【AI Explained】Current AI technologies in cancer diagnostics and treatment.

Literature Information

TL;DR

This review examines the transformative role of artificial intelligence (AI) in oncology, focusing on its applications in early cancer detection, personalized treatment planning, and patient management through advanced imaging, genomics, and therapeutic interventions. By highlighting the potential of AI to enhance precision and efficiency in cancer care, the study underscores the need to address challenges such as data privacy and regulatory issues to fully realize AI’s benefits in the field.

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Artificial intelligence (AI) · Cancer · Cancer diagnosis · Deep learning (DL) · Machine learning (ML)

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.

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Primary Questions Addressed

1. How are AI technologies being integrated into existing cancer diagnostic workflows to enhance accuracy and efficiency?
2. What specific AI algorithms are currently most effective in identifying biomarkers for various types of cancer?
3. In what ways are AI-driven clinical decision support systems changing the landscape of personalized cancer treatment?
4. What challenges do researchers face when implementing AI in liquid biopsy technologies for cancer detection?
5. How is the role of AI evolving in the management of cancer patient data, particularly concerning privacy and regulatory compliance?

Key Findings

Research Topic and Scope

This review by Tiwari et al. (2025) provides a comprehensive overview of the current applications of artificial intelligence (AI) in cancer diagnostics and treatment. It explores various AI technologies and their integration into different aspects of oncology, including imaging diagnostics, genomics, biomarker discovery, treatment planning, and patient management.

Main Findings and Perspectives

1. AI in Imaging Diagnostics: The review highlights the significant advancements in AI-facilitated imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and digital pathology. AI, particularly deep learning, enhances early cancer detection and improves diagnostic accuracy by analyzing complex imaging data.

2. Genomics and Biomarker Discovery: AI plays a critical role in analyzing genomic data and identifying novel biomarkers. Machine learning (ML) techniques are employed to stratify cancer patients based on genetic profiles, aiding in personalized treatment approaches.

3. Therapeutic Interventions: AI is transforming precision cancer therapies through clinical decision support systems, individualized treatment planning, and AI-driven drug discovery. The review discusses the potential of AI in optimizing radiation therapy and robotic surgery, enhancing treatment outcomes.

4. Patient Management: AI facilitates remote monitoring and personalized patient care, improving adherence to treatment and enabling timely interventions. AI-powered platforms assist in clinical trial management, optimizing patient recruitment, and enhancing trial design.

5. Challenges and Limitations: Despite the promising applications, the review identifies several challenges in AI integration into oncology, including data privacy concerns, interpretability of AI models, and the need for robust regulatory frameworks. There is a significant reliance on high-quality, standardized data for training AI models to ensure their effectiveness and reliability.

Research Progress

The review outlines the rapid evolution of AI technologies in oncology, emphasizing milestones such as:

• Development of AI algorithms for improved imaging diagnostics.
• Integration of AI in genomic analyses leading to better patient stratification.
• Enhanced capabilities in drug discovery and treatment optimization through AI.

Controversies and Gaps

Current research faces controversies regarding the ethical implications of AI in clinical decision-making and the potential biases inherent in AI training datasets. The review highlights the need for ongoing discussions about the balance between innovation and ethical standards in AI applications.

Future Research Directions

The authors suggest several future research directions, including:

• Exploration of federated learning to enhance data privacy while leveraging diverse datasets.
• Development of synthetic biology and quantum computing applications in AI for cancer care.
• Continued focus on patient-centered design in AI tools to ensure usability and effectiveness in clinical settings.

Conclusion and Significance

The review concludes that AI has the potential to revolutionize cancer care by enhancing diagnostics, treatment precision, and patient management. However, successful implementation requires addressing ethical concerns, improving data standardization, and ensuring transparency in AI applications. The integration of AI in oncology represents a transformative shift towards more personalized, efficient, and effective cancer care.

Limitations and Future Directions

The authors acknowledge the limitations of current AI systems, including the need for extensive validation in clinical settings and the challenges of integrating AI tools into existing healthcare workflows. Future research should focus on developing robust frameworks that ensure ethical AI deployment while maximizing its benefits in cancer treatment and management.

References

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Literatures Citing This Work

1. OncoTrace-TOO: Interpretable Machine Learning Framework for Cancer Tissue-of-Origin Identification Using Transcriptomic Signatures. - Yang Hao;Haochun Huang;Daiyun Huang;Jianwen Ruan;Xin Liu;Jianquan Zhang - Cancer reports (Hoboken, N.J.) (2025)
2. The Quest for Non-Invasive Diagnosis: A Review of Liquid Biopsy in Glioblastoma. - Maria George Elias;Harry Hadjiyiannis;Fatemeh Vafaee;Kieran F Scott;Paul de Souza;Therese M Becker;Shadma Fatima - Cancers (2025)
3. Revolutionizing Oncology Through AI: Addressing Cancer Disparities by Improving Screening, Treatment, and Survival Outcomes via Integration of Social Determinants of Health. - Amit Kumar Srivastav;Aryan Singh;Shailesh Singh;Brian Rivers;James W Lillard;Rajesh Singh - Cancers (2025)
4. Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges. - Qaiser Abbas;Woonyoung Jeong;Seung Won Lee - Healthcare (Basel, Switzerland) (2025)
5. Clinical relevance of circulating tumor DNA in HER2-positive advanced gastric cancer: a collaborative study of a phase Ib trial of dual HER2 and PD-1 targeted therapy (Ni-High). - Hiroki Osumi;Takeru Wakatsuki;Akira Ooki;Keisho Chin;Hirokazu Shoji;Mariko Ogura;Izuma Nakayama;Noriko Yamamoto;Hidekazu Hirano;Hiroki Hara;Keiko Minashi;Eiji Shinozaki;Ken Kato;Naoki Ishizuka;Shigehisa Kitano;Kengo Takeuchi;Narikazu Boku;Kensei Yamaguchi;Daisuke Takahari - Therapeutic advances in medical oncology (2025)
6. Revolutionizing spine surgery with emerging AI-FEA integration. - Christopher Franceschini;Mohsen Ahmadi;Xuanzong Zhang;Kelly Wu;Maohua Lin;Ridge Weston;Angela Rodio;Yufei Tang;Erik Engeberg;Gui Pires;Talha S Cheema;Frank D Vrionis - Journal of robotic surgery (2025)
7. Breast Cancer: Molecular Pathogenesis and Targeted Therapy. - Md Abdus Samad;Iftikhar Ahmad;Mohammad Rashid Khan;Mohd Suhail;Torki A Zughaibi;Fahad A Al-Abbasi;Khaled A Alhosaini;Mohd Shahnawaz Khan;Ajoy Kumer;Shams Tabrez - MedComm (2025)

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