ReviewBJR artificial intelligence2025
Large language models in cancer: potentials, risks, and safeguards.
Review in BJR artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Who Is Responsible When AI Gets Cancer Information Wrong? Implications for Patient Education.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- Can chatbots replace experts? Diagnostic accuracy of AI models in classifying impacted mandibular third molars.Odontology · 2026Article
- Large Language Models for Breast and Cervical Cancers Communication: Mixed Methods Evaluation Study Assessing Linguistic Quality, Safety, and Accessibility.JMIR cancer · 2026Article
- Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology.NPJ precision oncology · 2026Article
- Artificial intelligence integrated multi-omics and multimodal studies promote the efficacy of neoadjuvant chemotherapy in breast cancer: opportunities, challenges, and future perspectives.Breast cancer research : BCR · 2026Review
- Intelligent Support for Radiotherapy: A Review of Clinical Applications for Large Language Models.Journal of clinical medicine · 2026Review
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Leveraging large language models for heuristic usability assessment of medical software: Insights with the Radiation Planning Assistant.Journal of applied clinical medical physics · 2026Article
- LLM-powered TNM staging of neuroendocrine tumors from PET/CT reports.BMC medical imaging · 2025Article
- Large Language Models for Drug-Related Adverse Events in Oncology Pharmacy: Detection, Grading, and Actioning.Pharmacy (Basel, Switzerland) · 2025Review
- Temporal evolution of large language models (LLMs) in oncology.Journal of translational medicine · 2025Article
- Large Language Models in Population Oncology: A Contemporary Review on the Use of Large Language Models to Support Data Collection, Aggregation, and Analysis in Cancer Care and Research.JCO clinical cancer informatics · 2025Review
- Generative artificial intelligence, large language models and ChatGPT in musculoskeletal Oncology: Current applications and future potential.Journal of clinical orthopaedics and trauma · 2025Article
- Large Language Models for Adverse Drug Events: A Clinical Perspective.Journal of clinical medicine · 2025Review
- Recent advances in applications of artificial intelligence-assisted Raman spectroscopy in diagnosis of cancers.Frontiers in molecular biosciences · 2025Review
- Synthetic Lung-cancer Cohorts Generated by a Large Language Model: Epidemiological Validity Assessment.Open respiratory archivesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
This review examines the use of large language models (LLMs) in cancer, analysing articles sourced from PubMed, Embase, and Ovid Medline, published between 2017 and 2024. Our search strategy included terms related to LLMs, cancer research, risks, safeguards, and ethical issues, focusing on studies that utilized text-based data. 59 articles were included in the review, categorized into 3 segments: quantitative studies on LLMs, chatbot-focused studies, and qualitative discussions on LLMs on cancer. Quantitative studies highlight LLMs' advanced capabilities in natural language processing (NLP), while chatbot-focused articles demonstrate their potential in clinical support and data management. Qualitative research underscores the broader implications of LLMs, including the risks and ethical considerations. Our findings suggest that LLMs, notably ChatGPT, have potential in data analysis, patient interaction, and personalized treatment in cancer care. However, the review identifies critical risks, including data biases and ethical challenges. We emphasize the need for regulatory oversight, targeted model development, and continuous evaluation. In conclusion, integrating LLMs in cancer research offers promising prospects but necessitates a balanced approach focusing on accuracy, ethical integrity, and data privacy. This review underscores the need for further study, encouraging responsible exploration and application of artificial intelligence in oncology.
Indexed as
Identifiers
What Socratic 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 Socratic graph.