Both databases were searched on May 2024 for documents published between 2010 and 2024. The 2010 lower bound was chosen because the technological advances since 2010 have transformed the medical-device landscape. The database-specific queries were as follows.

We initiated our data collection with a search using the query: ((“medical devices” AND (“artificial intelligence” OR “internet of medical things” OR “deep learning” OR “Internet of Things” OR “machine learning” OR “Augmented reality”)). we refined our dataset by applying inclusion criteria based on specific keywords such as “Artificial Intelligence” or “Internet Of Things” or “Medical Devices” or “Health Care” or “Machine Learning” or “Biomedical Equipment” or “Big Data” or “Medical Device” or “Deep learning” or “Wearable Devices” or “Internet Of Medical Things” or “Cloud Computing” or “Augmented Reality” or “Wearable Medical Devices” or “Virtual Reality” or “Real-time”. This refinement was geared towards focusing on the specific technologies and their applications in medical devices.

Publications were included if they (1) addressed the integration of AI, IoMT, AR, cybersecurity, or related emerging technologies in medical devices; (2) were peer-reviewed journal articles, conference papers, reviews, or book chapters; and (3) were available in English with complete bibliographic metadata. Records were excluded if they were unrelated to medical devices, were commentaries, editorials, or non-scientific reports, or lacked sufficient metadata for bibliometric processing.

Records retrieved from the two databases were exported and combined in R using the Bibliometrix package,¹⁷ which merged the datasets and removed duplicates by matching on Digital Object Identifier (DOI) and, where DOIs were absent, on normalized title and source. Titles, abstracts, and keywords were then screened against the inclusion/exclusion criteria. Screening was performed independently by the authors of this manuscript; as well as the disagreements were resolved by them.

The selection process is summarized in the PRISMA flow diagram¹⁸ ( Figure 1). The number of records identified, de-duplicated, screened, excluded, and finally included is reported there, yielding a final corpus of 3,094 documents.

For advanced analysis and visualization, we used Biblioshiny from Bibliometrix,¹⁷ and VOSviewer,¹⁵ the latter constructing bibliometric maps with the VOS (Visualization of Similarities) technique, which uses mathematical algorithms to represent relationships between items.

Two forms of limitation are relevant. First, publication bias may arise from database-coverage differences and the predominance of English-language publications; to mitigate this we combined Scopus and Web of Science to broaden geographical and disciplinary representation, while acknowledging that indexing practices and language restrictions may still underrepresent emerging regions and local scientific outputs. Second, and by design, a bibliometric analysis of publication metadata does not assess the methodological quality, internal validity, or clinical validity of the individual studies it counts; consequently, no formal critical appraisal or risk-of-bias assessment was performed. This is an inherent limitation of the design rather than an omission, and readers should interpret the findings as a map of research activity rather than a synthesis of clinical evidence.

Table 1 summarizes the bibliometric profile of the corpus (2010–2024): 3,094 documents from 1,747 sources, with a 27.8% annual growth rate, an average of 13.92 citations per document, and a mean document age of 3.24 years. The dataset includes 10,695 authors, of whom a substantial share participates in international collaboration (16.29%). Articles predominate (1,405), followed by conference papers (580), reviews (376), and book chapters (165).

Artificial Intelligence (AI) simulates human intelligence in programmed machines capable of learning and reasoning. It includes machine learning, where algorithms improve from data without explicit programming; deep learning, which processes data through neural networks; natural language processing (NLP), enabling machines to interpret human language; and computer vision, which interprets visual data. In healthcare, AI applications range from robot-assisted surgery offering high precision to virtual nursing assistants that reduce unnecessary hospital visits and staff burden.^19–21 AI’s impact extends across sectors, for example, real-time fraud detection in finance and autonomous-vehicle safety in transportation, illustrating its broad transformative potential.^1,2

