Proceedings from the 2025 Midwest Pediatric Device Consortium showcase featuring software as a medical device.

The Harmonization By Doing (HBD) program was established in 2003 as a partnership among stakeholders of academia, industry and regulatory agencies in Japan and the United States, with a primary focus on streamlining processes of global medical device development for cardiovascular medical devices. While HBD has traditionally focused on development of devices intended to treat conditions prevalent in adults, in 2016, HBD established the "HBD-for-Children" program, which focuses on the development of pediatric devices as the development of medical devices for pediatric use lags behind that of medical devices for adults in both countries. Activities of the program have included: (1) conducting a survey with industry to better understand the challenges that constrain the development of pediatric medical devices; (2) categorizing pediatric medical devices into five categories based on global availability and exploring concrete solutions for the early application and regulatory approval in both geographies; and (3) facilitating global clinical trials of pediatric medical devices in both countries. The establishment of the HBD-for-Children program is significant because it represents a global initiative for the introduction of pediatric medical devices for patients in a timely manner. Through the program, academia, industry and regulatory agencies can work together to facilitate innovative pediatric device development from a multi-stakeholder perspective. This activity could also encourage industry partners to pursue the development of pediatric medical devices.

Background: The integration of medical devices with artificial intelligence (AI) software is rapidly advancing as technology progresses. AI machine learning can be used in commercial medical services to generate practical data; there is evidence that it can be integrated into newly developed devices. However, such devices must undergo approval, regulation, and supervision. The Food and Drug Administration approves regulations for numerous machine-learning medical devices and shares open lists with the public. In this article, we examine recent medical AI devices in different fields, including the diagnosis of colorectal polyps.Current Concepts: Currently, in the field of gastroenterology, there has been a significant amount of research aimed at enhancing adenoma detection rates using tools powered by AI, such as the EndoScreener and GI Genius. Various such devices have also been developed for other fields; examples include the 23andMe Personal Genome Service for DNA detection, Spectral MD’s DeepView platform for wound imaging in surgery, Gili Pro BioSensor for monitoring vital signs, DreaMed Advisor Pro for diabetes, Minuteful for urinary analysis, BrainScope TBI for cerebral diagnosis, Compumedics Sleep Monitoring System for sleep disorders, Idx-DR v2.3 for ophthalmology, and EarliPoint system for pediatrics.Discussion and Conclusion: By the time this article is published, it is likely that even more AI medical devices will have been approved and commercialized. The development of such devices should be strongly encouraged. Additionally, we anticipate greater involvement from practitioners in the development and validation of diverse medical AI devices in Korea.

Healthcare cybersecurity risk management: keys to an effective plan.

