Objective:

To explore the impact of AI-driven smart sensing and processing technologies on personalized healthcare, focusing on real-time monitoring, intelligent imaging, and multimodal assessments.

Approach:

• AI and Edge Computing: Studies demonstrate AI models operating efficiently on edge devices for real-time monitoring and classification, enhancing privacy and reducing latency.
• Remote Rehabilitation Assessment: Deep learning applied to consumer-grade webcams enables accessible movement analysis for stroke rehabilitation, lowering barriers to effective assessment.
• Intelligent Medical Imaging: AI frameworks enhance polyp detection in colonoscopy, addressing challenges in clinical deployment through uncertainty-aware modeling.
• Behavioral Diagnosis: Eye-tracking technology combined with deep learning aids in diagnosing autism spectrum disorder, providing objective screening tools.
• Healthcare Context Review: Reviews highlight AI's role in clinical decision-making and public health management, emphasizing the integration of AI in healthcare.
• Digital Health Literacy: Emphasizes the importance of user understanding and equitable access for effective AI adoption in healthcare settings.
• Research Trends Analysis: Visualization of research hotspots in clinical decision support systems contextualizes AI advancements and identifies future opportunities.

Key Findings:

• Edge computing and resource-aware AI are central to scalable healthcare delivery.
• Multimodal sensing enhances personalized assessments.
• Uncertainty-aware modeling is essential for clinical deployment.
• Personalization spans individual and population levels.
• Technological progress must consider literacy, ethics, and equitable implementation.

Interpretation:

Interdisciplinary collaboration is crucial for ensuring AI innovations are clinically meaningful and socially inclusive.

Limitations:

• Technological sophistication must be matched by user understanding.
• Risk of underutilization or widening disparities without equitable access.

Conclusion:

The editorial highlights the transformative potential of AI-driven smart sensing and processing in advancing personalized healthcare.