Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence...

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Public Health Policy

Background:

• Growing interest in applying artificial intelligence (AI) to health data.
• Need to understand public perspectives on AI in health research for improved patient care and efficiency.

Purpose of the Study:

• To explore general public views on using health data for AI research.
• To identify public hopes, fears, and conditions regarding health AI.

Main Methods:

• Qualitative study using six focus groups with 41 members of the public in Ontario, Canada.
• Thematic analysis of discussions on general AI and specific health AI scenarios.

Main Results:

• Public has limited AI knowledge but sees potential benefits in health AI.
• Conditional support for using health data if public benefit is evident and risks (privacy, commercial motives) are managed.
• Identified public hopes (accuracy, data volume) and fears (loss of human touch, skill depreciation).
• Mixed views on data consent, with a strong desire for transparency on data usage.

Conclusions:

• Despite concerns, the public conditionally supports health data use in AI research.
• Transparency and clear public benefit are key to gaining public trust in health AI.