Integrating artificial intelligence tools in health research.

Area of Science:

• Health Research
• Data Science
• Artificial Intelligence

Background:

• Health research utilizes unique workflows distinct from data science, which develops most AI tools.
• This divergence presents challenges for integrating AI-enabled tools into health research practices.

Purpose of the Study:

• To analyze the impact of workflow differences on AI tool adoption in health research.
• To provide actionable recommendations for effectively leveraging AI in health research.
• To guide the training of future health researchers for an AI-integrated research landscape.

Main Methods:

• Reflective analysis of discipline-specific workflows in health research and data science.
• Identification of challenges and opportunities in AI tool integration.
• Development of practical advice and training recommendations.

Main Results:

• Workflow disparities can hinder the seamless application of AI tools in health research.
• Strategic integration and training are crucial for maximizing AI's potential in health studies.
• Proactive preparation of researchers is essential for adapting to AI-driven research.

Conclusions:

• Bridging the gap between health research workflows and AI development is key for successful tool implementation.
• Future health researchers require enhanced training in AI methodologies and applications.
• Optimizing AI integration will accelerate discovery and innovation in health research.