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Artificial intelligence agents in cancer research and oncology

Nature Reviews Cancer volume 26, pages 256–269 (2026)Cite this article

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Abstract

Since 2022, artificial intelligence (AI) methods have progressed far beyond their established capabilities of data classification and prediction. Large language models (LLMs) can perform logical reasoning, enabling them to plan and orchestrate complex workflows. By using this planning ability and equipped with the ability to act upon their environment, LLMs can function as agents. Agents are (semi-)autonomous systems capable of sensing, learning and acting upon their environments. As such, they can interact with external knowledge or external software and can execute sequences of tasks with minimal or no human input. In cancer research and oncology, evidence for the capability of AI agents is rapidly emerging. From autonomously optimizing drug design and development to proposing therapeutic strategies for clinical cases, AI agents can handle complex, multistep problems that were not addressable by previous generations of AI systems. Despite rapid developments, many translational and clinical cancer researchers still lack clarity regarding the precise capabilities, limitations, and ethical or regulatory frameworks associated with AI agents. Here we provide a primer on AI agents for cancer researchers and oncologists. We illustrate how this technology is set apart from and goes beyond traditional AI systems. We discuss existing and emerging applications in cancer research and address real-world challenges from the perspective of academic, clinical and industrial research.

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Fig. 1: Types of artificial intelligence agent architectures.
Fig. 2: Artificial intelligence agents in cancer research.
Fig. 3: A multi-agent framework for oncological treatment decisions, which integrates diverse medical data.

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Acknowledgements

J.N.K. is supported by the German Cancer Aid (DECADE, 70115166), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; TRANSFORM LIVER, 031L0312A; TANGERINE, 01KT2302 through ERA-NET TRANSCAN; Come2Data, 16DKZ2044A; DEEP-HCC, 031L0315A), the German Academic Exchange Service (SECAI, 57616814), the European Union’s Horizon Europe and innovation programme (ODELIA, 101057091; GENIAL, 101096312), the European Research Council (ERC; NADIR, 101114631), the National Institutes of Health (EPICO, R01 CA263318) and the National Institute for Health and Care Research (NIHR, NIHR203331) Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Daniel Truhn

  2. Google DeepMind, Toronto, Ontario, Canada

    Shekoofeh Azizi

  3. Stanford University, Palo Alto, CA, USA

    James Zou

  4. La Fe Health Research Institute, Valencia, Spain

    Leonor Cerda-Alberich

  5. Harvard Medical School, Boston, MA, USA

    Faisal Mahmood

  6. Else Kroener Fresenius Center for Digital Health, Faculty of Medicine, TUD Dresden University of Technology, Dresden, Germany

    Jakob Nikolas Kather

  7. University Hospital Heidelberg, University of Heidelberg, Heidelberg, Germany

    Jakob Nikolas Kather

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Contributions

D.T., L.C.A., F.M. and J.N.K. researched data for the article. All authors contributed substantially to discussion of the content. D.T., S.A., J.Z., L.C.A. and J.N.K. wrote the article. All authors reviewed and/or edited the manuscript before submission.

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Correspondence to Jakob Nikolas Kather.

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Competing interests

J.N.K. declares consulting services for Bioptimus, France; Panakeia, UK; AstraZeneca, UK; and MultiplexDx, Slovakia. Furthermore, he holds shares in StratifAI, Germany; Synagen, Germany; and Ignition Lab, Germany; has received an institutional research grant by GSK and AstraZeneca; and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, Merck, MSD, BMS, Roche, Pfizer and Fresenius. D.T. received honoraria for lectures by Bayer, GE, Roche, AstraZeneca and Philips and holds shares in StratifAI GmbH, Germany, and in Synagen GmbH, Germany. F.M. is a scientific adviser for and holds shares in Modella AI and is an adviser for Danaher. S.A. is an employee of Alphabet and may own stock as part of the standard compensation package. J.Z. and L.C.A. declare no competing interests.

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Nature Reviews Cancer thanks Anant Madabhushi, Wayne Zhao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Chain-of-thought reasoning

A prompting technique that encourages language models to generate intermediate reasoning steps before arriving at a final answer, improving performance on complex tasks.

Contraindications

Clinical conditions or factors that make a particular treatment or procedure inadvisable because of potential harm to the patient.

Deep learning

A subset of machine learning that uses artificial neural networks with multiple layers to learn hierarchical representations of data.

Differential diagnoses

A systematic process of distinguishing between diseases or conditions that share similar clinical features to identify the most likely diagnosis.

Edge case

An unusual or extreme scenario that occurs at the boundaries of normal operating conditions, often revealing limitations in system performance.

Hyperparameters

Configuration settings defined before model training that control the learning process, such as learning rate, batch size and network architecture choices.

Large language model

(LLM). Type of artificial intelligence model trained on vast amounts of text data to understand and generate human language, capable of performing diverse language tasks without task-specific training.

Multi-turn conversation

A dialogue consisting of multiple exchanges between a user and an AI system, in which context from previous turns informs subsequent responses.

Natural language processing

(NLP). A field of AI artificial intelligence focused on enabling computers to understand, interpret and generate human language.

Parsing documents

The computational process of analysing and extracting structured information from unstructured or semi-structured text documents.

Precompiled reports

Standardized documents generated in advance or from templates, typically containing structured clinical or research data ready for review.

Reinforcement learning

A machine learning paradigm in which an agent learns to make decisions by receiving feedback in the form of rewards or penalties based on its actions.

Token

The basic unit of text processed by a language model, which may represent a word, subword or character depending on the tokenization scheme.

Transformer architecture

A neural network design that uses self-attention mechanisms to process sequential data in parallel, forming the foundation of modern LLMs.

Vision language model

An AI model capable of processing and relating both visual information (such as images) and textual data within a unified framework.

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Truhn, D., Azizi, S., Zou, J. et al. Artificial intelligence agents in cancer research and oncology. Nat Rev Cancer 26, 256–269 (2026). https://doi.org/10.1038/s41568-025-00900-0

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