Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Diagnostic Medicine

Background:

• Medical diagnosis traditionally relies on single data sources, which can be limiting.
• A growing need exists for automated diagnostic tools due to a scarcity of medical experts.
• Multimodal data fusion offers a promising avenue for more precise and confident diagnoses.

Purpose of the Study:

• To provide a comprehensive review of multimodal medical diagnosis.
• To equip researchers and academicians with knowledge on multimodal data aspects and challenges.
• To discuss the need for multimodal information and potential solutions in automated diagnosis.

Main Methods:

• Systematic review of high-quality articles from reputable journals and publishers.
• Analysis of multimodal data fusion techniques in medical diagnosis.
• Examination of challenges and ethical considerations in AI-driven medical diagnosis.

Main Results:

• Combining information from multiple medical sources significantly boosts diagnostic confidence.
• Automated diagnosis using multimodal fusion techniques is increasingly feasible with AI advancements.
• Identified key challenges in multimodal data integration and proposed potential solutions.

Conclusions:

• Multimodal medical diagnosis is essential for improving diagnostic accuracy and patient care.
• Further research is needed to address the complexities and ethical implications of AI in diagnostics.
• This review serves as a foundational resource for advancing multimodal medical research.