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Open research questions in Healthcare Technology and Patient Monitoring

56 unresolved questions extracted from the limitations and future-work sections of 170 Healthcare Technology and Patient Monitoring papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • 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

  • Significant challenges to resilience in remote patient monitoring and hospital-at-home systems. Cybersecurity-related risks for patients. The need for continuous and safe patient monitoring even in the face of cyber threats.

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

  • The variability of distribution of contamination in food products, - The need for context-specific validation data, - The limited adoption of on-site diagnostic technologies in the food sector

    On-site Diagnostics: Recommendations for Development and Deployment of On-Site diagnostics. · 2026 · DOI

  • Cybersecurity, data privacy, regulatory oversight, and implementation costs are significant barriers to the adoption of AI-enabled aviation healthcare systems. The limited diagnostic equipment, restricted medical resources, and confined physical space in aircraft cabins make it difficult to manage complex medical situations. 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

  • Examination complexity. Limited accessibility to health assessment services. Inefficiency in current healthcare environments.

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

  • Unreliable electricity. Intermittent connectivity. Limited clinical staffing. Limited digital literacy.

    A context-specific IoMT framework for energy-efficient remote monitoring of chronic patients in resource-limited settings · 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

  • This study presented a knowledge-driven semantic framework designed to enhance non- functional requirement (NFR) engineering in intelligent healthcare systems. By integrat- ing 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. The application of this framework in a clinical decision support system for chronic heart failure demonstrated notable improvements in NFR coverage, traceability, Bhar et al. Discover Artificial Intelligence (2026) 6:446 Page 20 of 22 conflict resolution, and stakeholder satisfaction. Ontologies enabled formal represen- tation and automated reasoning, while AI techniques like BERT-based classification and rule engines improved requirement prediction and conflict detection. The hybrid methodology effectively bridged the gap between technical system design and regulatory compliance, ensuring that essential attributes such as privacy, explainability, and perfor- mance were properly addressed throughout the development lifecycle. This increased trustworthiness is grounded in concrete outcomes, particularly improved traceability, earlier detection and resolution of NFR conflicts, and consistently positive stakeholder feedback on clarity, accountability, and regulatory audit support. Despite its strengths, the framework has limitations: scalability for very large ontolo- gies (> 10,000 concepts) may require optimization; handling highly subjective NFRs (e.g., usability) relies on stakeholder input; ontology complexity could hinder adop- tion by non-experts. Currently, non-expert interaction is supported through template- based annotation workflows and predefined SPARQL query templates, reducing direct exposure to ontology syntax. To mitigate ontology complexity for non-experts, future work could include user-friendly graphical interfaces or automated ontology navigation tools to simplify interaction without requiring deep semantic web expertise. Addition- ally, while the ontology was built with reusability in mind, extending it to vastly different medical domains may require domain-specific refinements. Future work will focus on concrete improvements. First, reinforcement learning techniques will be explored to enable adaptive prioritization of NFRs based on run- time feedback and changing operational contexts. Second, to better support subjective NFRs, AI techniques like sentiment analysis and advanced NLP could be explored to semi-automate elicitation, reducing reliance on manual stakeholder input. Outputs from sentiment analysis and NLP modules will be transformed into ontology-compatible RDF assertions, allowing subjective NFR signals to be formally represented as NFRO instances. NLP-derived RDF assertions will require human-in-the-loop validation. Con- fidence thresholds and expert approval workflows will be applied before such asser- tions become active ontology knowledge, helping to mitigate risks of misinterpretation in safety-critical healthcare environments. Third, the framework will be integrated with DevOps pipelines (e.g., CI/CD workflows) to support continuous compliance check- ing and automated validation of evolving regulatory constraints. This integration would involve automated SPARQL queries against the ontology post-code commit to verify traceability, dynamic SWRL rule updates for new conflicts, and ML-based alerts in CI/ CD tools like Jenkins or GitHub Actions. Pilot studies will be conducted in additional clinical domains such as oncology and emergency response systems to assess the gener- alizability and scalability of the approach.

    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

  • Future research should focus on standardizing economic evaluations of remote patient monitoring interventions. Future research should investigate the cost-effectiveness of remote patient monitoring interventions. Future research should explore the use of digital health interventions in different healthcare settings.

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

  • 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

  • The current system has limitations in terms of manual monitoring and data entry - There is a need for automated fluid balance calculations and integrated hemodynamic monitoring

    SMART Intensive Care Unit (ICU) for better care and outcomes in hospital settings: A perspective on innovation · 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. The study highlights the need for research on the impact of Industry 5.0 technologies on patient outcomes and the overall quality of care.

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

  • The study identifies a gap in the understanding of the potential of Industry 5.0 technologies to transform healthcare. 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 identifies the challenges related to Digital Twins in biomedical engineering. The paper discusses the future directions of Digital Twins in biomedical engineering. 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

  • 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

  • content as needed and take full responsibility for the content of the publication. 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 consideration will be required to determine how evidence can inform deployment decisions for specific purposes, - The development of context-specific validation data

    On-site Diagnostics: Recommendations for Development and Deployment of On-Site diagnostics. · 2026 · DOI

  • Future research should explore the development of long-covid clinics. Future research should examine the role of stakeholders in the future development of long-covid clinics.

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

  • There is a need for a systems approach to improve long-COVID care. The current care model for long-COVID is variable and often inadequate.

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

  • Data quality, interoperability, model transparency, ethical issues, and lack of prospective clinical validation were the key difficulties encountered. The studies included in this report are from two separate but complementary lines of evidence. The evidence of consistent improvement in patient-centered outcomes is substantial variation, with most of it being observational.

    Artificial intelligence assisted telemedicine, clinical decision support for anesthesia and critical care in intensive care units: a scoping review · 2026 · DOI

  • Further studies are needed to validate the effectiveness of AI-assisted telemedicine systems in improving patient-centered outcomes. The development of AI-assisted telemedicine systems and policies should be informed by the study's findings. Research should focus on addressing the key difficulties encountered in the use of AI in telemedicine and tele-ICU delivery platforms.

    Artificial intelligence assisted telemedicine, clinical decision support for anesthesia and critical care in intensive care units: a scoping review · 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

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56 open questions have been extracted from the limitations and future-work passages of 170 Healthcare Technology and Patient Monitoring papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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