De-identified Psychometric Validation Dataset for the Artificial Intelligence in Clinical

De-identified survey and psychometric validation data for the Artificial Intelligence in Clinical Practice, Education, and Research (AICPER) Scale among U.S. healthcare trainees. The dataset includes participant demographics and item-level responses assessing perceptions of artificial intelligence. Oluwaseun Adeyemi published the 14.0 KB CSV file on figshare under a CC-BY-4.0 license, last updated on 2026-05-17.

Use Cases

• Validate the psychometric properties of the AICPER scale based on survey responses.
• Analyze correlations between demographic factors and perceptions of AI based on participant data.
• Model attitudes towards AI in clinical practice, education, and research based on item-level responses.
• Benchmark AI acceptance levels among different cohorts of U.S. healthcare trainees.

Strengths

• Dataset is de-identified, which may support privacy compliance for research.
• Includes both demographic data and item-level survey responses for psychometric analysis.
• Published under a permissive CC-BY-4.0 license, facilitating reuse.

Limitations

• Row count is unknown, which may limit suitability assessment for statistical power.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic bias inherent to its collection from U.S. healthcare trainees.

Provenance

Source

Oluwaseun Adeyemi via figshare

Collection Method

Survey data collection and psychometric validation.

Freshness

Last updated 2026-05-17 01:15:51; freshness should be verified.

Geography

United States (U.S. healthcare trainees)