%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54814 %T Challenges and Alternatives to Evaluation Methods and Regulation Approaches for Medical Apps as Mobile Medical Devices: International and Multidisciplinary Focus Group Discussion %A Maaß,Laura %A Hrynyschyn,Robert %A Lange,Martin %A Löwe,Alexandra %A Burdenski,Kathrin %A Butten,Kaley %A Vorberg,Sebastian %A Hachem,Mariam %A Gorga,Aldo %A Grieco,Vittorio %A Restivo,Vincenzo %A Vella,Giuseppe %A Varnfield,Marlien %A Holl,Felix %+ University of Bremen, SOCIUM - Research Center on Inequality and Social Policy, Department of Health, Long Term Care and Pensions, Mary-Somerville-Straße 3, Bremen, 28359, Germany, 49 1719202704, laura.maass@uni-bremen.de %K medical apps %K mobile medical devices %K evaluation methods %K mobile medical device regulation %K focus group study %K alternative approaches %K logic model %K mobile phone %D 2024 %7 30.9.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The rapid proliferation of medical apps has transformed the health care landscape by giving patients and health care providers unprecedented access to personalized health information and services. However, concerns regarding the effectiveness and safety of medical apps have raised questions regarding the efficacy of randomized controlled trials (RCTs) in the evaluation of such apps and as a requirement for their regulation as mobile medical devices. Objective: This study aims to address this issue by investigating alternative methods, apart from RCTs, for evaluating and regulating medical apps. Methods: Using a qualitative approach, a focus group study with 46 international and multidisciplinary public health experts was conducted at the 17th World Congress on Public Health in May 2023 in Rome, Italy. The group was split into 3 subgroups to gather in-depth insights into alternative approaches for evaluating and regulating medical apps. We conducted a policy analysis on the current regulation of medical apps as mobile medical devices for the 4 most represented countries in the workshop: Italy, Germany, Canada, and Australia. We developed a logic model that combines the evaluation and regulation domains on the basis of these findings. Results: The focus group discussions explored the strengths and limitations of the current evaluation and regulation methods and identified potential alternatives that could enhance the quality and safety of medical apps. Although RCTs were only explicitly mentioned in the German regulatory system as one of many options, an analysis of chosen evaluation methods for German apps on prescription pointed toward a “scientific reflex” where RCTs are always the chosen evaluation method. However, this method has substantial limitations when used to evaluate digital interventions such as medical apps. Comparable results were observed during the focus group discussions, where participants expressed similar experiences with their own evaluation approaches. In addition, the participants highlighted numerous alternatives to RCTs. These alternatives can be used at different points during the life cycle of a digital intervention to assess its efficacy and potential harm to users. Conclusions: It is crucial to recognize that unlike analog tools, digital interventions constantly evolve, posing challenges to inflexible evaluation methods such as RCTs. Potential risks include high dropout rates, decreased adherence, and nonsignificant results. However, existing regulations do not explicitly advocate for other evaluation methodologies. Our research highlighted the necessity of overcoming the gap between regulatory demands to demonstrate safety and efficacy of medical apps and evolving scientific practices, ensuring that digital health innovation is evaluated and regulated in a way that considers the unique characteristics of mobile medical devices. %M 39348678 %R 10.2196/54814 %U https://www.jmir.org/2024/1/e54814 %U https://doi.org/10.2196/54814 %U http://www.ncbi.nlm.nih.gov/pubmed/39348678 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e58080 %T Exploring Impediments Imposed by the Medical Device Regulation EU 2017/745 on Software as a Medical Device %A Svempe,Liga %+ Faculty of Social Sciences, Riga Stradins University, Dzirciema 16, Riga, LV1007, Latvia, 371 67409120, liga.svempe@rsu.edu.lv %K software %K artificial intelligence %K medical device regulation %K rights %K digital health %D 2024 %7 5.9.2024 %9 Viewpoint %J JMIR Med Inform %G English %X In light of rapid technological advancements, the health care