{
  "abstract": "This study examines AI information literacy among healthcare professionals and identifies differences across professional roles. An online survey was administered to 390 healthcare professionals, including 22.3% medical doctors, 42.1% nurses, and 35.6% health professionals. AI information literacy was measured using the AI Information Literacy Scale (AILIS), which captures perceived confidence across four dimensions: process/create, assess, retrieve, and ethics. Findings indicated that across AILIS dimensions, participants reported the greatest confidence in retrieving information with AI and the least confidence in critically assessing AI-generated outputs. Medical doctors scored significantly higher than nurses and health professionals on process/create and retrieve. Across the main regression models, five predictors were included: age, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. Self-perceived understanding showed the strongest association with all four AI information literacy dimensions. The models explained between 27.3% and 48.3% of the variance. Age showed small negative associations in most models. Self-reported AI information literacy among healthcare professionals appeared uneven across dimensions, with relatively lower confidence in critical assessment. GenAI integration in healthcare should therefore be approached as a literacy and training challenge, with particular attention to evaluation skills and reflective understanding of AI outputs.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Education and Teaching, Tel-Hai University of Kiryat Shmona in the Galilee"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lilach Alon"
    },
    {
      "affs": [
        0
      ],
      "name": "Inbar Levkovich"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5xgctwpmfr0z1.bib",
  "cited_by": 1,
  "doi": "10.1186/s12909-026-10027-x",
  "fulltext_url": "https://doi.org/10.1186/s12909-026-10027-x",
  "id": "5xgctwpmfr0z1",
  "issns": [
    "1472-6920"
  ],
  "landing_url": "https://doi.org/10.1186/s12909-026-10027-x",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "LC8-6691"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7171394774",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/5xgctwpmfr0z1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:46:01Z",
      "native_id": "1181e667dc5347a199dc3a202df54b9d",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s12909-026-10027-x"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "AI information literacy in healthcare context: profiles and associated variables among healthcare professionals",
  "title_text": "AI information literacy in healthcare context: profiles and associated variables among healthcare professionals",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:20:46Z",
  "url": "https://research.draconitic.com/paper/5xgctwpmfr0z1",
  "venue": "BMC Medical Education",
  "versions": []
}