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Challenges and Future Trends of Smart Healthcare Based on AI-Empowered IoT

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 20 October 2025 | Viewed by 1466

Special Issue Editors

Department of Information Technology, Kennesaw State University, Marietta, GA 30060, USA
Interests: distributed optimization; method of multipliers
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Information Technology, Kennesaw State University, Marietta, GA 30060, USA
Interests: IoT for smart healthcare; distributed computing; signal processing; wireless sensor networks; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart healthcare, driven by Internet of Things (IoT) technologies and empowered by artificial intelligence (AI), offers significant potential for reshaping healthcare delivery and management. This Special Issue aims to gather original research and review articles addressing the challenges and future trends in leveraging AI-empowered IoT for enhancing healthcare systems. With the integration of IoT devices, sensors, data analytics, and AI capabilities, smart healthcare offers opportunities for personalized patient monitoring, efficient resource allocation, remote diagnostics, and proactive intervention. Topics of interest include but are not limited to the following:

  • IoT-enabled medical devices and wearables;
  • Remote patient monitoring systems;
  • Data analytics for healthcare decision making;
  • Security and privacy in IoT-enabled healthcare systems;
  • Telemedicine and telehealth solutions;
  • Health data interoperability and integration;
  • AI and machine learning applications in smart healthcare;
  • IoT-based medication adherence and management;
  • Smart healthcare infrastructure and architecture;
  • Reviews on challenges in IoT-enabled healthcare systems;
  • Reviews on future trends and advancements in smart healthcare based on IoT.

Dr. Liang Zhao
Dr. Maria Valero
Guest Editors

Manuscript Submission Information

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Keywords

  • smart healthcare
  • Internet of Things (IoT)
  • remote patient monitoring
  • data analytics
  • telemedicine
  • AI in healthcare
  • healthcare IoT security

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Published Papers (1 paper)

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Review

22 pages, 558 KB  
Review
Smart Healthcare at Home: A Review of AI-Enabled Wearables and Diagnostics Through the Lens of the Pi-CON Methodology
by Steffen Baumann, Richard T. Stone and Esraa Abdelall
Sensors 2025, 25(19), 6067; https://doi.org/10.3390/s25196067 - 2 Oct 2025
Viewed by 684
Abstract
The rapid growth of AI-enabled medical wearables and home-based diagnostic devices has opened new pathways for preventive care, chronic disease management and user-driven health insights. Despite significant technological progress, many solutions face adoption hurdles, often due to usability challenges, episodic measurements and poor [...] Read more.
The rapid growth of AI-enabled medical wearables and home-based diagnostic devices has opened new pathways for preventive care, chronic disease management and user-driven health insights. Despite significant technological progress, many solutions face adoption hurdles, often due to usability challenges, episodic measurements and poor alignment with daily life. This review surveys the current landscape of at-home healthcare technologies, including wearable vital sign monitors, digital diagnostics and body composition assessment tools. We synthesize insights from the existing literature for this narrative review, highlighting strengths and limitations in sensing accuracy, user experience and integration into daily health routines. Special attention is given to the role of AI in enabling real-time insights, adaptive feedback and predictive monitoring across these devices. To examine persistent adoption challenges from a user-centered perspective, we reflect on the Pi-CON methodology, a conceptual framework previously introduced to stimulate discussion around passive, non-contact, and continuous data acquisition. While Pi-CON is highlighted as a representative methodology, recent external studies in multimodal sensing, RFID-based monitoring, and wearable–ambient integration confirm the broader feasibility of unobtrusive, passive, and continuous health monitoring in real-world environments. We conclude with strategic recommendations to guide the development of more accessible, intelligent and user-aligned smart healthcare solutions. Full article
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