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Machine Learning

Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions

Ellen N. Huhulea(School of Medicine, New York Medical College, Valhalla, NY 10595, USA), Lillian Huang(School of Medicine, New York Medical College, Valhalla, NY 10595, USA), Shirley Eng(School of Medicine, New York Medical College, Valhalla, NY 10595, USA), Bushra Sumawi(Barshop Institute, The University of Texas Health Science Center, San Antonio, TX 78229, USA), Audrey Huang(School of Medicine, New York Medical College, Valhalla, NY 10595, USA), Esewi Aifuwa(School of Medicine, New York Medical College, Valhalla, NY 10595, USA), Rahim Hirani(School of Medicine, New York Medical College, Valhalla, NY 10595, USA), Raj K. Tiwari(School of Medicine, New York Medical College, Valhalla, NY 10595, USA), Mill Etienne(School of Medicine, New York Medical College, Valhalla, NY 10595, USA)
April 13, 2025Biomedicines52 citations
DOI

52

Citations

0

Influential Citations

Biomedicines

Venue

2025

Year

Abstract

Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI’s superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.

Analysis

Why This Paper Matters

This review provides a timely and broad overview of how artificial intelligence is transforming oncology, a field where cancer remains a leading cause of death worldwide. By covering early detection, personalized treatment, drug discovery, and the integration of AI with nanomedicine and immunotherapy, the paper serves as a valuable resource for AI practitioners and clinicians seeking to understand the current landscape and future opportunities. The inclusion of social determinants of health adds a critical dimension often overlooked in technical AI reviews, emphasizing that equitable cancer care requires more than algorithmic improvements.

Technical Contributions

The paper's key technical contributions include:

  • Comprehensive survey: Covers AI applications across the cancer care continuum, from computer-aided diagnosis to drug discovery.
  • Comparative analysis: Explicitly compares AI-based models with traditional diagnostic methods, highlighting AI's superior potential.
  • Integration with emerging therapies: Explores how AI can enhance nanomedicine and immunotherapy, two cutting-edge cancer treatment modalities.
  • Social determinants integration: Advocates for incorporating non-clinical factors into AI models to optimize care outcomes.

Results

The abstract does not provide specific quantitative metrics or benchmark results. Instead, it reports that AI models demonstrate "superior potential" compared to traditional diagnostic methods, and that AI-enhanced applications show promise in early detection and personalized treatment. The lack of concrete numbers (e.g., accuracy improvements, AUC scores) limits the ability to assess the magnitude of AI's advantage. The paper cites 52 references, suggesting a thorough literature survey, but no experimental results are presented.

Significance

This review is significant for AI practitioners as it maps the current state of AI in oncology and identifies key challenges—data quality, algorithmic biases, and clinical validation—that must be overcome for widespread adoption. By highlighting the integration of AI with nanomedicine and immunotherapy, it points to interdisciplinary opportunities that could accelerate cancer treatment innovation. The emphasis on social determinants of health is particularly important, as it reminds the AI community that technical performance alone is insufficient; equitable deployment requires addressing broader societal factors. This paper can guide future research directions and help prioritize areas where AI can have the greatest clinical impact.

deep-learningcomputer-aided-diagnosisdrug-discoverypersonalized-medicineimmunotherapynanomedicineoncology

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