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Review
. 2025 Jan 20;13(1):24.
doi: 10.3390/diseases13010024.

Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions

David B Olawade  1   2   3 , Aanuoluwapo Clement David-Olawade  4 , Temitope Adereni  5 , Eghosasere Egbon  6 , Jennifer Teke  2   7   8 , Stergios Boussios  8   9   10   11   12
Affiliations
Review

Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions

David B Olawade et al. Diseases. .

Abstract

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy and radiation often result in significant side effects and varied patient outcomes. Immunotherapy has emerged as a promising alternative, harnessing the immune system to target cancer cells. However, the complexity of immune responses and tumor heterogeneity challenges its effectiveness.

Objective: This mini-narrative review explores the role of artificial intelligence [AI] in enhancing the efficacy of cancer immunotherapy, predicting patient responses, and discovering novel therapeutic targets.

Methods: A comprehensive review of the literature was conducted, focusing on studies published between 2010 and 2024 that examined the application of AI in cancer immunotherapy. Databases such as PubMed, Google Scholar, and Web of Science were utilized, and articles were selected based on relevance to the topic.

Results: AI has significantly contributed to identifying biomarkers that predict immunotherapy efficacy by analyzing genomic, transcriptomic, and proteomic data. It also optimizes combination therapies by predicting the most effective treatment protocols. AI-driven predictive models help assess patient response to immunotherapy, guiding clinical decision-making and minimizing side effects. Additionally, AI facilitates the discovery of novel therapeutic targets, such as neoantigens, enabling the development of personalized immunotherapies.

Conclusions: AI holds immense potential in transforming cancer immunotherapy. However, challenges related to data privacy, algorithm transparency, and clinical integration must be addressed. Overcoming these hurdles will likely make AI a central component of future cancer immunotherapy, offering more personalized and effective treatments.

Keywords: artificial intelligence; biomarkers; cancer immunotherapy; personalized medicine; predictive models.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
AI integration in cancer immunotherapy workflow.
Figure 2
Figure 2
PRISMA flowchart for selection of included studies.
Figure 3
Figure 3
Distribution of papers across key subsections of the review.
Figure 4
Figure 4
Distribution of machine- and deep-learning techniques across review areas.
Figure 5
Figure 5
AI-driven biomarker discovery workflow for cancer immunotherapy.
See this image and copyright information in PMC

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