<p>Characteristics of included studies.</p>

Sixteen studies form the basis of a scoping review investigating healthcare professionals' perceptions of Explainable AI (XAI) in Clinical Decision Support (CDS) systems within hospital settings. The dataset, authored by Bethany A. Van Dort and shared under a CC-BY-4.0 license, was last updated in May 2026. It likely contains extracted study characteristics and qualitative findings on acceptance drivers, explainability needs, and design preferences.

Use Cases

• Analyzing trends in XAI techniques for clinical decision support based on the mention of feature importance and SHAP methods.
• Investigating design preferences for XAI explanations in clinical settings based on described needs for concise information and simple plots.
• Studying factors influencing acceptance of AI systems in healthcare based on described drivers like workflow integration and data quality.
• Identifying potential risks of AI in clinical workflows based on mentioned concerns about overreliance and reduced professional autonomy.

Strengths

• Includes 16 studies identified through systematic searches of MEDLINE, Embase, and Web of Science.
• Study quality was assessed using the CASP checklist, suggesting a structured review process.
• The dataset is openly available under a permissive CC-BY-4.0 license.

Limitations

• The dataset is very small at 17.5 KB, indicating limited scope.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Bethany A. Van Dort via figshare

Collection Method

Data extracted from a scoping review of academic literature.

Freshness

Last updated 2026-05-14 17:56:47; freshness should be verified.