Deep learning in multimedia healthcare applications: a review

Area of Science:

• Artificial Intelligence in Medicine
• Digital Health
• Biomedical Informatics

Background:

• Rising chronic diseases and the COVID-19 pandemic strain global health systems and economies.
• There is a critical need for innovative, cost-effective technological solutions to meet public health demands.
• Advancements in computing power, big data, and deep learning research have spurred healthcare applications.

Purpose of the Study:

• To provide an overview of deep learning-based healthcare solutions utilizing multimedia data.
• To explore the application of deep learning in various healthcare contexts.
• To highlight challenges and future directions in this research area.

Main Methods:

• Review of existing literature on deep learning applications in healthcare.
• Explanation of different types of multimedia data (images, video, audio, text) used in deep learning.
• Identification and discussion of relevant deep learning multimedia applications in healthcare.

Main Results:

• Deep learning techniques are increasingly utilized in healthcare for diagnosis, prediction, and treatment.
• Multimedia data from sources like IoT devices and smartphones are valuable inputs for deep learning models.
• Various deep learning models have been proposed to support healthcare systems using diverse data types.

Conclusions:

• Deep learning combined with multimedia data presents a promising avenue for advancing healthcare.
• These technologies can enhance diagnostic accuracy, treatment efficacy, and patient care.
• Addressing challenges in data integration and model interpretability is crucial for future progress.