ReviewTherapeutic advances in vaccines and immunotherapy2024
Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives.
Review in Therapeutic advances in vaccines and immunotherapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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
28 citing papers in PubMed.
- Advanced gene editing technologies for oncology mechanisms, applications, and clinical implementation.Cancer gene therapy · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- A Comprehensive Evaluation of CAR-T Cell Gene Therapy, Tracing its Revolutionary Clinical Breakthroughs and Advancements Towards Next-Generation Engineering.Expert reviews in molecular medicine · 2026Review
- Chimeric Antigen Receptor T‑Cell Therapy Targeting Tumor Necrosis Factor Receptor Superfamily Member 8 (CD30) for Relapsed or Refractory Anaplastic Large Cell Lymphoma: Rationale, Strategies, and Emerging Clinical Evidence.ACS pharmacology & translational science · 2026Review
- Review
- In vivo CAR-cell therapy: current challenges and emerging therapeutic advances.Molecular biomedicine · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Beyond CRISPR: next-gen precision engineering of CAR-NK cells for enhanced persistence, trafficking, and tumor eradication.Cancer cell international · 2026Review
- Algorithm guided personalized T cell therapy: machine learning unlocks next generation TCR engineered immunotherapy.Pharmacological reports : PR · 2026Review
- Engineering Immunity: Current Progress and Future Directions of CAR-T Cell Therapy.International journal of molecular sciences · 2026Review
- Engineering controllable CAR T-cell therapies: from binary safety switches to programmable immunity.Frontiers in immunology · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 2026Review
- A proposal of good manufacturing practices (GMP) laboratory for CAR T-cell therapy in Latin America and the Caribbean: challenges and opportunities.Frontiers in public health · 2026Review
- Dual-module aCAR-iCAR NK cells for solid tumors: cascade resistance mechanisms, AI-driven engineering, and precision stratification.Frontiers in immunology · 2026Review
- Immune effector cell-associated neurotoxicity syndrome following CAR T-cell therapy: a review of recent advances.Journal of translational medicine · 2025Review
- CAR-NK Engineering to Overcome TME Barriers.Cells · 2025Review
- Challenges and limitations of chimeric antigen receptor T-cell therapies in solid tumors: why are approvals restricted to hematologic malignancies?Journal of hematology & oncology · 2025Review
- Engineering CAR-T cells for solid tumors: bispecific antigen targeting, tumor microenvironment modulation, and toxicity control.Immunologic research · 2025Review
- Advancing CAR T-Cell Therapy in Solid Tumors: Current Landscape and Future Directions.Cancers · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies' design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions for future research and development. This paper examines some of the recent advances of AI for CAR-based therapies, for example, using deep learning (DL) to design CARs that target multiple antigens and avoid antigen escape; using natural language processing to extract relevant information from clinical reports and literature; using computer vision to analyze the morphology and phenotype of CAR cells; using reinforcement learning to optimize the dose and schedule of CAR infusion; and using AI to predict the efficacy and toxicity of CAR-based therapies. These applications demonstrate the potential of AI to improve the quality and efficiency of CAR-based therapies and to provide personalized and precise treatments for cancer patients. However, there are also some challenges and limitations of using AI for CAR-based therapies, for example, the lack of high-quality and standardized data; the need for validation and verification of AI models; the risk of bias and error in AI outputs; the ethical, legal, and social issues of using AI for health care; and the possible impact of AI on the human role and responsibility in cancer immunotherapy. It is important to establish a multidisciplinary collaboration among researchers, clinicians, regulators, and patients to address these challenges and to ensure the safe and responsible use of AI for CAR-based therapies.
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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 OpenQuestion graph.