ReviewYearbook of medical informatics2019
Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey.
Review in Yearbook of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.Journal of healthcare engineering · 2023Pooled it
- Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Development and validation of an explainable machine learning prediction model for futile recanalization after mechanical thrombectomy in acute large vessel occlusion stroke.Journal of neurointerventional surgery · 2026Article
- The unintended negative consequences of artificial intelligence use for psychologists.Frontiers in psychology · 2026Article
- Intelligent data governance and quality control for chest Bi syndrome/coronary heart disease across the prevention-treatment-rehabilitation continuum: integrating a standardized framework, adversarially-optimized trigger engine, and domain-adaptive AI.Frontiers in medical technology · 2026Article
- Recent advances, comparative performance, and data requirements of artificial intelligence-assisted technologies for the diagnosis of gastric cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2026Review
- What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach.Bioengineering (Basel, Switzerland) · 2025Article
- The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.Human vaccines & immunotherapeutics · 2024Article
- The Co-Piloting Model for Using Artificial Intelligence Systems in Medicine: Implementing the Constrained-Disorder-Principle-Based Second-Generation System.Bioengineering (Basel, Switzerland) · 2024Review
- Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases.PLOS digital health · 2024Article
- Clinical Information Retrieval: A Literature Review.Journal of healthcare informatics research · 2024Article
- Clinical decision support systems (CDSS) in assistance to COVID-19 diagnosis: A scoping review on types and evaluation methods.Health science reports · 2024Article
- Development and application of Chinese medical ontology for diabetes mellitus.BMC medical informatics and decision making · 2024Article
- Augmented Reality Surgical Navigation System Integrated with Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- Feasibility and utility of Point-of-Care electronic clinical data capture in Uganda's healthcare system: a qualitative study.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Development and Testing of the Smart Healthcare Prototype System through COVID-19 Patient Innovation.Healthcare (Basel, Switzerland) · 2023Article
- (De)troubling transparency: artificial intelligence (AI) for clinical applications.Medical humanities · 2023Article
- Artificial Intelligence in Medicine and Dentistry.Acta stomatologica Croatica · 2023Review
- On the importance of interpretable machine learning predictions to inform clinical decision making in oncology.Frontiers in oncology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis survey analyses the latest literature contributions to clinical decision support systems (DSSs) on a two-year period (2017-2018), focusing on the approaches that adopt Artificial Intelligence (AI) techniques in a broad sense. The goal is to analyse the distribution of data-driven AI approaches with respect to "classical" knowledge-based ones, and to consider the issues raised and their possible solutions.
methodsWe included PubMed and Web of Science
resultsWe selected 75 papers, 49 of which describe approaches in the data-driven AI area, 20 present purely knowledge-based DSSs, and 6 adopt hybrid approaches relying on both formalized knowledge and data.
conclusionsRecent studies in the clinical DSS area demonstrate a prevalence of data-driven AI, which can be adopted autonomously in purely data-driven systems, or in cooperation with domain knowledge in hybrid systems. Such hybrid approaches, able to conjugate all available knowledge sources through proper knowledge integration steps, represent an interesting example of synergy between the two AI categories. This synergy can lead to the resolution of some existing issues, such as the need for transparency and explainability, nowadays recognized as central themes to be addressed by both AI and medical informatics research.
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.