AI Can Be a Powerful Social Innovation for Public Health if Community Engagement Is at the Core

Area of Science:

• Public Health
• Health Informatics
• Artificial Intelligence

Background:

• Public health has been slow to adopt emerging technologies like generative AI, despite historical innovation in areas like vaccination.
• Increasingly complex requirements necessitate enhanced agility and responsiveness to health challenges.
• Community engagement is a cornerstone of current public health programs and research.

Purpose of the Study:

• To highlight the critical need for community engagement in adopting AI technologies in public health.
• To propose a multifaceted approach for safer and more effective AI integration in public health.
• To ensure AI adoption in public health is transparent, responsive, and equitable.

Main Methods:

• This viewpoint proposes a call to action for integrating AI in public health.
• Recommendations include AI training for public health professionals, community co-design of AI tools, and establishing governance mechanisms.
• Emphasis on participatory methods and community knowledge to address health disparities.

Main Results:

• Without community engagement, AI adoption risks excluding vulnerable populations and exacerbating health inequities.
• Involving communities helps mitigate risks related to privacy and bias in AI technologies.
• AI, when integrated equitably, can augment public health efforts to improve outcomes.

Conclusions:

• A proactive, community-centered approach is essential for the responsible adoption of AI in public health.
• Training, co-design, and governance are key to maximizing AI benefits while minimizing potential harms.
• Ensuring equity and transparency in AI use will enhance public health's capacity to address evolving challenges.