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Exploring stakeholder attitudes towards AI in clinical practice.

Ian A Scott1,2, Stacy M Carter3, Enrico Coiera4

  • 1Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia ian.scott@health.qld.gov.au.

BMJ Health & Care Informatics
|December 10, 2021
PubMed
Summary

Stakeholder attitudes towards artificial intelligence (AI) in healthcare are generally positive, especially with experience. However, concerns about privacy, liability, and clinician oversight necessitate tailored development and implementation strategies for AI acceptance.

Keywords:
artificial intelligencecomputer-assisteddecision makingmachine learningpatient-centered care

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Area of Science:

  • Healthcare Technology Assessment
  • Medical Informatics
  • Digital Health

Background:

  • Varying stakeholder attitudes towards artificial intelligence (AI) in healthcare can impede its adoption.
  • Understanding these diverse perspectives is crucial for successful AI integration.

Purpose of the Study:

  • To explore and synthesize evidence on the attitudes of various stakeholders (clinicians, consumers, managers, researchers, regulators, industry) towards AI applications in healthcare.

Main Methods:

  • Exploratory analysis of 27 studies published between January 2010 and May 2021.
  • Searched PubMed and Google Scholar for articles on artificial intelligence/AI in medical/healthcare settings, focusing on attitudes and non-robotic, clinician-facing applications.

Main Results:

  • General attitudes towards healthcare AI are positive, particularly with direct experience and established safeguards.
  • Clinicians and consumers favor AI for data interpretation over direct clinical decision influence.
  • Concerns include privacy, liability (clinicians), and loss of oversight/shared decision-making (consumers).

Conclusions:

  • Common expectations exist across stakeholder groups, highlighting key dependencies for AI development.
  • Addressing attitudinal differences through policy and process is vital for bridging gaps and ensuring AI implementation success.