Emerging AI paradigms offer future-ready frameworks for personalized, privacy-preserving oncology care and multi-omics integration powered by AI enables the discovery of robust biomarkers for diagnosis, prognosis, and therapy response prediction.
Abstract
Highlights • Artificial intelligence (AI) is transforming the cancer research from drug discovery and biomarker identification to diagnosis and therapy.• Deep learning frameworks accelerate oncology drug discovery and repurposing, reducing timelines and costs through predictive modelling and molecular simulation.• Multi-omics integration powered by AI enables the discovery of robust biomarkers for diagnosis, prognosis, and therapy response prediction.• Federated learning, digital twins, and explainable AI guide future clinical use.• Emerging AI paradigms offer future-ready frameworks for personalized, privacy-preserving oncology care.
Artificial intelligence (AI) and Machine learning are transforming drug discovery in prostate cancer by addressing the biological heterogeneity, therapeutic resistance, and inefficiencies inherent in traditional pipelines. By integrating multi-omics, imaging, and clinical datasets, Artificial intelligence/Machine learn...
A. Musa, Muhammad Lawan Wasaram, Kashim Ibrahim Muhammad et al.· International Journal of App...· 0 citations
Background: Chemotherapy is a fundamental and rapidly acting modality for cancer treatment that employs specific anticancer agents such as cyclophosphamide, 5-fluorouracil, and methotrexate. It is administered either with curative intent, typically through defined drug combinations, or with palliative intent, aiming to...
Majid Monajjemi, Oktay Bıyıklıoğlu, F. Mollaamin et al.· Revista Colombiana de Cienci...· 0 citations
Artificial intelligence (AI) is increasingly transforming cancer management by enabling the analysis of complex multimodal data generated across the cancer care continuum. This structured narrative review synthesizes current applications of AI, machine learning, and deep learning in cancer detection, imaging diagnosis,...
Eva Rahman Kabir, N. Mustafa, Zara Sheikh et al.· Discover Applied Sciences· 0 citations
This review discusses how AI can accelerate computational drug discovery, virtual screening (VS), and the repurposing of already-approved medications, and it can also optimize combination therapies through computational simulation.
D. Bhagwat, Sonali A. Chikhalwale, S. Mali et al.· Current pharmaceutical desig...· 0 citations
This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches, and describes future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology.
Kanishk Yadav, Taneesha Gupta· Journal of the Egyptian Nati...· 0 citations
Overall, AI is becoming an integral component of modern oncology, particularly radiation oncology, and its successful integration into routine clinical practice will require robust validation, transparent governance, equitable implementation, and continued clinician oversight to ensure safe, effective, and patient-cent...
K. Rastogi· The Rise of Artificial Intel...· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 23, 2026
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