The Internet of Things (IoT) links physical objects embedded with sensors and software that connect and exchange data over the internet, enabling real-time monitoring, control, and optimization across sectors; in manufacturing, for instance, IoT supports predictive maintenance that reduces downtime.^5,6,12

In healthcare, the Internet of Medical Things (IoMT) applies IoT to patient monitoring and care: smart inhalers and wearable trackers collect health data and improve chronic-disease management, and IoMT supports remote patient monitoring that enables informed, timely clinical decisions, moving healthcare toward more patient-centred, data-driven delivery.^{7–9,22–24}

Augmented Reality (AR) enhances the physical world with digital overlays, providing real-time information; in medicine, AR can display patient data during procedures for more precise and safer surgery, and in education it brings complex concepts to life. Virtual Reality (VR) creates immersive environments widely used in therapy and rehabilitation, for example, controlled, intensive sessions for patients recovering from stroke or injury, accelerating recovery without the physical constraints of the real world.^14,25–29

Big-data analysis examines vast datasets to uncover patterns, trends, and insights. In healthcare it is used to analyze patient information, predict disease patterns, and anticipate treatment outcomes, supporting more effective, individualized interventions. Its growing capabilities continue to influence societal efficiency and economic activity.^3,4

As digital technologies permeate all aspects of life, cybersecurity has become a critical pillar of the digital economy, essential for protecting sensitive data and preventing cyber threats. In healthcare, robust cybersecurity protects patient records and ensures the integrity of medical devices. The growing adoption of IoT devices has expanded the attack surface, requiring advanced strategies to secure interconnected devices and networks; emerging approaches include the use of AI to detect anomalous patterns quickly and accurately, enabling proactive threat prevention.^10–12

Figure 2 illustrates the frequency of key terms related to advanced technologies in healthcare over 2010–2024. Terms such as “Machine Learning”, “Artificial Intelligence”, “Deep Learning”, “Healthcare”, “Security”, “Internet of Things (IoT)”, “Medical Devices”, and “Internet of Medical Things” rise markedly, particularly from 2016 onward. These trends have identifiable drivers. The steep rise of “Deep Learning” and “Artificial Intelligence” from around 2015–2016 coincides with the breakthrough performance of deep neural networks in computer vision and their rapid diffusion into medical imaging. The acceleration of “Healthcare”, “Internet of Things”, and “Internet of Medical Things” around 2019–2021 aligns with the expansion of remote patient monitoring and telehealth, which the COVID-19 period intensified. The consistent growth of “Security” reflects rising attention to the protection of sensitive medical data following high-profile healthcare and device-related cyber-incidents. Together these trends indicate not merely increased usage but a field reorganizing around connected, data-driven, and AI-enabled devices.

Figure 3 shows the global distribution of scientific production. The United States leads (1,248 publications), followed by China (737), India (654), and the United Kingdom (437); other notable contributors include Italy, Germany, South Korea, and Japan. Africa’s output, while smaller, shows emerging contributions from Egypt (54), South Africa (22), and Morocco (22), with Morocco notable for its growing role on the continent.

These are raw publication counts and should be read with care, because absolute counts favor large, well-funded research systems. The leading positions of the United States, China, and India are consistent with their population size, aggregate R&D investment, and established biomedical-device industries, rather than indicating higher per-researcher productivity. Normalizing output by population or by gross domestic expenditure on R&D would reorder the ranking and reveal high per-capita performers among smaller research systems; several European countries, for example, produce large volumes relative to population. We therefore present the counts as indicators of aggregate research capacity, and we identify normalized bibliometric benchmarking (per capita, per GDP, or per R&D expenditure) as an important direction for future work. Read in this light, the African figures represent a genuine and growing, if still emerging, participation rather than a marginal one.

VOSviewer¹⁵ was used to map co-occurrence structure with a minimum keyword co-occurrence of 50, yielding 66 keywords and three clusters Figure 4: (i) an AI and AR cluster (red), on integrating AI and AR to improve diagnostic accuracy and patient outcomes; (ii) an IoT and cybersecurity cluster (green), on protecting sensitive medical data and ensuring the reliability of connected devices; and (iii) an embedded systems cluster (blue), on hardware/software integration for efficient, real-time device operation.

