Strategies for mitigating data heterogeneities in AI-based neuro-disease detection

Area of Science:

• Medical Artificial Intelligence
• Clinical Data Science
• Computational Medicine

Background:

• Disease-detection AI models are increasingly trained on heterogeneous clinical data.
• Diverse data sources present unique challenges for AI model development and validation.
• Understanding these challenges is crucial for reliable AI deployment in healthcare.

Purpose of the Study:

• To discuss the challenges and best practices for AI models trained on heterogeneous clinical data.
• To focus on the interrelated problems of model bias, causality, and rare diseases in AI.
• To provide guidance for developing robust and equitable disease-detection AI.

Main Methods:

• Review and synthesis of current literature on AI in healthcare.
• Analysis of common issues arising from heterogeneous clinical datasets.
• Discussion of theoretical frameworks for bias, causality, and rare disease modeling.

Main Results:

• Heterogeneous data can introduce significant model bias, affecting generalizability.
• Establishing causality in AI models trained on observational clinical data is complex.
• Rare diseases pose unique difficulties for AI model training and validation due to data scarcity.

Conclusions:

• Addressing model bias, causality, and rare diseases is essential for trustworthy AI.
• Best practices involve careful data curation, bias mitigation strategies, and causal inference methods.
• Future research should focus on developing AI that is robust, fair, and effective across diverse populations and conditions.