Challenges of reproducible AI in biomedical data science.

Area of Science:

• Biomedical Data Science
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is rapidly transforming biomedical data science.
• The reproducibility of AI models in this field is a critical, yet unclear, issue.

Discussion:

• This study investigates AI reproducibility challenges stemming from data, model, and learning complexities.
• A game-theoretical perspective is employed to analyze these challenges.
• The conflict between adhering to reproducibility standards and researchers' personal goals hinders progress.

Key Insights:

• Reproducibility is crucial for the advancement of AI in biomedical data science.
• Complexities in data, models, and learning processes impede AI reproducibility.
• Personal research goals can conflict with standardization efforts.

Outlook:

• Addressing the conflict between standards and personal goals is key to enhancing AI reproducibility.
• Future work should focus on developing strategies to foster both reproducibility and research innovation.
• Establishing clear guidelines and incentives for reproducible AI practices is essential.