ArticleJCO clinical cancer informatics2018
Prospects and challenges for clinical decision support in the era of big data.
Article in JCO clinical cancer informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
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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
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Why aren't they used? Systematic review of barriers to implementation of clinical decision support systems for early cancer detection in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Pooled it
- Global updates in the treatment of gastric cancer: a systematic review. Part 2: perioperative management, multimodal therapies, new technologies, standardization of the surgical treatment and educational aspects.Updates in surgery · 2020Pooled it
- Evaluation of model performance in predicting sepsis after intestinal obstruction surgery: a multicenter retrospective study.Annals of medicine · 2025Article
- Prediction of gestational diabetes mellitus at the first trimester: machine-learning algorithms.Archives of gynecology and obstetrics · 2024Article
- Personalized Medicine Transformed: ChatGPT's Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units.Journal of personalized medicine · 2024Article
- Translation of AI into oncology clinical practice.Oncogene · 2023Review
- The Critical Role of Pharmacists in the Clinical Delivery of Pharmacogenetics in the U.S.Pharmacy (Basel, Switzerland) · 2023Review
- A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS).Scientific reports · 2023Article
- Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment.Frontiers in oncology · 2023Article
- Precision radiotherapy via information integration of expert human knowledge and AI recommendation to optimize clinical decision making.Computer methods and programs in biomedicine · 2022Article
- Impact of a Machine Learning-Based Decision Support System for Urinary Tract Infections: Prospective Observational Study in 36 Primary Care Practices.JMIR medical informatics · 2022Article
- Review
- Artificial intelligence in oncology: Path to implementation.Cancer medicine · 2021Review
- Requirements and reliability of AI in the medical context.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling.BJR open · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called Big Data (BD); an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data; patient privacy; transformation of current analytical approaches to handle such noisy and heterogeneous data; and expanded use of advanced statistical learning methods based on confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical endpoints, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the utilization and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.
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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.