ArticleNPJ digital medicine2025
Large language model integrations in cancer decision-making: a systematic review and meta-analysis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07760857 (Investigation of an Intelligent Centre-adaptive Multi-modal Fusion Framework), which is not on this map. Cited by 37 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.
Investigation of an Intelligent Centre-adaptive Multi-modal Fusion Framework (Cad- MMFF) to Overcome Unnecessary Prostate Biopsies and Optimize MRI Utilization: a Hybrid Retrospective-prospective Study
Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- The potential of large language models in the field of infertility: a systematic review.Journal of assisted reproduction and genetics · 2025Pooled it
- The daily dose: Early usability of an LLM tool for patient summaries and trial matching in radiation oncology.Clinical and translational radiation oncology · 2026Article
- Alignment between AI clinical decision tools and multidisciplinary tumor board decisions in prostate cancer.International urology and nephrology · 2026Article
- Performance of large language models in ambiguous treatment scenarios of early breast cancer.Breast (Edinburgh, Scotland) · 2026Article
- The Use of Artificial Intelligence Chatbots by Newly Diagnosed Cancer Patients: A Descriptive Phenomenological Study.Current oncology (Toronto, Ont.) · 2026Article
- Assessing Quality Gaps and Clinician Perspectives on AI Integration in Colorectal Cancer NGS Pathways.Current oncology (Toronto, Ont.) · 2026Article
- Augmenting patient safety surveillance in radiation oncology with large language model-based root cause analysis: A proof-of-concept study.PLOS digital health · 2026Article
- Article
- Precision oncology meets Generative AI: assessing large language models in multidisciplinary GIST tumor boards.BMC cancer · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Multiturn Large Language Model-Based Conversational Agents for Patients With Cancer and Caregivers: Scoping Review.JMIR cancer · 2026Article
- A multidimensional benchmarking framework for large language models in oncologic decision making.Scientific reports · 2026Article
- Applications of Large Language Models in Ovarian Cancer Management: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2026Article
- Research through Evaluation for Large Language Model in Patient-Clinician Communications.Research square · 2026Article
- Article
- Digital Registrar: A Schema-First Framework for Multi-Cancer Privacy-Preserving Pathology Abstraction via Local LLMs.Diagnostics (Basel, Switzerland) · 2026Article
- Sequencing AI Automation and Data Interoperability in Oncology Using a Scenario-Planning Framework Coupled With Discrete-Event Simulation: Proof-of-Concept Study.Journal of medical Internet research · 2026Article
- Review
- Performance of Large Language Models on Exam-style Questions and Case Challenges Across Varying Levels of Complexity.Journal of medical systems · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Large Language Models (LLMs) are increasingly used to support cancer patients and clinicians in decision-making. This systematic review investigates how LLMs are integrated into oncology and evaluated by researchers. We conducted a comprehensive search across PubMed, Web of Science, Scopus, and the ACM Digital Library through May 2024, identifying 56 studies covering 15 cancer types. The meta-analysis results suggested that LLMs were commonly used to summarize, translate, and communicate clinical information, but performance varied: the average overall accuracy was 76.2%, with average diagnostic accuracy lower at 67.4%, revealing gaps in the clinical readiness of this technology. Most evaluations relied heavily on quantitative datasets and automated methods without human graders, emphasizing "accuracy" and "appropriateness" while rarely addressing "safety", "harm", or "clarity". Current limitations for LLMs in cancer decision-making, such as limited domain knowledge and dependence on human oversight, demonstrate the need for open datasets and standardized evaluations to improve reliability.
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.