Artificial intelligence in oncology: current landscape, challenges, and future directions

Authors

  • Otabek Oribjonov Fergana Medical Institute of Public Health
  • Khadisakhon Oribjonova Fergana Medical Institute of Public Health

Keywords:

artificial intelligence, oncology, cancer, deep learning, radiology, pathology, genomics, precision medicine, clinical decision‑support

Abstract

Artificial intelligence (AI) has rapidly become a central driver of innovation in oncology, reshaping cancer screening, diagnosis, prognostication, and treatment personalization across the care continuum. Recent advances in deep learning, multimodal data integration, and large language models are enabling more accurate tumor detection, biomarker discovery, and clinical decision support than was achievable with conventional methods. In this review, we summarize key applications of AI in cancer imaging, pathology, genomics, and treatment planning, and contrast them with traditional non‑AI approaches in terms of performance, workflow impact, and current evidence. We discuss emerging roles of AI in clinical trial optimization and population‑level cancer control, while highlighting persistent challenges including data quality, bias, explainability, regulation, and implementation in real‑world settings. Finally, we outline future directions such as foundation models, agentic AI systems, and integration with precision and hyper‑personalized oncology, emphasizing the need for rigorous validation, multidisciplinary collaboration, and ethical frameworks to safely translate these technologies into routine practice.

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Published

2026-03-16

How to Cite

Oribjonov, O., & Oribjonova , K. (2026). Artificial intelligence in oncology: current landscape, challenges, and future directions. Journal of Clinical and Biomedical Research, 1(2), 95–104. Retrieved from https://medjournal.it.com/index.php/jcbr/article/view/104

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