ArticleOrphanet journal of rare diseases2020
Diagnosis of Rare Diseases: a scoping review of clinical decision support systems.
Article in Orphanet journal of rare diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].Schmerz (Berlin, Germany) · 2024Pooled it
- Diagnostic Performance of Large Language Models for Orthopedic-Related Rare Diseases and Their Impact on Physicians' Diagnostic Accuracy: 2-Stage Comparative Evaluation Study Based on the Chinese Rare Disease Catalog.Journal of medical Internet research · 2026Article
- Rare-Disease Diagnosis on the ZebraMap Multimodal Case Report Dataset: A Hybrid Pipeline with Grounded Explainability.Sensors (Basel, Switzerland) · 2026Article
- Diagnostic Accuracy of Large Language Models for Rare Diseases: A Systematic Review and Meta-Analysis.medRxiv : the preprint server for health sciences · 2026Article
- ZebraMap: A Multimodal Rare Disease Knowledge Map with Automated Data Aggregation & LLM-Enriched Information Extraction Pipeline.Diagnostics (Basel, Switzerland) · 2025Article
- Factors associated with diagnostic delays in Peruvian patients with rare diseases.Intractable & rare diseases research · 2025Article
- Co-creation process of an app for people with rare diseases - a citizen science approach.Orphanet journal of rare diseases · 2025Article
- Performance of Large Language Models in Diagnosing Rare Hematologic Diseases and the Impact of Their Diagnostic Outputs on Physicians: Combined Retrospective and Prospective Study.Journal of medical Internet research · 2025Article
- Barriers and Facilitators of the Use of Computerized Critical Care Information Systems in the Intensive Care Unit: Qualitative Interview Study.Journal of medical Internet research · 2025Article
- Evaluating the Prototype of a Clinical Decision Support System in Primary Care: Qualitative Study.JMIR formative research · 2025Article
- The impact of rare diseases on the quality of life in paediatric patients: current status.Frontiers in public health · 2025Review
- Unraveling the Genetic Heterogeneity of Acute Lymphoblastic Leukemia Based on NGS Applications.Cancers · 2024Review
- Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System.Journal of medical systems · 2024Article
- Diet and Nutrients in Rare Neurological Disorders: Biological, Biochemical, and Pathophysiological Evidence.Nutrients · 2024Review
- Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review.Journal of clinical medicine · 2024Review
- High-throughput assays to assess variant effects on disease.Disease models & mechanisms · 2024Article
- Objectivizing issues in the diagnosis of complex rare diseases: lessons learned from testing existing diagnosis support systems on ciliopathies.BMC medical informatics and decision making · 2024Article
- Synthetic protein protease sensor platform.Frontiers in bioengineering and biotechnology · 2024Article
- Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process.BMC medical informatics and decision making · 2023Article
- Clinical decision support systems to improve drug prescription and therapy optimisation in clinical practice: a scoping review.BMJ health & care informatics · 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
No grant is acknowledged in the PubMed record.
Abstract
backgroundRare Diseases (RDs), which are defined as diseases affecting no more than 5 out of 10,000 people, are often severe, chronic and life-threatening. A main problem is the delay in diagnosing RDs. Clinical decision support systems (CDSSs) for RDs are software systems to support clinicians in the diagnosis of patients with RDs. Due to their clinical importance, we conducted a scoping review to determine which CDSSs are available to support the diagnosis of RDs patients, whether the CDSSs are available to be used by clinicians and which functionalities and data are used to provide decision support.
methodsWe searched PubMed for CDSSs in RDs published between December 16, 2008 and December 16, 2018. Only English articles, original peer reviewed journals and conference papers describing a clinical prototype or a routine use of CDSSs were included. For data charting, we used the data items "Objective and background of the publication/project", "System or project name", "Functionality", "Type of clinical data", "Rare Diseases covered", "Development status", "System availability", "Data entry and integration", "Last software update" and "Clinical usage".
resultsThe search identified 636 articles. After title and abstracting screening, as well as assessing the eligibility criteria for full-text screening, 22 articles describing 19 different CDSSs were identified. Three types of CDSSs were classified: "Analysis or comparison of genetic and phenotypic data," "machine learning" and "information retrieval". Twelve of nineteen CDSSs use phenotypic and genetic data, followed by clinical data, literature databases and patient questionnaires. Fourteen of nineteen CDSSs are fully developed systems and therefore publicly available. Data can be entered or uploaded manually in six CDSSs, whereas for four CDSSs no information for data integration was available. Only seven CDSSs allow further ways of data integration. thirteen CDSS do not provide information about clinical usage.
conclusionsDifferent CDSS for various purposes are available, yet clinicians have to determine which is best for their patient. To allow a more precise usage, future research has to focus on CDSSs RDs data integration, clinical usage and updating clinical knowledge. It remains interesting which of the CDSSs will be used and maintained in the future.
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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.