A review on multimodal machine learning in medical diagnostics

Area of Science:

• Medical informatics
• Artificial intelligence in healthcare
• Machine learning for diagnostics

Background:

• Increasing volumes of medical and clinical data offer valuable diagnostic insights.
• Single-source data, like electrocardiography (ECG), can be unreliable due to noise and interference.
• Multimodal learning offers a solution by integrating diverse data sources.

Purpose of the Study:

• To review and summarize recent research on multimodal machine learning for disease detection.
• To identify emerging trends and future research directions in this field.

Main Methods:

• Extraction of features from multiple medical data types.
• Development of multimodal machine learning and deep learning models.
• Review of existing literature on multimodal diagnostic approaches.

Main Results:

• Multimodal learning enhances model robustness and diagnostic accuracy by leveraging diverse data.
• Integration of various data sources overcomes limitations of single-modality diagnostics.
• Significant research interest in developing accurate multimodal models for medical applications.

Conclusions:

• Multimodal learning is a promising approach for improving disease detection accuracy and reliability.
• Further research is needed to explore novel feature extraction and model development techniques.
• The integration of diverse medical data holds significant potential for advancing diagnostic capabilities.