The rapid advancement of technology, particularly artificial intelligence (AI), is transforming the world of management. This chapter delves into the futuristic trends in management, focusing on the integration of AI into various managerial aspects, its potential benefits, challenges, and strategies for successful adoption. AI-driven decision-making aids managers in data-driven processes, identifying patterns, trends, and insights through AI algorithms. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment, enhanced employee experience through AI-driven content, and AI-driven operations management. AI-driven supply chain optimization, process automation, and customer relationship management (CRM) are also explored. However, ethical implications of AI in management include addressing biases and fairness concerns, ensuring transparency and accountability, and navigating privacy and data security challenges. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise, fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement and promoting AI-human synergy. The chapter emphasizes the importance of fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement. AI's integration into management practices has the potential to revolutionize various aspects of organizations, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee productivity. AI-driven tools enable efficient data management, identification of trends, correlations, and actionable insights from complex datasets. They can optimize financial resources, human capital, or physical assets, enhance operational efficiency, and provide personalized customer experiences. AI-driven decision-making aids managers in making informed decisions by leveraging capabilities such as data processing, pattern recognition, real-time insights, and predictive analytics. These capabilities help managers segment customers, analyze market trends, and predict future demand. AI also enhances predictive and prescriptive analytics by providing recommendations for optimal performance. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment. AI-powered tools can streamline the traditional recruitment process by scanning online platforms, screening resumes, and conducting assessments and skill evaluations. AI helps mitigate bias in the hiring process through blind hiring, objective evaluation, and data-driven decisions. However, challenges and ethical considerations include data privacy and security, transparency and explainability, algorithmic bias, and candidate experience. In conclusion, AI's integration into management practices has the potential to revolutionize various areas, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee performance. However, HR professionals must address ethical concerns such as data privacy, transparency, algorithmic bias, and candidate experience to ensure the success and competitiveness of AI-based recruitment. AI can significantly enhance the employee experience by providing personalized learning and development programs, enhancing performance evaluations, building employee engagement strategies, and predicting potential attrition risks. AI-powered tools can assess employees' existing skills, knowledge gaps, and learning preferences, enabling the creation of personalized development plans. AI-driven learning platforms can adjust the difficulty and content of training materials based on individual progress, fostering a culture of continuous learning. AI-based performance evaluations can provide valuable insights to enhance the performance evaluation process, offering real-time tracking and 360-degree feedback analysis. AI-driven employee engagement strategies can bolster employee satisfaction, tailoring benefits, rewards, and recognition programs to individual needs. Predictive attrition analysis and chatbots for employee support can also help retain valuable employees. AI can also optimize supply chain operations by accurately predicting demand and managing inventory. AI-driven systems can dynamically adjust inventory levels based on real-time demand fluctuations and lead times, maintaining optimal resource utilization and minimizing excess inventory. AI-powered logistics and route optimization can revolutionize logistics and transportation management by optimizing routes and enhancing overall efficiency. AI-driven process automation can transform various business operations by identifying suitable processes, analyzing high-volume data processing, streamlining workflow and resource allocation, and addressing workforce concerns and upskilling needs amid automation. By embracing AI in operations management, businesses can achieve unprecedented levels of efficiency, resilience, and responsiveness to market demands. In conclusion, AI can transform the employee experience, drive productivity, and foster a dynamic workforce. However, careful consideration of ethical principles and continuous monitoring of AI systems are essential for responsible implementation. AI can significantly improve customer satisfaction and loyalty through real-time feedback analysis, chat sentiment analysis, and personalized loyalty programs. AI-powered customer support includes chatbots and virtual assistants, which provide instant and round-the-clock assistance. AI platforms, such as Natural Language Processing (NLP), enable chatbots to understand and respond to customer queries in a conversational manner. However, AI integration also presents challenges, such as addressing biases and fairness concerns in AI algorithms, ensuring transparency and accountability in AI systems, and managing privacy and data security challenges. Adhering to data protection regulations and implementing robust data governance frameworks are essential for safeguarding customer and organizational data. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise. This can be achieved through change management and training, promoting a collaborative environment, fostering a learning mindset, upskilling and reskilling initiatives, emphasizing creativity and critical thinking, and mitigating potential job displacement and promoting AI-human synergy. In conclusion, embracing AI in management offers numerous opportunities to enhance organizational efficiency, productivity, and competitiveness. It is crucial for management professionals and corporate leaders to understand and harness the power of AI responsibly. By being proactive in addressing challenges and aligning AI initiatives with organizational values, businesses can leverage futuristic trends in management to thrive in the dynamic and ever-evolving landscape.

Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place. The European Union (EU) is reforming these fields with new legislation (General Data Protection Regulation [GDPR], Cybersecurity Directive, Medical Devices Regulation, In Vitro Diagnostic Medical Device Regulation). This reform is gradual, but it has now made its first impact, with the GDPR and the Cybersecurity Directive having taken effect in May, 2018. As regards the United States (U.S.), the regulatory scene is predominantly controlled by the Food and Drug Administration. This paper considers issues of accountability, both legal and ethical. The processes of medical device decision-making are largely unpredictable, therefore holding the creators accountable for it clearly raises concerns. There is a lot that can be done in order to regulate AI applications. If this is done properly and timely, the potentiality of AI based technology, in radiology as well as in other fields, will be invaluable.Teaching Points• AI applications are medical devices supporting detection/diagnosis, work-flow, cost-effectiveness.• Regulations for safety, privacy protection, and ethical use of sensitive information are needed.• EU and U.S. have different approaches for approving and regulating new medical devices.• EU laws consider cyberattacks, incidents (notification and minimisation), and service continuity.• U.S. laws ask for opt-in data processing and use as well as for clear consumer consent.

