AI-driven multimodal colorimetric analytics for biomedical and behavioral health diagnostics

Area of Science:

• Biomedical data analytics
• Computational biology
• Health informatics

Background:

• Exponential growth in multi-scale biomedical and behavioral data presents challenges for analysis.
• Effective management requires advanced computational techniques for accurate interpretation and decision-making.
• Integrating diverse data sources is crucial for advancing healthcare.

Purpose of the Study:

• To explore mobile-based colorimetry and multimodal data fusion for scalable, low-cost healthcare tools.
• To develop predictive models for real-time health monitoring and personalized diagnostics.
• To review advancements in image-driven colorimetric analysis and data fusion for healthcare.

Main Methods:

• Utilizing mobile-based colorimetry with image processing for detecting colorimetric changes.
• Developing a conceptual framework integrating colorimetry with clinical, imaging, and environmental data.
• Reviewing innovations in AI/ML models, biosensors, and biomedical imaging.

Main Results:

• Proposed framework integrates mobile colorimetry with multimodal data for predictive modeling.
• Highlights advancements in image-enabled colorimetric analysis and multimodal fusion techniques.
• Emphasizes innovations in AI/ML for data management, security, and reliability.

Conclusions:

• Mobile-based colorimetry and multimodal data fusion offer scalable solutions for diagnostics.
• The approach can improve diagnostic accuracy, enable early disease detection, and support personalized medicine.
• Addresses the need for robust data systems and interpretable AI/ML models for reliable healthcare applications.