Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications - A...

Area of Science:

• * Digital Health
• * Artificial Intelligence in Medicine
• * Clinical Decision Support

Background:

• * Generative artificial intelligence (GenAI) is revolutionizing eHealthcare, offering improvements in diagnostics and efficiency.
• * However, concerns exist regarding automation bias and potential deskilling of clinicians.
• * This impacts core diagnostic expertise and professional judgment.

Purpose of the Study:

• * To systematically review the dual impact of GenAI in eHealthcare.
• * To analyze how GenAI enhances clinical efficiency and decision support.
• * To investigate the potential diminishment of clinicians' diagnostic expertise and professional judgment.

Main Methods:

• * A systematic search was conducted across major scientific databases (PubMed, Scopus, Web of Science, Google Scholar).
• * 11,269 records were identified, with 29 studies meeting inclusion criteria per PRISMA 2020 guidelines.
• * Narrative synthesis was employed due to study heterogeneity.

Main Results:

• * Key themes included automation bias (10 studies) and clinician deskilling (9 studies).
• * GenAI demonstrated significant improvements in diagnostic accuracy, workflow efficiency, and decision quality across radiology, telehealth, and education.
• * Overreliance poses risks of automation bias, cognitive deskilling, and loss of interpretive autonomy.

Conclusions:

• * Mitigation strategies include human-in-the-loop frameworks, explainable AI (XAI), and ethical governance.
• * Responsible GenAI adoption necessitates balancing analytical capabilities with human judgment.
• * Redefining the clinician-AI relationship as a cognitive partnership is crucial for sustainable integration.