The brave new world of artificial intelligence: dawn of a new era

While computer-related failures are known to play a significant role in deaths and injuries involving medical devices reported to the U.S. Food and Drug Administration (FDA),1 there is no similar reporting system that meaningfully captures security-related failures in medical devices.Medical device software must satisfy system properties, including safety, security, reliability, resilience, and robustness, among others. This column focuses on the challenges to satisfying a security property for medical devices: post-market surveillance, integrity and availability, and regulation and standards.Medical devices depend on software for patient care ranging from radiation therapy planning to pharmaceutical compounding to automated diagnosis of disease with mobile medical apps. Meanwhile, the medical community has observed an uptick in reported security vulnerabilities in medical device software—raising doubts of cybersecurity preparedness. It should come as little surprise that security risks in medical devices "could lead to patient harm" as recently explained by the chief scientist at the FDA Center for Devices and Radiological Health.2 Device manufacturers and healthcare providers ought to more carefully and deliberately consider security hazards during the phases from design to use of medical devices.Between years 2006 and 2011, 5,294 recalls and approximately 1.2 million adverse events of medical devices were reported to the FDA's Manufacturer and User Facility Device Experience (MAUDE) database.1 Almost 23% of these recalls were due to computer-related failures, of which approximately 94% presented medium to high risk of severe health consequences (such as serious injury or death) to patients.1 For security incidents on medical devices, no systematic national reporting system exists.3 Yet, individual hospitals know of hundreds of security incidents on medical devices.2For instance, the FDA MAUDE does not capture adverse events such as lack of or impaired availability of function when malware infects a medical device's operating system. FDA's own disclaimer explains that the MAUDE database is qualitative rather than quantitative. MAUDE is incomplete with underreporting and reporting bias.Imagine the reaction of a clinician using a high-risk pregnancy monitor that begins to perform more slowly because of a Conficker infection. Would the clinician report a malware infection? Likely not. Admitting to playing a role in accidentally infecting a medical device-would likely lead to consequences ranging from disciplinary action to loss of reputation. Thus, the actual incidence of security failures leading to healthcare delivery failures may be significantly greater than the available statistics suggest. To have a better understanding of medical device security, the bad-news diode must be shorted. Reporting must be incentivized rather than penalized.If you watch television crime dramas, you may be duped into thinking that hacking of medical devices is the number-one risk for public health today. You would be wrong. The most pressing risks are much less sexy: the unavailability of patient care and the lack of health data integrity. Here, we highlight a few examples that illustrate the consequences of unavailability and lack of integrity.Interventional radiology suites and cardiac catheterization labs contain a number of computer systems to perform time-sensitive cardiac procedures, such as angioplasty, to open blocked arteries for improved outcomes in patients suffering acute heart attacks or strokes.According to The Wall Street Journal,2 a Department of Veterans Affairs (VA) catheterization laboratory in New Jersey was temporarily closed in January 2010. Malware had infected the computer systems. The consequence? Patients do not receive the safe and effective care they deserve when malware causes unavailability of care. The VA has experienced hundreds of malware infections in medical devices such as X-ray machines and lab equipment made by well-known, reputable companies.Conficker was detected on 104 devices at the James A. Haley Veterans Hospital in Tampa.2 The affected devices included an X-ray machine, mammography, and a gamma camera for nuclear medicine studies. Conficker is a relatively old piece of malware with well-known mitigation strategies. Why does old malware persist on medical devices?We observe that one of the cultural challenges to improved cybersecurity and t herefore safety and effectiveness is a lifecycle mismatch. For instance, operating system software with production lifecycles measured in months does not match well with a medical device having production lifecycles measured in years or decades. The equivalent of a transformer for impedance matching does not yet exist for safely connecting these different production cultures.Risks of depending on unsupported software has parallels to depending on a device where parts are no longer manufactured or repaired. Medical devices still rely on the original versions of Windows XP (circa 2001). In October 2012, the Beth Israel Deaconess Medical Center in Boston reported to the NIST Information Security and Privacy Advisory Board that the hospital depends on 664 Windows-based medical devices primarily because of supply chain issues. Of the 664 computers, 600 devices run the original version of Windows XP. There are no Service Pack 1 (SP1) machines, but there are 15 SP2 machines and 1 SP3 machine. One MRI machine still runs Windows 95. Security support for SP1, SP2, and SP3 ended on October 10, 2006, July 13, 2010, and April 14, 2014, respectively. In many cases, a medical device manufacturer does not provide an effective way for hospitals to upgrade to supported versions of operating systems. Today, healthcare providers are told to maintain a secure system from insecure devices.4A medical device infected with malware can stray from its expected behavior. For instance, malware can cause a device to slow down and miss critical interrupts. When this happened on a high-risk pregnancy monitor, healthcare professionals could no longer trust the integrity of the sensor readings and depended on backup methods.5Antivirus software can help mitigate certain cybersecurity risks, but they also introduce their own risks. On April 21, 2010, a third of the hospitals in Rhode Island were forced to "postpone elective surgeries and stop treating patients without traumas in emergency rooms" because an automated antivirus software update had accidentally misclassified a critical Windows DLL as malicious. The problem with antivirus software is that by definition, antivirus software is a postmarket afterthought to make up for design flaws in the device. Antivirus software does not remove the need to incorporate security into the early design of medical devices.According to the FDA mission statement, the agency holds responsibility for protecting public health by assuring the safety, efficacy, and security of medical devices. In June of this year, the FDA issued draft guidance on cybersecurity6 and gave examples of what FDA reviewers would expect to see during premarket review. The draft guidance intentionally does not prescribe any particular approach or technology but instead recommends that manufacturers consider cybersecurity starting at the concept phase of the medical device.The FDA recommends that manufacturers provide:Standards bodies are taking actions to improve medical device cybersecurity. For instance, the Association for the Advancement of Medical Instrumentation (AAMI) recently formed a working group on medical device security that includes engineers from manufacturing and regulators. AAMI has already released standards specific to network-related cybersecurity risks (ANSI/AAMI/IEC-80001). International harmonization of cybersecurity guidance is likely on the horizon, given that phrases such as "security patches" appear in proposals from the International Medical Device Regulators Forum.Modern healthcare delivery depends on medical device software to help patients lead more normal and healthy lives. Medical device security problems are real, but the focus on hacking goes only skin deep. Consequences of diminished integrity and availability caused by untargeted malware include the inability to deliver timely and effective patient care. By addressing security and privacy risks at the concept phase, medical devices can remain safe and effective despite the cybersecurity threats endemic to computing. Security of medical devices is more than just a potential problem on the horizon.This work was supported in part by NFS CNS-1331652 and HHS 90TR0003/01. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of NSF or HHS.

