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Open research questions in Healthcare Technology and Patient Monitoring

78 gap statements mined from Healthcare Technology and Patient Monitoring papers in our 4.5M-paper local library, which holds 343 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The lack of interpretability and transparency of AI models. The need for prospective validation and multicenter validation of AI tools in ICUs. The limited integration of AI with clinical decision-making processes.

    Smart ICUs: The Role of Artificial Intelligence in Modern Intensive Care Units · 2026 · DOI

  • The challenges identified by the paper include the lack of standard cybersecurity controls for legacy medical devices. The physical or insider attacker with legitimate device proximity is a significant challenge.

    A practical cybersecurity framework for legacy medical devices · 2026 · DOI

  • The research gap identified by the paper is the lack of a comprehensive cybersecurity framework for legacy medical devices. The existing frameworks have limitations, such as not addressing physical or insider attackers.

    A practical cybersecurity framework for legacy medical devices · 2026 · DOI

  • The study suggests that future research should investigate the technical challenges associated with the adoption of Industry 5.0 technologies. The research implies that further study is needed on the importance of strategic planning, infrastructure investment, and interdisciplinary collaboration in the adoption of new technologies.

    HUMAN-CENTRIC INNOVATION IN HEALTHCARE: THE ROLE OF INDUSTRY 5.0 TECHNOLOGIES IN ENHANCING PERSONALIZED PATIENT CARE · 2026 · DOI

  • The research highlights the need for further investigation into the barriers to the adoption of these technologies. The study suggests that there is a lack of research on the importance of human-centric innovation in the adoption of new technologies.

    HUMAN-CENTRIC INNOVATION IN HEALTHCARE: THE ROLE OF INDUSTRY 5.0 TECHNOLOGIES IN ENHANCING PERSONALIZED PATIENT CARE · 2026 · DOI

  • The paper suggests that future research should focus on addressing the challenges related to Digital Twins in biomedical engineering. The paper highlights the need for further research on the applications of Digital Twins in biomedical engineering. The paper suggests that future research should explore the use of Digital Twins in other areas of healthcare.

    Digital Twins in Biomedical Engineering · 2026 · DOI

  • The paper highlights the need for clear and transparent regulatory frameworks, accountability mechanisms, and policies for equal access to avoid technological gaps.

    Digital Twins in Biomedical Engineering · 2026 · DOI

  • Given the limited evidence base and the NHS’s largely reactive response, applying the EBC framework revealed variations in resources, service delivery, and patient experiences.

    Exploring the role of a systems approach in improving long-COVID clinics · 2026 · DOI

  • As stakeholder engagement emerged as a key limitation, the next step is to advance to the “Design” phase, involving all relevant stakeholders in MBSE-based validation to refine and optimise the system model. Future research should also explore integrating technologies and service models from comparable chronic conditions to improve efficiency, scalability, and sustainability.

    Exploring the role of a systems approach in improving long-COVID clinics · 2026 · DOI

  • The need for more effective treatments for mental health disorders. The lack of accessibility to medical education and healthcare services in certain parts of the world. The need for more personalized and targeted interventions for chronic disease management.

    Artificial Intelligence and Virtual Reality in the Metaverse: A Scoping Review of Human-Centered Applications in Public Health · 2026 · DOI

  • The lack of prospective aviation-specific studies to validate AI systems under real-world flight conditions is a significant research gap.

    Artificial Intelligence and telemedicine integration in managing in-flight medical emergencies: Current evidence, clinical applications and future directions · 2026 · DOI

  • There is a lack of prospective aviation-specific studies to validate AI systems under real-world flight conditions. Most existing evidence regarding AI performance originates from hospital and outpatient healthcare settings rather than aviation environments.

    Artificial Intelligence and telemedicine integration in managing in-flight medical emergencies: Current evidence, clinical applications and future directions · 2026 · DOI

  • Lightweight, localized, and mobile-driven AI systems – designed to function in resource-constrained environments – can empower frontline health workers, ease diagnostic bottlenecks, and reimagine medical delivery in clinics where conventional infrastructure is scarce.

    Integrating artificial intelligence into resource-constrained clinics in South Asia · 2026 · DOI

  • Less is known about how routinely available patient characteristics relate to alarm patterns in specific ICU populations. There is a lack of understanding of whether similar monitor configurations produce similar alarm rates across clinically different units.

