ReviewNPJ precision oncology2024
Large language models and multimodal foundation models for precision oncology.
Review in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
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
48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Natural language generation in healthcare: A review of methods and applications.Journal of biomedical informatics · 2026Pooled it
- Applying transformer-based deep learning models in image-driven cancer diagnosis: a comprehensive bibliometric analysis of global research trends.Frontiers in oncology · 2026Pooled it
- HistoEncoder: A digital pathology foundation model for prostate cancer.Journal of pathology informatics · 2026Article
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Beyond robotic platforms: artificial intelligence and the emergence of intelligent surgical ecosystems in colorectal surgery.Journal of robotic surgery · 2026Review
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Beyond chemotherapy: The rise of nucleic acid nanoformulations in personalized lung cancer therapy.Cancer pathogenesis and therapy · 2026Review
- From data to value in multimodal learning.Patterns (New York, N.Y.) · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- SCRIPT: Stratified clinical risk prediction from pathology reports using large language models.Journal of pathology informatics · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Towards an ecosystem of clinical decision support tools for precision cancer medicine.NPJ precision oncology · 2026Review
- Visual recognition limitations in multimodal large language models: A comparative analysis of histological image interpretation.PLOS digital health · 2026Article
- From BERT to GPT-4: A systematic review of AI-Driven toxicity extraction and grading in radiation oncology.Technical innovations & patient support in radiation oncology · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- AI-driven multi-omics integration in breast cancer: clinical applications, immunotherapy prediction, and translational challenges.Frontiers in molecular biosciences · 2026Review
- AI-driven multi-omics biomarkers in precision breast cancer therapeutics: from discovery to clinical translation.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The technological progress in artificial intelligence (AI) has massively accelerated since 2022, with far-reaching implications for oncology and cancer research. Large language models (LLMs) now perform at human-level competency in text processing. Notably, both text and image processing networks are increasingly based on transformer neural networks. This convergence enables the development of multimodal AI models that take diverse types of data as an input simultaneously, marking a qualitative shift from specialized niche models which were prevalent in the 2010s. This editorial summarizes these developments, which are expected to impact precision oncology in the coming years.
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.