BackgroundThe integration of connected medical devices (MDs) into health care brings benefits but also introduces new, often challenging-to-assess risks related to cybersecurity, which have the potential to harm patients. Current regulations in the European Union and the United States mandate the consideration of these risks in the benefit-risk analysis (BRA) required for MD approval. This important step in the approval process weighs all the defined benefits of a device with its anticipated risks to ensure that the product provides a positive argument for use. However, there is limited guidance on how cybersecurity risks should be systematically evaluated and incorporated into the BRA.ObjectiveThis scoping review aimed to identify current legal frameworks, guidelines, and standards in the United States, Canada, South Korea, Singapore, Australia, the United Kingdom, and the European Union on how cybersecurity risks should be considered in the BRA of MDs.MethodsThis scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A systematic literature search of 10 databases was conducted in two phases on July 3, 2024 and September 30, 2024, including the guidance databases of the Food and Drug Administration, the Medical Device Coordination Group, and other International Medical Device Regulators Forum members; the International Medical Device Regulators Forum database; PubMed; and Scopus. Search terms included “cybersecurity,” “security,” “benefit/risk,” “benefit-risk,” and “risk-benefit.” Additional references were identified via citation searching and expert interviews. Inclusion criteria were met if a document was a guideline or standard in force that provided guidance on the BRA or cybersecurity risks of MDs. Documents were excluded when they were not relevant to MDs, they were limited to a subclass of devices, they were about in vitro diagnostic MDs or investigational devices, and the content of the source was insufficient to undertake a scientific analysis. Data were extracted and analyzed using MAXQDA 2022, and the findings were narratively summarized and visualized in figures and tables.ResultsThe search identified 150 documents, with 34 (22.7%) meeting the inclusion criteria. These 34 documents included 4 (12%) regulations, 5 (15%) standards, 6 (18%) technical reports, and 19 (56%) guidance documents. While cybersecurity risks were acknowledged in most documents, detailed methods for their integration into the BRA were lacking. Some standards and guidelines provided examples of how to consider cybersecurity risks in the BRA, but a comprehensive and standardized approach was lacking.ConclusionsThis review highlights a substantial gap between the recognition of cybersecurity risks in MDs and the guidance on their incorporation into the BRA. Standardized frameworks are needed to provide clear methods for evaluating cybersecurity risks and their impact on the safety and security of MDs.

