AI and Algorithmic Literacy Among Health Workers: A Scoping Review

Twelve studies published between 2021 and 2025 were analyzed in this scoping review. The work, authored by Ihoghosa Iyamu and published on figshare in April 2026, examines how AI and algorithmic literacy are conceptualized and measured among health workers through a digital health literacy lens.

Use Cases

• Benchmarking existing AI literacy measurement tools like AILS, MAILS, and SNAIL based on their application in health education studies.
• Identifying research gaps in communicative AI literacy competencies for health workers as described in the review's findings.
• Informing the development of health-specific AI literacy frameworks based on the synthesis of functional, critical, and communicative dimensions.
• Analyzing the conceptual alignment between AI literacy and digital health literacy as discussed in the review's results.

Strengths

• The review is based on a systematic search of 7 major academic databases, including Ovid MEDLINE and IEEE Xplore.
• Findings are synthesized using a theory-informed framework grounded in Nutbeam's model of digital health literacy.
• The dataset is openly licensed under CC-BY-4.0, permitting reuse with attribution.

Limitations

• The dataset is a 17.4 KB document summarizing a review; it does not contain the primary data from the 12 analyzed studies.
• Row count is unknown, which may limit suitability assessment for quantitative analysis.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Collection Method

Scoping review conducted using Arksey and O'Malley's framework, with data extracted using a theory-informed charting framework.

Time Range

Studies published between January 2020 and May 2025 were searched; 12 studies from 2021-2025 were included.

Freshness

Last updated 2026-04-23 05:26:43; freshness should be verified.