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Related Experiment Videos

Exploring Healthcare Providers' Expectations and Perceptions of AI Machine Learning Decision Tree Models in

Bas Luka Hendrik Laan1, Linda Peute1, Divya Srivastava2

  • 1Department of Medical Informatics, Amsterdam University Medical Centres.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

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Artificial Intelligence (AI) and Machine Learning (ML) models are viewed by healthcare professionals as tools to improve care quality. However, successful integration requires addressing concerns about time savings and ensuring proper training for effective adoption.

Area of Science:

  • Healthcare Management
  • Health Informatics
  • Artificial Intelligence in Medicine

Background:

  • Healthcare systems face increasing demands for efficiency and quality improvement.
  • The integration of Artificial Intelligence (AI) and Machine Learning (ML) presents opportunities and challenges in clinical settings.
  • Understanding stakeholder perceptions is crucial for successful technology adoption in healthcare.

Purpose of the Study:

  • To explore healthcare policymakers' and professionals' perceptions of AI/ML Decision Tree Models.
  • To identify potential impacts of these models on clinical work processes.
  • To understand facilitators and barriers to the adoption of AI/ML in healthcare.

Main Methods:

  • Qualitative study employing semi-structured interviews.
Keywords:
Health PersonnelMachine LearningPolicymakers

Related Experiment Videos

  • Participants included Dutch healthcare policymakers and professionals.
  • Thematic analysis was used to identify key themes and subthemes.

    • Main Results:

    • AI/ML models are perceived as supportive tools that can enhance care quality through task automation.
    • Concerns exist regarding significant time savings and potential increases in patient turnover.
    • Seamless integration with existing systems and comprehensive training are critical for adoption.

    Conclusions:

    • AI/ML Decision Tree Models offer potential benefits for healthcare delivery by supporting professionals and improving efficiency.
    • Addressing stakeholder skepticism and ensuring user-centric implementation are key to realizing the full potential of AI in clinical settings.
    • Further research should focus on practical implementation strategies and long-term impact assessments.