Citation

Rahman, Sayedur and Hosain, Md. Tanzib and Fahad, Nafiz and Morol, Md. Kishor and Hossen, Md. Jakir (2026) Agentic artificial intelligence is the future of cancer detection and diagnosis. Array, 29. p. 100676. ISSN 2590-0056

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Abstract

Agentic artificial intelligence systems, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), are a big change in oncology because they can find and diagnose cancer in ways that have never been done before. In accordance with PRISMA 2020 criteria, we conducted a systematic search across nine databases from January 2023 to September 2025, reviewing 3986 records and incorporating 123 papers that assessed agentic AI in cancer detection and diagnosis. Research demonstrated swift expansion (91.9% published in 2024-2025) across various cancer kinds, with breast (22.0%) and lung cancer (13.8%) being the most extensively examined. GPT-4 versions showed performance similar to that of human experts: they found errors better than pathologists (89.5% vs. 88.5%), classified skin lesions as well as dermatologists (84.8% vs. 84.6%), and staged ovarian cancer with 97% accuracy compared to 88% by radiologists. Zero-shot LLMs consistently surpassed conventional supervised models. But there were big problems, like factual errors in 15%–41% of instances, algorithmic bias, and low agreement with tumor boards (50%–70%). Agentic AI has a lot of promise for finding cancer, especially in organized tasks. However, the research so far suggests that it should be used as an aid rather than an independent system. Concerns about reliability and bias in algorithms are two of the most important impediments. Future priorities encompass Retrieval-Augmented Generation(RAG) systems, domain-specific models, and forthcoming trials to ascertain clinical value.

Item Type:	Article
Uncontrolled Keywords:	Large Language Model, Agentic AI, Cancer, RAG, VLM
Subjects:	R Medicine > R Medicine (General) > R855-855.5 Medical technology
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	09 Feb 2026 07:13
Last Modified:	09 Feb 2026 07:13
URII:	http://shdl.mmu.edu.my/id/eprint/15243

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