Two caveats qualify this map. First, the clusters are not static: the AI/AR cluster expanded most rapidly in the second half of the period, and cybersecurity terms migrated from the periphery toward the core as connectivity increased, indicating a field consolidating around AI-enabled, connected devices. Second, the high minimum co-occurrence threshold (50) biases the map toward high-frequency, generic terms and can suppress specialized but important topics; notably, interoperability, standardization, and post-market surveillance are comparatively under-represented relative to their practical significance. The three-cluster structure should therefore be read as a robust first-order summary rather than an exhaustive taxonomy, and the apparent tidiness of three clusters is partly an artefact of the threshold choice.

The corpus also carries a clear collaboration and growth signal. Co-authorship averages 4.74 authors per document, with 16.29% of documents involving international collaboration and only 231 single-authored documents out of 3,094, indicating a strongly team-based, increasingly cross-border research culture, consistent with the multidisciplinary nature of medical-device engineering (spanning clinical, computational, and hardware expertise). The 27.8% compound annual growth rate over 2010–2024 substantially outpaces the growth of the general biomedical literature, quantitatively substantiating the “emerging” characterization of the field rather than asserting it qualitatively. Read together, the rising international co-authorship and the steep growth trajectory suggest a field transitioning from isolated national efforts toward a globally networked research enterprise.

The integration of AI and AR is enhancing efficiency, safety, and patient outcomes. Advances in AI and AR are improving diagnostics, personalized treatment, and monitoring²⁹; AI enhances the precision of converting mechanical to electrical signals in devices, while AR provides real-time visual feedback that improves usability³⁰; and AI and AR are increasingly applied in health metaverses that raise patient engagement and treatment outcomes through virtual and augmented experiences.³¹

IoT and cybersecurity are jointly reshaping healthcare. IoT improves the quality and efficiency of medical services, with AI and machine learning strengthening cybersecurity¹¹; security enhancements for IoMT protect medical data and patient safety¹²; secure communication protocols address message-level identification needs¹³; edge AI enables real-time data processing with embedded security²²; 21 and overviews of IoMT emphasize the cybersecurity challenges and solutions for protecting connected devices.²³

Embedded integration enhances the accuracy, safety, and efficiency of treatment and monitoring. TrustFlow-X provides fine-grained control-flow integrity against memory-based attacks in life-critical devices³²; a wearable, energy-efficient single-channel ECG system enables long-term obstructive sleep apnea monitoring with real-time, patient-specific detection³³; and Timed Automata extended with Tasks (TAT) formalize medical guidelines to enable verification and improve protocols.³⁴

The five technology families differ substantially in maturity, benefit, and implementation difficulty. Table 5 summarizes a comparative evaluation, which the surrounding discussion expands. AI/ML is the most transformative but faces the steepest validation, transparency, and data-governance hurdles; IoMT delivers immediate monitoring value but concentrates security and interoperability risk; AR/VR is clinically compelling in training and navigation yet remains comparatively niche and hardware-dependent; embedded systems are foundational and mature but constrained by real-time and safety-certification demands; and cybersecurity is a cross-cutting enabler whose absence undermines all of the others.

Several devices deployed illustrate the practical impact of these technologies. Autonomous AI diagnosis: IDx-DR became the first FDA-authorized system to make an autonomous diagnostic decision without physician interpretation, detecting diabetic retinopathy from retinal images in primary-care settings.³⁵ AI-enabled cardiac monitoring: large-scale, app-based screening using photoplethysmography and single-lead ECG on consumer wearables has demonstrated feasibility for detecting atrial fibrillation at population scale.³⁶ Closed-loop insulin delivery: “artificial-pancreas” systems couple continuous glucose monitoring with algorithmic control of insulin pumps, exemplifying tight IoMT – embedded – AI integration in a life-critical device. Robot-assisted surgery and AR navigation: surgical robots combined with AR overlays of anatomy and imaging enhance precision and reduce the need to divert attention to separate monitors.^27,37 These examples ground the bibliometric trends in concrete clinical value while also surfacing the validation, cost, and integration challenges discussed above.

