ReviewFrontiers in oncology2019
AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale High Performance Computing.
Review in Frontiers in oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 41 citations in OpenAlex.
- Evolution of publicly available large language models for complex decision-making in breast cancer care.Archives of gynecology and obstetrics · 2024Article
- UNNT: A novel Utility for comparing Neural Net and Tree-based models.PLoS computational biology · 2024Article
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports.JCO clinical cancer informatics · 2024Article
- Awareness and intention-to-use of digital health applications, artificial intelligence and blockchain technology in breast cancer care.Frontiers in medicine · 2024Article
- Exploring the human microbiome - A step forward for precision medicine in breast cancer.Cancer reports (Hoboken, N.J.) · 2023Review
- Challenging ChatGPT 3.5 in Senology-An Assessment of Concordance with Breast Cancer Tumor Board Decision Making.Journal of personalized medicine · 2023Article
- The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances via Discovery in Physical Sciences.Medical physics · 2023Article
- Predictive Radiation Oncology - A New NCI-DOE Scientific Space and Community.Radiation research · 2022Article
- Exploring approaches for predictive cancer patient digital twins: Opportunities for collaboration and innovation.Frontiers in digital health · 2022Article
- Artificial intelligence in cancer research, diagnosis and therapy.Nature reviews. Cancer · 2021Article
- Global trends in the use of artificial intelligence for urological tumor histopathology: A 20-year bibliometric analysis.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build
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.