ReviewBlood cancer discovery2025
Advancing CAR T-cell Therapies with Artificial Intelligence: Opportunities and Challenges.
Review in Blood cancer discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Chimeric antigen receptor (CAR)-T cell therapy for solid tumors in pediatric patients: current breakthroughs, dilemmas, and strategies.Experimental hematology & oncology · 2026Review
- Review
- 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
- Early Immune Signature Features, Including TLR2 and TLR4 Expression, Are Associated with Complete Remission After CD19 CAR-T Cell Therapy.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Nanoparticles-enhanced CAR-T cell therapy: current advances and future directions.Biomarker research · 2026Review
- Advances and challenges of chimeric antigen receptor T cell therapy in digestive system malignancies.World journal of clinical oncology · 2026Review
- Advancing adoptive T cell therapy in ovarian cancer: barriers, innovations, and emerging platforms.Journal for immunotherapy of cancer · 2026Review
- Advancements and expanding applications of CAR-T cell therapy.Frontiers in immunology · 2026Review
- CAR-T cell signaling dynamics, rational design principles and artificial intelligence for next-generation chimeric antigen receptors.Frontiers in immunology · 2026Review
- Predictive markers for the efficacy of CAR T-cell therapy: the interplay between CAR T-cell fitness and systemic immunity.Blood advances · 2025Review
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
- Challenges and strategies in clinical applications of CAR-T therapy for autoimmune diseases.Journal of hematology & oncology · 2025Review
- Strategic innovations: Tackling challenges of immunotherapy in acute myeloid leukemia.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Overcoming Immune Barriers in Allogeneic CAR-NK Therapy: From Multiplex Gene Editing to AI-Driven Precision Design.Biomolecules · 2025Review
- Article
- Case Report: Combined umbilical cord blood and peripheral blood stem cell transplantation with donor lymphocyte infusion for R/R AML post CAR-CLL1 failure.Frontiers in immunology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Artificial intelligence could enhance chimeric antigen receptor T-cell therapy outcomes through optimization of all steps, from target identification, vector design, and manufacturing to personalized data-driven clinical decisions. In this report, we highlight steps toward unlocking this potential, including the need for standardized, comprehensive data repositories as a way for addressing barriers to artificial intelligence learning, such as data heterogeneity and patient privacy.
Indexed as
Identifiers
What OpenQuestion holds
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.