Generative AI and Its Applications in Cancer Diagnosis: A Systematic Review

DOI

10.2991/978-94-6239-756-9_23How to use a DOI?

Keywords

Cancer detection; Diagnostic accuracy; Generative AI; Generative adversarial networks

Abstract

Generative Artificial Intelligence (GAI) is reshaping cancer diagnostics by enabling reliable analysis of complex medical imaging, which improve timely detection and treatment planning. The comprehensive review aims to evaluate the present applications of generative AI techniques, such as generative adversarial networks (GANs) and related variation auto encoders, in oncology, emphasizing on their ability to improve diagnostic accuracy and efficiency. These models excel in analyzing intricate 2D imaging data (e.g., X-rays, pathology slides) and computationally intensive 3D volumetric data from CT and MRI scans, significantly enhancing sensitivity and specificity in cancer detection and decision support. In contrast, the substantial computational demands associated with training and deploying these models introduces challenges, requiring robust infrastructure, including GPU-accelerated servers, high-speed NVMe storage, and reliable networking for real-time diagnostic inference. Hybrid architectures combining on premise systems for latency-sensitive tasks with cloud platforms for large-scale training are increasingly vital. Secure data management—through encryption, audit logging, and compliance with healthcare regulations—remains essential to protect patient privacy. This review synthesizes evidence on generative AI’s role in advancing cancer diagnostics, highlighting its potential to reduce diagnostic errors and optimize clinical workflows. Future scope includes developing more efficient generative models, integrating multimodal data for comprehensive diagnostics, and addressing ethical considerations to ensure equitable access and deployment. By aligning scalable, secure infrastructure with these advancements, generative AI can drive faster, more accurate diagnoses and sustainable integration into clinical practice.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Sana Bagban
AU  - Mangal Singh
PY  - 2026
DA  - 2026/08/31
TI  - Generative AI and Its Applications in Cancer Diagnosis: A Systematic Review
BT  - Proceedings of the Conference on Bioengineering for Global Health (SYMRESEARCH 2.0 2025)
PB  - Atlantis Press
SP  - 327
EP  - 341
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6239-756-9_23
DO  - 10.2991/978-94-6239-756-9_23
ID  - Bagban2026
ER  -