Advancing pediatric medical device development via non-dilutive NIH SBIR/STTR grant funding

IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.

This article reports on the progress made in addressing pediatric medical device needs through the establishment of the Pediatric Device Consortia Grant Program. Pediatric practitioners should be aware of both the imperative for well-studied devices for children and the existence of recently created resources to help foster the development of such products. This article discusses some of the challenges associated with pediatric device development and describes the implementation of section 305 of the Pediatric Medical Device Safety and Improvement Act of 2007. This statute called for the creation of nonprofit consortia to facilitate the development, production, and distribution of pediatric medical devices. A summary of the accomplishments of the pediatric device consortia is presented. Eleven million dollars have been awarded to 5 consortia since 2009. As of July 2012, they have collectively assisted in the development of 219 pediatric device ideas. The consortia provide innovators with both mentorship and services to help advance proposed pediatric device projects, including assistance with prototyping, identification of potential funding sources, preclinical and clinical trial design, and introductions to potential manufacturers. Currently, 5 federally funded pediatric device consortia exist to help advance the development of potential pediatric devices. These consortia serve as a national resource for those with ideas for medical devices that may advance the health and well-being of children.

RapidAI Review for Stroke Detection What Is the Issue? Stroke is a sudden loss of neurologic function caused by poor or interrupted blood flow within the brain. It is 1 of the leading causes of death and a major cause of disability in Canada. For patients with suspected stroke, prompt evaluation using CT imaging and other tests can help to determine the type of stroke, to assess the severity of damage, and to guide treatment decisions. RapidAI is an artificial intelligence (AI)–enabled software platform that facilitates the viewing, processing, and analysis of CT images to aid clinicians in assessing patients with suspected stroke. Understanding the potential benefits and harms of using RapidAI is important to clarify its role in stroke detection. What Did We Do? We sought to identify, synthesize, and critically appraise literature evaluating the effectiveness, accuracy, and cost-effectiveness of RapidAI for detecting large-vessel occlusion (LVO) (i.e., ischemic stroke) and intracranial hemorrhage (ICH) (i.e., hemorrhagic stroke). We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published up to July 22, 2024. We screened citations for inclusion based on predefined criteria, critically appraised the included studies, narratively summarized the findings, and assessed the certainty of evidence. Our methods were guided by the Scottish Health Technologies Group’s health technology assessment (HTA) framework. We highlighted and reflected on the ethical and equity implications of using RapidAI for stroke detection, found in the clinical literature, integrating these considerations throughout the review. We engaged a patient contributor who had experienced a hemorrhagic stroke, to learn about her experience, perspectives, and priorities. Additionally, we incorporated feedback from clinical and ethics experts, the manufacturer, and other interested parties. What Did We Find? We found 2 cohort studies and 11 diagnostic accuracy studies that assessed the effectiveness and accuracy of RapidAI for detecting stroke. Among these, 3 studies evaluated RapidAI as it is intended to be used in clinical practice (i.e., to complement clinician interpretation of CT images), while the remaining 10 studies assessed RapidAI as a standalone intervention. The patient contributor identified important outcomes for stroke care, including improving speed and accuracy of diagnosis, minimizing the damaging effects of stroke, and reducing mortality rates. She also highlighted ethical considerations regarding the use of AI in health care, such as providing data privacy and equitable access, as well as informing patients about the use of AI technologies in the care pathway. Low-certainty evidence suggests that evaluation of CT angiography images by Rapid LVO combined with clinician interpretation, compared to clinician interpretation alone, may result in clinically important reductions in radiology-report turnaround time in patients with suspected stroke. For detecting ICH, low-certainty evidence suggests that Rapid ICH combined with clinician interpretation, using clinician interpretation as a reference standard, has a sensitivity of 92% (95% confidence interval [CI], 78% to 98%) and a specificity of 100% (95% CI, 98% to 100%). However, estimates of sensitivity and specificity for detecting LVO varied, based on studies using different modules of RapidAI as a standalone intervention, providing only indirect accuracy data. The effects of RapidAI on other time-to-intervention metrics, measures of physical and cognitive function, and response to therapy (e.g., reperfusion rates) were very uncertain. We did not identify any evidence on the effects of RapidAI on many important clinical outcomes, including patient harms, mortality, health-related quality of life, length of hospital stay, or health care resource implications. We did not find any studies on the cost-effectiveness of RapidAI for detecting stroke that met our selection criteria for this review. Ethical and equity considerations related to patient autonomy, privacy, transparency, access, and algorithmic bias have implications across the technology life cycle when using