Although the medical device industry operates within a stringent regulatory environment, the growing deployment of connected, intelligent medical devices (CIMDs) in the healthcare sector is challenging these established regulatory frameworks. CIMDs come in a variety of forms, from implantables, to specialist IoMT devices deployed at the point-of-care, to AI-based medical devices, and AI as a medical device (AIaMDs). These devices raise several cybersecurity, data management, and algorithmic integrity concerns for patient safety and the delivery of reliable, responsible healthcare. The purpose of this article is to focus on a particular characteristic of CIMDs: their changing risk profile, several times throughout their lifecycle, with limited awareness from users, manufacturers, and regulators. Looking at the implications of these often subtle yet meaningful software modifications for current medical device regulations and for critical stakeholders in the CIMD ecosystem, the article highlights three main challenges to: i) risk assessment, classification and management frameworks that underpin current medical device regulations; ii) current medical device compliance frameworks, especially the post-market surveillance of medical devices; and iii) the detection, categorization, and reporting of compromised devices that might not perform according to their intended purpose. The article brings empirical evidence from a qualitative research study conducted with critical stakeholders in the medical device sector.

Neurotechnology is a rapidly emerging field with vast potential within healthcare, but also has inherent concerns. There is, therefore, a need to ensure the responsible and ethical development and regulation of these technologies. This horizon scan aimed to provide an overview of neurotechnologies in development and those approved by the FDA as of June 2024 for a range of conditions relating to mental health, healthy ageing, and physical disability. Searches of clinical trials, conferences, journals, and news were performed in March 2024. Relevant technologies were identified through a process of screening, data extraction and synthesis. A total of 81 unique neurotechnologies were identified, with 23 relating to mental health, 31 to healthy ageing, and 42 to physical disability. A total of 79% percent did not yet have FDA approval and 77.4% were at earlier stages of development (pilot/feasibility studies), with 22.6% at pivotal or post-market stages. Digital elements were common features of the technologies, including software, apps, and connectivity to other devices; however, there were only three technologies with an identifiable AI component. A complex regulatory landscape and unique ethical and safety concerns associated with neurotechnology could pose challenges to innovators, though the emerging nature of the field also provides an opportunity to pre-emptively address potential issues.

Background: Health care professionals receive little training on the digital technologies that their patients rely on. Consequently, practitioners may face significant barriers when providing care to patients experiencing digitally mediated harms (eg, medical device failures and cybersecurity exploits). Here, we explore the impact of technological failures in clinical terms. Objective: Our study explored the key challenges faced by frontline health care workers during digital events, identified gaps in clinical training and guidance, and proposes a set of recommendations for improving digital clinical practice. Methods: A qualitative study involving a 1-day workshop of 52 participants, internationally attended, with multistakeholder participation. Participants engaged in table-top exercises and group discussions focused on medical scenarios complicated by technology (eg, malfunctioning ventilators and malicious hacks on health care apps). Extensive notes from 5 scribes were retrospectively analyzed and a thematic analysis was performed to extract and synthesize data. Results: Clinicians reported novel forms of harm related to technology (eg, geofencing in domestic violence and errors related to interconnected fetal monitoring systems) and barriers impeding adverse event reporting (eg, time constraints and postmortem device disposal). Challenges to providing effective patient care included a lack of clinical suspicion of device failures, unfamiliarity with equipment, and an absence of digitally tailored clinical protocols. Participants agreed that cyberattacks should be classified as major incidents, with the repurposing of existing crisis resources. Treatment of patients was determined by the role technology played in clinical management, such that those reliant on potentially compromised laboratory or radiological facilities were prioritized. Conclusions: Here, we have framed digital events through a clinical lens, described in terms of their end-point impact on the patient. In doing so, we have developed a series of recommendations for ensuring responses to digital events are tailored to clinical needs and center patient care.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: This study investigates the role of and methods for patent analysis in advancing medical technology (MedTech) innovation, a sector characterized by diverse, non-pharmacological or non-immunological healthcare technologies and significant research investment. Patents are critical early indicators of innovation, supporting horizon scanning and weak signal detection. Objective: The study aimed to identify intellectual property sources, evaluate methods for patent retrieval and analysis, and outline objectives for using patent data to anticipate trends and inform healthcare strategies. It also offered a methodological framework to support stakeholders in adopting innovative MedTech solutions. Methods: A rapid review (RR) was conducted using Cochrane Rapid Review Methods and PRISMA guidelines, with a pre-registered protocol on the Open Science Framework. Searches in Embase, IEEE Xplore, and Web of Science targeted records from 2020 onwards. Three independent reviewers screened studies using Rayyan. We included any study type, published since 2020 that provided sufficient data on patent sources, methods and tools applied to the study of MedTech. Our data extraction included bibliographic details, study characteristics, and methodological information. Risk of bias assessments were not undertaken. Narrative and tabular methods, supplemented by visual charts, were employed to synthesise findings. Results: Our searches identified 1,741 studies, of which 124 were included after title, abstract, and full-text screening, with 54% being original research, 44% reviews, and the remainder being conference abstracts. Most studies (68%) relied solely on patent databases, while others searched the grey literature. Research objectives of the included studies were grouped into nine themes, with trend analysis (50%) and policy recommendations (20%) being the most common. The review analysed 199 patent databases, with 27% of studies using multiple sources. Time horizons for patent searches averaged 24.6 years, ranging from 1900 to 2019. Automated approaches, employed in 33% of studies, frequently utilised tools like Gephi for network visualization. Disease mapping, based on NICE classification, indicated that cancer (19%) and respiratory conditions (16%), particularly COVID-19, were key areas, while digital health dominated the "health and social care delivery" category. Conclusions: The review highlights the value of patent data in trend analysis and its broader role in shaping policies and research strategies. While patents provide crucial insights into emerging technologies, inconsistent de-duplication practices across studies pose a risk of data inflation, accentuating the need for transparency and rigour. Finally, this review emphasized the importance of data transformation and visualization in detecting emerging trends with Python and R being the most commonly used programming languages for developing custom tools. Clinical Trial: N/A

