Observational studyJCO clinical cancer informatics2020
Effect of an Artificial Intelligence Clinical Decision Support System on Treatment Decisions for Complex Breast Cancer.
Observational study in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 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
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Evolving and Improving the Sustainability of Molecular Tumor Boards: The Value and Challenges.Cancers · 2024Guideline
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Pooled it
- Artificial intelligence in breast cancer: applications and advancements.Cancer biology & medicine · 2026Review
- Can Small Open-Source Language Models With Retrieval-Augmented Generation Match GPT-4 Performance in Breast Cancer Clinical Decision Support?JCO clinical cancer informatics · 2026Article
- The effect of AI-assisted bedside echocardiography on inpatient care: a prospective trial.European heart journal. Digital health · 2026Article
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era.International journal of breast cancer · 2026Article
- Using Artificial Intelligence to Develop Clinical Decision Support Systems-The Evolving Road of Personalized Oncologic Therapy.Diagnostics (Basel, Switzerland) · 2025Article
- Supporting Meaningful Choices: A Decision Aid for Individuals Facing Existential Distress and Considering Psilocybin-Assisted Therapy.Healthcare (Basel, Switzerland) · 2025Article
- Article
- Evaluating AI-driven precision oncology for breast cancer in low- and middle-income countries: a review of machine learning performance, genomic data use, and clinical feasibility.Frontiers in digital health · 2025Review
- Evaluating the efficacy of large language models in cardio-oncology patient education: a comparative analysis of accuracy, readability, and prompt engineering strategies.Frontiers in artificial intelligence · 2025Article
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Facilitators and barriers to AI adoption in nursing practice: a qualitative study of registered nurses' perspectives.BMC nursing · 2024Article
- Benefits of Clinical Decision Support Systems for the Management of Noncommunicable Chronic Diseases: Targeted Literature Review.Interactive journal of medical research · 2024Review
- An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.Journal of medical Internet research · 2024Article
- Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.Journal of medical systems · 2024Review
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- Article
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 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines. PATIENTS AND
methodsA cross-sectional observational study was conducted involving 1,977 patients at high risk for recurrent or metastatic breast cancer from the Chinese Society of Clinical Oncology. Ten oncologists provided blinded treatment recommendations for an average of 198 patients before and after viewing therapeutic options offered by the CDSS. Univariable and bivariable analyses of treatment changes were performed, and multivariable logistic regressions were estimated to examine the effects of physician experience (years), patient age, and receptor subtype/TNM stage.
resultsTreatment decisions changed in 105 (5%) of 1,977 patients and were concentrated in those with hormone receptor (HR)-positive disease or stage IV disease in the first-line therapy setting (73% and 58%, respectively). Logistic regressions showed that decision changes were more likely in those with HR-positive cancer (odds ratio [OR], 1.58;
conclusionUse of an artificial intelligence-based CDSS had a significant impact on treatment decisions and NCCN guideline adherence in HR-positive breast cancers. Although cases of stage IV disease in the first-line therapy setting were also more likely to be changed, the effect was not statistically significant (
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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.