Developing medical devices with emerging technologies: trends, challenges, and future directions
Bibliographic record
Abstract
This paper examines the rapid advancements and integration of emerging technologies in the medical field, particularly focusing on Artificial Intelligence (AI), the Internet of Medical Things (IoMT), Augmented Reality (AR), and cybersecurity. The study leverages data from Scopus and Web of Science databases to highlight the trends, challenges, and future directions in the development of medical devices. Significant progress has been made in enhancing patient care through the integration of AI and IoMT, which facilitate predictive analytics, personalized treatment plans, and real-time data monitoring. AR is transforming medical training and surgical precision, while cybersecurity measures are becoming increasingly vital to protect sensitive health data. Despite these advancements, the field faces challenges such as data privacy concerns, infrastructure limitations, and interoperability issues. The study also explores Africa’s contributions, with a particular emphasis on Morocco’s emerging role in this technological landscape. Three major research clusters identified include AI and AR, IoT and cybersecurity, and embedded systems, each playing an important role in the evolution of medical technologies. By analyzing publications from 2010 to 2024, the paper provides insights into the current state and future potential of advanced medical technologies, aiming to offer a foundation for further research and innovation in this rapidly evolving field.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".