Evidence map›Paper›PMID 39851488›Full record

ReviewDiseases (Basel, Switzerland)2025

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

David B Olawade, Aanuoluwapo Clement David-Olawade, Temitope Adereni, Eghosasere Egbon, Jennifer Teke, Stergios Boussios

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Diseases (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Superantigens in Cancer Immunotherapy: Mechanisms, Engineering Strategies, and Therapeutic Potential.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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  3. Immunotherapy for Cancer: Current Advances and Future Directions.International journal of molecular sciences · 2026
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  17. [Multidimensional characteristics of the tumor microenviron-ment and advances in targeted delivery strategies].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

David B OlawadeDepartment of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK.ORCID 0000-0003-0188-9836
Aanuoluwapo Clement David-OlawadeEndoscopy Unit, Glenfield Hospital, University Hospitals of Leicester NHS Trust, Leicester LE3 9QP, UK.ORCID 0000-0002-7052-6425
Temitope AdereniDepartment of Public Health, University of Dundee, Dundee DD1 4HN, UK.ORCID 0009-0002-3153-0859
Eghosasere EgbonDepartment of Tissue Engineering and Regenerative Medicine, Faculty of Life Science Engineering, FH Technikum, 1200 Vienna, Austria.
Jennifer TekeDepartment of Research and Innovation, Medway NHS Foundation Trust, Gillingham, Kent ME7 5NY, UK.
Stergios BoussiosFaculty of Medicine, Health and Social Care, Canterbury Christ Church University, Canterbury, Kent CT1 1QU, UK.ORCID 0000-0002-2512-6131

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCancer 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.

objectiveThis 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.

methodsA 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.

resultsAI 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.

conclusionsAI 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.

Indexed as

artificial intelligencebiomarkerscancer immunotherapypersonalized medicinepredictive models

Identifiers

PMID39851488
PMCPMC11764268

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.