sector is undergoing significant transformation with the continuous emergence of novel digital solutions. Consequently, regulatory frameworks must continuously adapt to ensure their main goal to protect patients. In 2017, the new Medical Device Regulation (EU) 2017/745 (MDR) came into force, bringing more complex requirements for development, launch, and postmarket surveillance. However, the updated regulation considerably impacts the manufacturers, especially small- and medium-sized enterprises, and consequently, the accessibility of medical devices in the European Union market, as many manufacturers decide to either discontinue their products, postpone the launch of new innovative solutions, or leave the European Union market in favor of other regions such as the United States. This could lead to reduced health care quality and slower industry innovation efforts. Effective policy calibration and collaborative efforts are essential to mitigate these effects and promote ongoing advancements in health care technologies in the European Union market. This paper is a narrative review with the objective of exploring hindering factors to software as a medical device development, launch, and marketing brought by the new regulation. It exclusively focuses on the factors that engender obstacles. Related regulations, directives, and proposals were discussed for comparison and further analysis. %M 39235850 %R 10.2196/58080 %U https://medinform.jmir.org/2024/1/e58080 %U https://doi.org/10.2196/58080 %U http://www.ncbi.nlm.nih.gov/pubmed/39235850 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 2 %N %P e47283 %T Forecasting Artificial Intelligence Trends in Health Care: Systematic International Patent Analysis %A Benjamens,Stan %A Dhunnoo,Pranavsingh %A Görög,Márton %A Mesko,Bertalan %+ The Medical Futurist Institute, Povl Bang-Jensen u 2/B1 4/1, Budapest, 1118, Hungary, 36 703807260, berci@medicalfuturist.com %K artificial intelligence %K patent %K healthcare %K health care %K medical %K forecasting %K future %K AI %K machine learning %K medical device %K open-access %K AI technology %D 2023 %7 26.5.2023 %9 Original Paper %J JMIR AI %G English %X Background: Artificial intelligence (AI)– and machine learning (ML)–based medical devices and algorithms are rapidly changing the medical field. To provide an insight into the trends in AI and ML in health care, we conducted an international patent analysis. Objective: It is pivotal to obtain a clear overview on upcoming AI and MLtrends in health care to provide regulators with a better position to foresee what technologies they will have to create regulations for, which are not yet available on the market. Therefore, in this study, we provide insights and forecasts into the trends in AI and ML in health care by conducting an international patent analysis. Methods: A systematic patent analysis, focusing on AI- and ML-based patents in health care, was performed using the Espacenet database (from January 2012 until July 2022). This database includes patents from the China National Intellectual Property Administration, European Patent Office, Japan Patent Office, Korean Intellectual Property Office, and the United States Patent and Trademark Office. Results: We identified 10,967 patents: 7332 (66.9%) from the China National Intellectual Property Administration, 191 (1.7%) from the European Patent Office, 163 (1.5%) from the Japan Patent Office, 513 (4.7%) from the Korean Intellectual Property Office, and 2768 (25.2%) from the United States Patent and Trademark Office. The number of published patents showed a yearly doubling from 2015 until 2021. Five international companies that had the greatest impact on this increase were Ping An Medical and Healthcare Management Co Ltd with 568 (5.2%) patents, Siemens Healthineers with 273 (2.5%) patents, IBM Corp with 226 (2.1%) patents, Philips Healthcare with 150 (1.4%) patents, and Shanghai United Imaging Healthcare Co Ltd with 144 (1.3%) patents. Conclusions: This international patent analysis showed a linear increase in patents published by the 5 largest patent offices. An open access database with interactive search options was launched for AI- and ML-based patents in health care. %M 10449890 %R 10.2196/47283 %U https://ai.jmir.org/2023/1/e47283 %U https://doi.org/10.2196/47283 %U http://www.ncbi.nlm.nih.gov/pubmed/10449890 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 1 %P e34038 %T Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device %A Carolan,Jane Elizabeth %A McGonigle,John %A