-art-ff3b06242b2441678aae59ddce7bd2c7 |
| institution | DOAJ |
| issn | 1476-4598 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Molecular Cancer |
| spelling | doaj-art-ff3b06242b2441678aae59ddce7bd2c72025-08-20T03:10:29ZengBMCMolecular Cancer1476-45982025-06-0124114110.1186/s12943-025-02369-9Current AI technologies in cancer diagnostics and treatmentAshutosh Tiwari0Soumya Mishra1Tsung-Rong Kuo2International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei Medical UniversityDepartment of Biotechnology, School of Interdisciplinary Education and Research, Guru Ghasidas VishwavidyalayaInternational Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei Medical UniversityAbstract Cancer 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 cancer 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 applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, 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 recommend 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. Graphical Abstract This graphical abstract schematically illustrates the progressive role of artificial intelligence in the cancer treatment continuum.https://doi.org/10.1186/s12943-025-02369-9CancerArtificial intelligence (AI)Machine learning (ML)Cancer diagnosisDeep learning (DL)Precision oncology |
| spellingShingle | Ashutosh Tiwari Soumya Mishra Tsung-Rong Kuo Current AI technologies in cancer diagnostics and treatment Molecular Cancer Cancer Artificial intelligence (AI) Machine learning (ML) Cancer diagnosis Deep learning (DL) Precision oncology |
| title | Current AI technologies in cancer diagnostics and treatment |
| title_full | Current AI technologies in cancer diagnostics and treatment |
| title_fullStr | Current AI technologies in cancer diagnostics and treatment |
| title_full_unstemmed | Current AI technologies in cancer diagnostics and treatment |
| title_short | Current AI technologies in cancer diagnostics and treatment |
| title_sort | current ai technologies in cancer diagnostics and treatment |
| topic | Cancer Artificial intelligence (AI) Machine learning (ML) Cancer diagnosis Deep learning (DL) Precision oncology |
| url | https://doi.org/10.1186/s12943-025-02369-9 |
| work_keys_str_mv | AT ashutoshtiwari currentaitechnologiesincancerdiagnosticsandtreatment AT soumyamishra currentaitechnologiesincancerdiagnosticsandtreatment AT tsungrongkuo currentaitechnologiesincancerdiagnosticsandtreatment |