RapidAI for detecting stroke. What Does This Mean? RapidAI has the potential to improve acute stroke care by creating efficiencies in the diagnostic process. However, the impact of RapidAI on many outcomes, including those that are important to patients, is uncertain due to limitations of the available evidence. To improve the certainty of findings, there is a need for evidence from robustly conducted studies at lower risk of bias that enrol diverse patient populations and measure outcomes that are important to patients, with improved reporting. The cost-effectiveness of RapidAI for stroke detection is currently unknown. In addition to the evidence on the effectiveness and accuracy of RapidAI for detecting stroke, decision-makers may wish to reflect on the ethical and equity considerations that arise during the deployment of AI-enabled technologies, such as those related to autonomy, privacy, transparency, and explainability of machine-learning models, and the need for considerations related to equity and access in their design, development, and deployment. AI Implementation Review What Is the Issue? Globally, we are seeing a widespread increase in the interest, development, and use of artificial intelligence (AI)–enabled medical devices. Comprehensive evaluation through health technology assessment (HTA) can ensure that digital health technologies (DHTs), including AI-enabled medical devices, are adequately equipped to balance benefits and harms, while being interoperable and equitably accessible to people living in Canada. In the UK, a checklist called Digital Technology Assessment Criteria (DTAC) is used as an add-on component to HTAs to capture additional considerations for the implementation of DHTs. The 5 core areas of DTAC are clinical safety, data protection, technical security, interoperability, and usability and accessibility. In Canada, we currently do not have a DTAC equivalent that can be used as an add-on to traditional HTA. This implementation review is needed to assist health systems in Canada in preparing for the uptake of AI-enabled medical devices, as these technologies pose new challenges. We assessed whether the safeguards and assessment criteria captured by DTAC and other AI-related resources are in place to inform decision-making around the digital infrastructure elements of implementation. What Did We Do? We conducted an implementation review, using a phased approach, to determine whether DTAC can be applied to the health care context in Canada to inform the implementation of DHTs and to identify any additional implementation considerations specific to the use of AI-enabled medical devices in Canada. We integrated ethics and equity considerations across both phases of the review. In phase 1, we applied DTAC to the health care context in Canada by determining whether we have equivalent or similar measures, strategies, and policies in place to implement DHTs safely. In phase 2, an information specialist searched for literature to identify implementation guidance specific to AI and relevant to Canada to supplement DTAC. One reviewer screened publications for inclusion based on predefined criteria, incorporated relevant information into tables, and summarized the findings narratively. We leveraged patient engagement activities conducted in a concurrent Canada’s Drug Agency review of a specific AI-enabled medical device in stroke detection to learn from a patient contributor with lived experience of a hemorrhagic stroke. We learned about her experience, perspectives, priorities, and thoughts about using AI in clinical decision-making. What Did We Find? With some caveats, we found that many of DTAC’s assessment criteria have equivalent or similar guidance for the health care context in Canada. Some exceptions are derived from the differences in Canada’s current governance and health care structure. Further investigation is required to understand whether certain policies in Canada provide sufficient coverage to fulfill DTAC’s criteria (e.g., clinical safety). We identified several considerations for implementing AI-enabled medical devices, with many having underlying ethical and equity implications. Much of the identified guidance emphasizes implementation considerations that apply to the AI system’s entire life cycle, including the most prevalent consideration: ensuring AI-enabled medical devices are monitored, maintained, and sustainable. Examples of additional considerations include AI data governance and data protection; transparency and explainability; and inclusiveness, equity, and minimization of bias. The patient contributor highlighted several considerations relevant for this review, such as data protection and privacy as well as accessibility and equity. What Does This Mean? We have identified key considerations for AI-enabled medical devices that health care decision-makers may consider for the safe and successful implementation of AI in health care in Canada. While Canada has DTAC-equivalent or similar measures, strategies, or policies in place, we identified a need for a checklist like DTAC that senior decision-makers can use. This checklist could be an adaptation of DTAC and could include additional implementation considerations for AI-enabled medical devices to ensure that these technologies meet the minimum baseline standards set out by DTAC and inform the next steps for the safe and successful implementation of AI-enabled medical devices in Canada. This implementation review for all AI-enabled medical devices is to be used alongside reviews of specific AI technologies, including the concurrent review of RapidAI, and will serve as a foundational report to be tailored for each AI topic and updated with the latest developments in the regulation and other aspects of management of AI in the context of Canada.

