ArticleHealthcare (Basel, Switzerland)2025
Surveying the Digital Cytology Workflow in Italy: An Initial Report on AI Integration Across Key Professional Roles.
Article in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
2 citing papers in PubMed.
- Conditional Acceptance and the Optimism-Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy.Medical sciences (Basel, Switzerland) · 2026Article
- AI in Cervical Cancer Cytology Diagnostics: A Narrative Review of Cutting-Edge Studies.Bioengineering (Basel, Switzerland) · 2025Review
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
backgroundThe integration of artificial intelligence (AI) in healthcare, particularly in digital cytology, has the potential to enhance diagnostic accuracy and workflow efficiency. However, AI adoption remains limited due to technological and human-related barriers. Understanding the perceptions and experiences of healthcare professionals is essential for overcoming these challenges and facilitating effective AI implementation.
objectivesThis study aimed to assess AI integration in digital cytology workflows by evaluating professionals' perspectives on its benefits, challenges, and requirements for successful adoption.
methodsA survey was conducted among 150 professionals working in public and private healthcare settings in Italy, including laboratory technicians (35%), medical doctors (25%), biologists (20%), and specialists in diagnostic technical sciences (20%). Data were collected through a structured Computer-Assisted Web Interview (CAWI) and a Virtual Focus Group (VFG) to capture quantitative and qualitative insights on AI familiarity, perceived advantages, and barriers to adoption.
resultsThe findings indicated varying levels of AI familiarity among professionals. While many recognized AI's potential to improve diagnostic accuracy and streamline workflows, concerns were raised regarding resistance to change, implementation costs, and doubts about AI reliability. Participants emphasized the need for structured training and continuous support to facilitate AI adoption in digital cytology.
conclusionsAddressing barriers such as resistance, cost, and trust is essential for the successful integration of AI in digital cytology workflows. Tailored training programs and ongoing professional support can enhance AI adoption, ultimately optimizing diagnostic processes and improving clinical outcomes.
Indexed as
Identifiers
What OpenQuestion holds
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.