    Patient characteristics and monitor alarms in a cardiac ICU: A retrospective cross-sectional study · 2026 · DOI

  • Limited information technology infrastructure and unique challenges in LTC settings require a comprehensive examination of AI’s potential to enhance care quality and operational efficiency.

    Application of artificial intelligence to electronic health record data in long-term care facilities: a scoping review · 2026 · DOI

  • Although artificial intelligence (AI) has been widely applied to electronic health record (EHR) data in hospital settings, its use in long-term care (LTC) facilities remains unexplored.

    Application of artificial intelligence to electronic health record data in long-term care facilities: a scoping review · 2026 · DOI

  • Larger prospective studies are warranted to determine whether real-time AI-assisted device tracking influences procedural decision-making, workflow, safety, or clinical outcomes.

    Clinical Implementation and Performance of Real-Time AI System for Device Tracking in Neurointervention: First U.S. Evaluation of Neuro-Vascular Assist · 2026 · DOI

  • Alarms associated with intravenous smart pumps (IVSPs) contribute to alarm fatigue; however, little is known about what causes unnecessary IVSP alarms or how to reduce them.

    Exploring the impact of medication type and infusion duration on IV smart pump alarms · 2026 · DOI

  • By integrating ontology engineering with AI-based reasoning and classification techniques, the framework offers a structured, adaptable approach to modelling, eliciting, and validating NFRs in complex, data-intensive domains like healthcare.

    A knowledge-driven semantic framework for non-functional requirement engineering in intelligent healthcare systems · 2026 · DOI

  • The inclusion of articles was limited to those published within the last 5 years and available in Dutch and English. The exclusion of gray literature could introduce publication bias. A single researcher primarily conducted the title-abstract and full-text screening, which increased the risk of selection bias.

    Identifying and Comparing Intervention Cost Components in Remote Patient Monitoring: A Scoping Review · 2026 · DOI

  • Current healthcare environments often require separate devices for different diagnostic tests. This separation results in complexity and inefficiency.

    Intelligent Integrated Medical Diagnostic Capsule (IIMDC) · 2026 · DOI

  • Conventional techniques of IV fluid monitoring require manual interventions by healthcare professionals. Many research works have focused on fluid level detection and simple alerting methods, but there are still issues with cost-effectiveness, ease of use, and accessibility of monitoring.

    Intra Venous (IV) Fluid Monitoring System with Real-Time Alert · 2026 · DOI

  • The development of more interpretable and transparent AI models. The conduct of prospective validation and multicenter validation studies. The exploration of new applications of AI in ICUs, such as personalized medicine and patient-centered care.

    Smart ICUs: The Role of Artificial Intelligence in Modern Intensive Care Units · 2026 · DOI

  • Bristol Myers Squibb (BMS), Novartis, and Curis, outside the submitted work. C.W. declares no competing interests.

    Enhancing the resilience of remote patient monitoring and hospital-at-home systems: a digital-twin-based framework · 2026 · DOI

  • Further investigation into the use of AI and VR for improving healthcare standards globally. Development of more effective AI-powered mental health interventions. Exploration of the use of virtual reality exposure therapy for treating other mental health disorders.

    Artificial Intelligence and Virtual Reality in the Metaverse: A Scoping Review of Human-Centered Applications in Public Health · 2026 · DOI

Most-cited papers in Healthcare Technology and Patient Monitoring

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Open questions raised by papers listed on this page include: (1) The challenges identified by the paper include the lack of standard cybersecurity controls for legacy medical devices (“A practical cybersecurity framework for legacy medical devices”, 2026); (2) The study suggests that future research should investigate the technical challenges associated with the adoption of Industry 5.0 technologies (“HUMAN-CENTRIC INNOVATION IN HEALTHCARE: THE ROLE OF INDUSTRY 5.0 TECHNOLOGIES…”, 2026); (3) The paper suggests that future research should focus on addressing the challenges related to Digital Twins in biomedical engineering (“Digital Twins in Biomedical Engineering”, 2026); (4) Future research should also explore integrating technologies and service models from comparable chronic conditions to improve efficiency, scalability, and sustainability (“Exploring the role of a systems approach in improving long-COVID clinics”, 2026); (5) The lack of accessibility to medical education and healthcare services in certain parts of the world (“Artificial Intelligence and Virtual Reality in the Metaverse: A Scoping Review…”, 2026). 78 gap statements have been mined from Healthcare Technology and Patient Monitoring papers in our 4.5M-paper local library, which holds 343 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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