ReviewBiomolecules & biomedicine2026
Advanced immunotherapy across diseases and the role of artificial intelligence: A review.
Review in Biomolecules & biomedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunotherapy, a therapeutic strategy aimed at modulating the host immune system, has undergone rapid evolution over recent decades, particularly in oncology. Advanced methodologies, including immune checkpoint inhibition, cytokine therapy, chimeric antigen receptor T-cell therapy (CAR-T), and tumor-infiltrating lymphocyte therapies, have significantly transformed cancer treatment. This review summarizes recent advancements in immunotherapy and examines its expanding applications across a range of diseases, such as autoimmune disorders, infectious diseases, transplant rejection, and allergic conditions. A structured literature search was conducted using PubMed and Google Scholar, prioritizing studies published from 2015 to 2026. The findings underscore the efficacy of monoclonal antibodies, adoptive cell therapies, cytokine modulation, and checkpoint-targeted strategies beyond oncology. However, challenges remain, including variable patient responses, immune-related adverse events, and treatment costs. This review also explores the emerging role of artificial intelligence (AI) in enhancing personalized immunotherapy through patient stratification, biomarker identification, and predictive modeling. The integration of multi-omics data with AI presents promising opportunities for improving treatment efficacy and safety, although issues related to data quality, interpretability, regulatory frameworks, and ethical considerations must be addressed. In conclusion, immunotherapy is rapidly extending beyond cancer, and AI-supported personalized approaches offer a promising pathway to safer, more effective, and broadly applicable treatments.
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Registered trials
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 Socratic graph.