Internet of Things in Telemedicine: A Systematic Review of Current Trends and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main aim of this review paper is to explore the role of Internet of Things (IoT) technologies in telemedicine and their impact on healthcare outcomes, focusing on chronic disease management, elderly care, and emergency services.This study conducted a systematic review following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).The research identified 15 peer-reviewed articles published between 2010 and 2024, focusing on the integration of IoT in telemedicine.Scopus database was chosen because it indexes scientific documents from various disciplines, such as Computer Science, Engineering, and Medicine.The following search terms were used: "IoT" OR "Internet of Things" AND "Telemedicine."After screening the titles, abstracts, and full texts, 15 studies met the inclusion criteria for analysis.The review found that IoT technologies significantly improve patient outcomes, with chronic disease management showing a 20-30% reduction in complications.Cloud integration enhances scalability and real-time monitoring, facilitating better elderly care, especially in remote areas.However, challenges related to data security, interoperability, and the cost of IoT devices were noted.The findings suggest that IoT holds great potential for transforming healthcare delivery, further research is needed to address data privacy challenges, cost-effectiveness, and integration into existing healthcare systems.These insights are valuable for healthcare providers, policymakers, and technology developers working to implement IoT-based solutions in telemedicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it