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Future Internet
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Intelligent Sensor and Internet of Medical Things for Smart Healthcare
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Intelligent Sensor and Internet of Medical Things for Smart Healthcare

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Smart System Infrastructure and Applications".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 824

Editors

Dr. Manob Saikia

E-Mail Website
Guest Editor
1. Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA
2. Electrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA
Interests: bio-instrumentation and medical devices; wearable bioelectronics and IoT; neuro-engineering (fNIRS/EEG); causal AI and deep learning; multimodal sensor fusion; digital twins and predictive modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kunal Mankodiya

E-Mail Website
Guest Editor
Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
Interests: wearable sensors; smart textiles; body sensor networks; medical internet of things; medical cyber–physical systems; neural engineering; human–computer interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid advancement of Intelligent Sensors and the Internet of Medical Things (IoMT) is fundamentally transforming the landscape of Smart Healthcare, shifting the paradigm from reactive treatment to proactive, 24/7 digital health monitoring. This Special Issue explores the synergy between low-cost, user-friendly IoMT devices and sophisticated AI-driven algorithms to revolutionize disease prevention, early diagnosis, and the management of chronic conditions. By leveraging edge–cloud optimization and high-accuracy machine learning models, we can now monitor complex multimodal physiological and behavioral signals to provide clinicians with real-time data for informed, personalized clinical outcomes. Together, these Intelligent Sensors and the IoMT have the potential to provide continuous assessment of physical, mental, and behavioral health, enhancing overall well-being in daily life.

We invite original research and reviews that address current challenges in Intelligent Sensors and the Internet of Medical Things (IoMT) for Smart Healthcare, including biomedical signal processing, hardware miniaturization, flexible electronics, and the security/privacy of connected healthcare systems. We aim to provide a platform for scientists and engineers to present their latest theoretical and technological advancements in intelligence-enhanced health monitoring.

Dr. Manob Saikia
Prof. Dr. Kunal Mankodiya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent sensors
  • internet of medical things (IoMT)
  • smart healthcare
  • wearable biosensors and systems
  • digital health and wellness
  • artificial intelligence in healthcare
  • machine learning and deep learning
  • biomedical signal processing
  • edge computing and cloud–edge optimization
  • remote patient monitoring (RPM)
  • bioinstrumentation and hardware miniaturization
  • flexible, stretchable, and imperceptible bioelectronics
  • neural engineering and the brain–computer interface (BCI)
  • multimodal biosensing
  • human-centered systems
  • security, privacy, and trust in eHealth
  • sensors for physical rehabilitation
  • cognitive and behavioral health monitoring
  • stress, emotion, and fatigue detection
  • clinical decision-support systems
  • personalized medicine
  • point-of-care (PoC) devices
  • energy-efficient IoMT protocols
  • smart textiles and on-body systems

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Published Papers (2 papers)

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Research

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16 pages, 5619 KB  
Article
An Edge Artificial Intelligence Framework for IoMT-Enabled Remote Health Monitoring and Clinical Information Retrieval
by Pir Noman Ahmad, Muhammad Shahid Anwar, Igor Heberto Barahona, Atta Ur Rahman, Haseeb Nisar and Umama Burhan
Future Internet 2026, 18(6), 324; https://doi.org/10.3390/fi18060324 - 15 Jun 2026
Viewed by 431
Abstract
Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical [...] Read more.
Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article
(This article belongs to the Special Issue Intelligent Sensor and Internet of Medical Things for Smart Healthcare)
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Review

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36 pages, 4209 KB  
Review
Agentic AI and Multi-Agent Collaboration in Healthcare: A Comprehensive Survey of Architectures, Clinical Safety, and Future Directions
by Subir Biswas, Rajib Mondal and Manob Jyoti Saikia
Future Internet 2026, 18(8), 391; https://doi.org/10.3390/fi18080391 (registering DOI) - 25 Jul 2026
Abstract
In the healthcare and clinical domain, artificial intelligence (AI) is evolving from earlier models that primarily predicted outcomes or generated content toward agentic AI systems that demonstrate the capability to make decisions and complete tasks autonomously. Previous research on AI has contributed significantly [...] Read more.
In the healthcare and clinical domain, artificial intelligence (AI) is evolving from earlier models that primarily predicted outcomes or generated content toward agentic AI systems that demonstrate the capability to make decisions and complete tasks autonomously. Previous research on AI has contributed significantly to disease identification, deep learning applications, large language models (LLMs), and generative AI. These systems primarily function as assistive tools, as they generate text or predictions without directly interacting with clinical infrastructures. Therefore, recent research trends are increasingly oriented toward agentic AI systems that extend beyond traditional predictive and generative model performance. This manuscript provides a detailed review of the current state of agentic AI, starting with the evolution of AI and the concept of a medical agent. A medical agent refers to an intelligent AI system designed to assist in clinical or administrative tasks by analyzing data, supporting decision making, and interacting with healthcare environments. Its underlying agentic AI architecture integrates planning, memory, reasoning, and environmental interaction to enable autonomous tool use, multi-agent collaboration, and continuous perception decision action loops across diverse healthcare applications and clinical workflows. The review further examines safety mechanisms, including human-in-the-loop oversight, self-verification strategies, and regulatory alignment frameworks, which are designed to ensure reliability, accountability, compliance, and safe deployment in regulated healthcare environments. Our findings indicate that a large number of AI agents have been introduced in various manuscripts for healthcare applications; however, fully autonomous systems remain challenging to achieve, as AI still faces several limitations related to reliability, interpretability, data dependency, and integration within complex clinical workflows. In response to these challenges, this survey shifts the focus from task-specific model performance to system-level autonomy and workflow orchestration, providing a structured foundation for understanding the design, deployment, governance, and limitations of agentic AI systems in modern healthcare ecosystems. Full article
(This article belongs to the Special Issue Intelligent Sensor and Internet of Medical Things for Smart Healthcare)
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