ReviewCancers2020
Clinical Decision Support Systems in Breast Cancer: A Systematic Review.
Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis 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
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decision-support tools via mobile devices to improve quality of care in primary healthcare settings.The Cochrane database of systematic reviews · 2021Pooled it
- Trial
- The GUIDE-CDSS framework: a comprehensive framework to GUIDE and evaluate the implementation of Clinical Decision Support Systems in healthcare, based on an overview of reviews.Journal of the American Medical Informatics Association : JAMIA · 2026Review
- Development and evaluation of AI-based clinical decision support system for streamlining breast multidisciplinary team meetings.BMJ health & care informatics · 2026Article
- Comparative Evaluation of Machine Learning and Specialist Physicians in Breast Care Triaging: A Real-World Observational Study.European journal of breast health · 2025Article
- Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis.JMIR cancer · 2025Article
- AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis.Discover oncology · 2025Article
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- A Review on Integrating Breast Cancer Clinical Data: A Unified Platform Perspective.Current treatment options in oncology · 2025Review
- Clinical decision making in prostate cancer care-evaluation of EAU-guidelines use and novel decision support software.Scientific reports · 2024Article
- Workflow analysis of breast cancer treatment decision-making: challenges and opportunities for informatics to support patient-centered cancer care.JAMIA open · 2024Article
- Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.Journal of clinical medicine · 2024Article
- Long non-coding RNA LOXL1-AS1: a potential biomarker and therapeutic target in human malignant tumors.Clinical and experimental medicine · 2024Review
- Implementing and Evaluating a National Integrated Digital Registry and Clinical Decision Support System in Early Intervention in Psychosis Services (Early Psychosis Informatics Into Care): Co-Designed Protocol.JMIR research protocols · 2024Article
- Article
- Article
- Consistency of CSCO AI with Multidisciplinary Clinical Decision-Making Teams in Breast Cancer: A Retrospective Study.Breast cancer (Dove Medical Press) · 2024Article
- Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.PloS one · 2024Article
- Clinical Decision Support System to Detect the Occurrence of Ventilator-Associated Pneumonia in Pediatric Intensive Care.Diagnostics (Basel, Switzerland) · 2023Article
- Ten Topics to Get Started in Medical Informatics Research.Journal of medical Internet research · 2023Article
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.
Funding
Abstract
Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.
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.