Dennis,Andrea %A Lorgelly,Paula %A Banerjee,Amitava %+ Institute of Health Informatics, University College London, Gower Street, London, WC1E 6BT, United Kingdom, 44 07464345635, j.carolan@ucl.ac.uk %K Artificial intelligence %K machine learning %K algorithm %K software %K risk assessment %K informatics %D 2022 %7 27.1.2022 %9 Viewpoint %J JMIR Med Inform %G English %X Artificial intelligence (AI) is a broad discipline that aims to understand and design systems that display properties of intelligence. Machine learning (ML) is a subset of AI that describes how algorithms and models can assist computer systems in progressively improving their performance. In health care, an increasingly common application of AI/ML is software as a medical device (SaMD), which has the intention to diagnose, treat, cure, mitigate, or prevent disease. AI/ML includes either “locked” or “continuous learning” algorithms. Locked algorithms consistently provide the same output for a particular input. Conversely, continuous learning algorithms, in their infancy in terms of SaMD, modify in real-time based on incoming real-world data, without controlled software version releases. This continuous learning has the potential to better handle local population characteristics, but with the risk of reinforcing existing structural biases. Continuous learning algorithms pose the greatest regulatory complexity, requiring seemingly continuous oversight in the form of special controls to ensure ongoing safety and effectiveness. We describe the challenges of continuous learning algorithms, then highlight the new evidence standards and frameworks under development, and discuss the need for stakeholder engagement. The paper concludes with 2 key steps that regulators need to address in order to optimize and realize the benefits of SaMD: first, international standards and guiding principles addressing the uniqueness of SaMD with a continuous learning algorithm are required and second, throughout the product life cycle and appropriate to the SaMD risk classification, there needs to be continuous communication between regulators, developers, and SaMD end users to ensure vigilance and an accurate understanding of the technology. %M 35084352 %R 10.2196/34038 %U https://medinform.jmir.org/2022/1/e34038 %U https://doi.org/10.2196/34038 %U http://www.ncbi.nlm.nih.gov/pubmed/35084352 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 4 %P e20652 %T Tracking the Presence of Software as a Medical Device in US Food and Drug Administration Databases: Retrospective Data Analysis %A Ceross,Aaron %A Bergmann,Jeroen %+ Natural Interaction Lab, Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, United Kingdom, 44 01865 273000, aaron.ceross@eng.ox.ac.uk %K regulation %K software %K medical device %D 2021 %7 3.11.2021 %9 Proposal %J JMIR Biomed Eng %G English %X Background: Software as a medical device (SaMD) has gained the attention of medical device regulatory bodies as the prospects of standalone software for use in diagnositic and therapeutic settings have increased. However, to date, figures related to SaMD have not been made available by regulators, which limits the understanding of how prevalent these devices are and what actions should be taken to regulate them. Objective: The aim of this study is to empirically evaluate the market approvals and clearances related to SaMD and identify adverse incidents related to these devices. Methods: Using databases managed by the US medical device regulator, the US Food and Drug Administration (FDA), we identified the counts of SaMD registered with the FDA since 2016 through the use of product codes, mapped the path SaMD takes toward classification, and recorded adverse events. Results: SaMD does not seem to be registered at a rate dissimilar to that of other medical devices; thus, adverse events for SaMD only comprise a small portion of the total reported number. Conclusions: Although SaMD has been identified in the literature as an area of development, our analysis suggests that this growth has been modest. These devices are overwhelmingly classified as moderate to high risk, and they take a very particular path to that classification. The digital revolution in health care is less pronounced when evidence related to SaMD is considered. In general, the addition of SaMD to the medical device market seems to mimic that of other medical devices. %M 38907384 %R 10.2196/20652 %U https://biomedeng.jmir.org/2021/4/e20652 %U https://doi.org/10.2196/20652 %U http://www.ncbi.nlm.nih.gov/pubmed/38907384