BACKGROUND Health care professionals receive little training on the digital technologies that their patients rely on. Consequently, practitioners may face significant barriers when providing care to patients experiencing digitally mediated harms (eg, medical device failures and cybersecurity exploits). Here, we explore the impact of technological failures in clinical terms. OBJECTIVE Our study explored the key challenges faced by frontline health care workers during digital events, identified gaps in clinical training and guidance, and proposes a set of recommendations for improving digital clinical practice. METHODS A qualitative study involving a 1-day workshop of 52 participants, internationally attended, with multistakeholder participation. Participants engaged in table-top exercises and group discussions focused on medical scenarios complicated by technology (eg, malfunctioning ventilators and malicious hacks on health care apps). Extensive notes from 5 scribes were retrospectively analyzed and a thematic analysis was performed to extract and synthesize data. RESULTS Clinicians reported novel forms of harm related to technology (eg, geofencing in domestic violence and errors related to interconnected fetal monitoring systems) and barriers impeding adverse event reporting (eg, time constraints and postmortem device disposal). Challenges to providing effective patient care included a lack of clinical suspicion of device failures, unfamiliarity with equipment, and an absence of digitally tailored clinical protocols. Participants agreed that cyberattacks should be classified as major incidents, with the repurposing of existing crisis resources. Treatment of patients was determined by the role technology played in clinical management, such that those reliant on potentially compromised laboratory or radiological facilities were prioritized. CONCLUSIONS Here, we have framed digital events through a clinical lens, described in terms of their end-point impact on the patient. In doing so, we have developed a series of recommendations for ensuring responses to digital events are tailored to clinical needs and center patient care.

Strategic foresight relies on horizon scanning to detect weak signals of innovation and anticipate disruption in fast-moving MedTech. While tools such as the Rumsfeld matrix and the Three Horizons model structure thinking about uncertainty and time, their application in MedTech is limited and insufficient for addressing the regulatory, technical, and evidential complexity of emerging or disruptive technologies. Regulatory frameworks provide stability through defined safety and performance pathways, but shifts and regional variability requirements complicate anticipatory assessment. These challenges expose methodological gaps in current practice: broad foresight frameworks lack the technical granularity, regulatory alignment, and systematic processes required for MedTech, where rapid innovation intensifies uncertainty. This paper presents a structured methodological framework to support systematic, reproducible, and decision-focused horizon scanning in MedTech. The framework aims to (1) standardize methods for planning, execution, and reporting; (2) reduce uncertainty through systematic identification and interpretation of weak signals; and (3) improve transparency, quality, and comparability across policy, regulatory, clinical, and strategic contexts. The framework was developed following the 3-step process by McMeekin et al. First, data were identified through a mapping review of MedTech futures and foresight methods, mapping UK health care decision-makers, and participating in national and international horizon scanning initiatives (2021-2025). Second, the framework was constructed by integrating the sources, methods, and insights with the principles of regulatory and technology readiness, supported by ongoing consultation with UK regulatory and health system bodies. Third, the framework is being applied and tested in MedTech horizon scanning projects. Formal validation and iterative revision are planned. The MedTech Innovation Scanning Techniques (MIST) framework integrates foresight theory, horizon scanning methods, and the MedTech innovation pathway, enabling systematic weak signal detection across 3 time horizons. In the imminent horizon (technology readiness level [TRL] 8-9), uncertainty is lowest, and weak signals are more visible, supported by rapid evidence synthesis and expert engagement. In the transitional horizon (TRL 4-7), uncertainty increases due to trial outcomes, funding, and market dynamics, requiring broader evidence sources and complementary techniques such as scenario analysis and bibliometrics. In the emerging horizon (TRL 1-3), weak signals are abundant but least predictable; early-stage data (patents, preclinical research, and conference outputs) are analyzed using methods such as term-similarity visualization to reveal nascent innovations. Across all horizons, MIST supports systematic signal identification, contextualization, and prioritization to inform anticipatory decision-making. MedTech horizon scanning is challenging. MIST addresses these challenges by integrating the Three Horizons model, Rumsfeld matrix, technology pathways, regulatory considerations, and sources to guide weak signal detection. MIST provides a structured, transparent, and reproducible approach tailored to MedTech. While formal validation and integration of equity and ethical dimensions are ongoing, the framework fills a critical methodological gap in horizon scanning for MedTech.