ReviewCancer control : journal of the Moffitt Cancer Center
Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence.
Review in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Artificial intelligence and transforming cancer care.Discover oncology · 2026Review
- Toward adaptive therapeutic timing: integration of mechanistic pharmacology and artificial intelligence in precision dosing.Frontiers in pharmacology · 2026Review
- Review
- Drug repurposing in cancer research: a bibliometric analysis.Discover oncology · 2025Article
- Pharmacometrics in the Age of Large Language Models: A Vision of the Future.Pharmaceutics · 2025Article
- Adaptive Treatment of Metastatic Prostate Cancer Using Generative Artificial Intelligence.Clinical Medicine Insights. Oncology · 2025Review
- Review
- Generative AI - Assisted Adaptive Cancer Therapy.Cancer control : journal of the Moffitt Cancer CenterReview
- Defeating lethal cancer: Interrupting the ecologic and evolutionary basis of death from malignancy.CA: a cancer journal for cliniciansReview
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
Therapeutic resistance is a major challenge facing the design of effective cancer treatments. Adaptive cancer therapy is in principle the most viable approach to manage cancer's adaptive dynamics through drug combinations with dose timing and modulation. However, there are numerous open issues facing the clinical success of adaptive therapy. Chief among these issues is the feasibility of real-time predictions of treatment response which represent a bedrock requirement of adaptive therapy. Generative artificial intelligence has the potential to learn prediction models of treatment response from clinical, molecular, and radiomics data about patients and their treatments. The article explores this potential through a proposed integration model of Generative Pre-Trained Transformers (GPTs) in a closed loop with adaptive treatments to predict the trajectories of disease progression. The conceptual model and the challenges facing its realization are discussed in the broader context of artificial intelligence integration in oncology.
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.