As devices become connected and AI-driven, the threat surface broadens beyond classical data breaches. Generative AI introduces new risks, training-data leakage, model-inversion and membership-inference attacks that can expose patient data, and prompt-injection against clinical language interfaces. Adversarial attacks can perturb medical images or signals to cause confident misclassification by diagnostic models, a demonstrated vulnerability with direct safety implications.³⁸ At the network level, distributed denial-of-service (DDoS) attacks can disrupt hospital connectivity and the availability of monitoring services, while man-in-the-middle (MITM) attacks can intercept or alter device telemetry between sensors, gateways, and clinical systems.

Correspondingly, privacy-preserving and secure-AI approaches are becoming central to intelligent healthcare systems. Federated learning trains models across institutions without moving raw patient data, reducing exposure while enabling multi-site learning³⁹; differential privacy adds calibrated noise to bound the information any individual record contributes to a model; and edge computing keeps sensitive processing on or near the device, lowering latency and limiting data in transit.²² Combined with strong authentication, encryption, and secure-by-design development, these techniques form a layered defence appropriate to AI-enabled devices.

Significant implementation challenges remain. Infrastructure limitations, reliable connectivity and computational resources, hinder adoption, especially in rural or underserved areas, risking a digital divide in care quality. Interoperability is constrained by proprietary standards, making standardized protocols and interfaces essential for seamless integration across manufacturers. Clinical adoption further depends on rigorous validation, workflow integration, clinician training, and trust in opaque AI models, where biased training data can produce unequal performance across populations and over-reliance on automation must be avoided.

This study has limitations beyond those inherent to its design (Section 2.5). The corpus is restricted to Scopus- and Web of Science-indexed, English-language documents, which may exclude relevant regional or non-English research; predefined keywords may omit studies using different terminology; and, as a bibliometric analysis, it maps research activity through metadata and does not assess the methodological or clinical quality of individual studies. Despite these limits, the results consistently indicate a shift toward more intelligent, connected, and data-driven medical devices.

All ethical guidelines were strictly adhered to during the conduct of this research. Ethical approval and consent were not required, as the study analyses published bibliographic metadata and does not involve human participants or animals.

We would like to express our profound appreciation to the Research Foundation for Development and Innovation in Science and Engineering (FRDISI). Their invaluable support and the favorable environment they provided have been instrumental in conducting this research.

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Published: 17 Aug 2026, 13:1007

Published: 19 Mar 2026, 13:1007

Published: 04 Sep 2024, 13:1007

Current Reviewer Status: ?

VERSION 2

PUBLISHED 19 Mar 2026

Revised

Reviewer Report 01 Jul 2026

N. Chaithra Chaithra, JAIN (Deemed to be University), Bangalore, Karnataka, India 

Approved with Reservations

VIEWS 0

Reviewer Report 01 Jul 2026

Soha Rawas, Faculty of Science, Department of Mathematics and computer Science, Beirut Arab University, Beirut, Lebanon 

Not Approved

VIEWS 0

Reviewer Report 07 May 2026

Shehu Mohammed, Department of Computer Science, Abdu Gusau Polytechnic, Talata Mafara, Zamfara, Nigeria;  School of Computer Applications, Lovely Professional University, Phagwara, Punjab, India 

Approved with Reservations

VIEWS 0

VERSION 1

PUBLISHED 04 Sep 2024

Reviewer Report 11 Dec 2025

Ibrahim Adabara, Kampala International University - Western Campus, Bushenyi, Uganda 

Approved with Reservations

VIEWS 0