Cybersecurity vulnerabilities of cardiac implantable electronic devices: Communication strategies for clinicians—Proceedings of the Heart Rhythm Society's Leadership Summit

Generative artificial intelligence (GenAI) is becoming an important tool in medical product development. A main component of this development includes annotating, summarizing, and extracting key insights from expert interviews to identify clinical pain points and curate device requirements. These tasks are time- and labor-intensive, resulting in increased administrative burden and reduced efficiency. As a result, researchers have developed large language models (LLMs) that can disseminate research and interview findings with reduced workload and improved productivity. This study explores the use of GenAI, specifically GPT-4o, to extract user functional and design requirements from medical professional interviews for the iterative development of an infant heart rate detector for neonatal resuscitation. A total of 29 healthcare practitioners were interviewed using a semistructured interview format. The interviews were recorded and transcribed. GPT-4o was used to extract user insights from the transcripts, and the results were compared with manual interviewer notes. A total of 26 h of interview data were collected. All interviewees validated the clinical need for a modality that enables quick and accurate heart rate (HR) measurement during neonatal resuscitation. A set of user requirements was extracted from the interviews and curated under the themes of ease of use, fast and accurate HR measurement, reusability, display, battery life, start-up time, and cost. Also, quantitative analyses of the interviewee's years of experience, clinical settings, and specialties were conducted. These analyses were conducted using GPT-4o and compared with ground-truth manual annotations to determine the accuracy and reliability of GenAI in content extraction and summarization. Overall, this study explored the user requirements identified through in-depth interviews for the development of a pediatric medical device. It also aimed to demonstrate the potential of GenAI in curating these design requirements, offering a framework for researchers and product designers to explore the use of LLMs in curating user requirements and design specifications for medical devices.

Medical devices are currently at a pivotal stage driven by policy and technological innovation. Traditional device development suffers from lengthy cycles, high costs, and poor alignment with clinical needs, making it difficult to adapt to rapidly evolving healthcare scenarios and high-end diagnostic demands. Breakthroughs in artificial intelligence (AI) technologies, particularly deep learning (DL), machine learning (ML), and medical big data analytics (MBDAT), offer critical solutions to this challenge: deep learning (DL) empowers the development of medical imaging devices, enabling precise lesion identification; machine learning (ML) supports diagnostic aids, intelligent decision support systems, and digital therapeutics, constructing personalized treatment and intervention models while optimizing data processing efficiency for laboratory biochemical analyzers; medical big data analytics drives innovation in drug response prediction devices, enhancing medication precision and safety through multi-source data integration and mining. This paper provides a systematic review of advancements in this field across three dimensions: specific AI applications in medical device R&D, current R&D status and trends enabled by AI, and existing challenges and future directions.