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Design and Development of Medical Electronic Instrumentation fills a gap in the existing medical electronic devices literature by providing background and examples of how medical instrumentation is actually designed and tested. The book includes practical examples and projects, including working schematics, ranging in difficulty from simple biopotential amplifiers to computer-controlled defibrillators. Covering every stage of the development process, the book provides complete coverage of the practical aspects of amplifying, processing, simulating and evoking biopotentials. In addition, two chapters address the issue of safety in the development of electronic medical devices, and providing valuable insider advice.

The evolution of technology enables the design of smarter medical devices. Embedded Sensor Systems play an important role, both in monitoring and diagnostic devices for healthcare. The design and development of Embedded Sensor Systems for medical devices are subjected to standards and regulations that will depend on the intended use of the device as well as the used technology. This article summarizes the challenges to be faced when designing Embedded Sensor Systems for the medical sector. With this aim, it presents the innovation context of the sector, the stages of new medical device development, the technological components that make up an Embedded Sensor System and the regulatory framework that applies to it. Finally, this article highlights the need to define new medical product design and development methodologies that help companies to successfully introduce new technologies in medical devices.

Growing demand for customized pharmaceutics and medical devices makes the impact of additive manufacturing increased rapidly in recent years. The 3D printing has become one of the most revolutionary and powerful tool serving as a technology of precise manufacturing of individually developed dosage forms, tissue engineering and disease modeling. The current achievements include multifunctional drug delivery systems with accelerated release characteristic, adjustable and personalized dosage forms, implants and phantoms corresponding to specific patient anatomy as well as cell-based materials for regenerative medicine. This review summarizes the newest achievements and challenges of additive manufacturing in the field of pharmaceutical and biomedical research that have been published since 2015. Currently developed techniques of 3D printing are briefly described while comprehensive analysis of extrusion-based methods as the most intensively investigated is provided. The issue of printlets attributes, i.e. shape and size is described with regard to personalized dosage forms and medical devices manufacturing. The undeniable benefits of 3D printing are highlighted, however a critical view resulting from the limitations and challenges of the additive manufacturing is also included. The regulatory issue is pointed as well.

The European Union Medical Device Regulations 2017/745 entered into force on May 2021 with changes related to strengthening the clinical evaluation requirements, particularly for high-risk devices. This study investigates how the increased requirements on medical device manufacturers in relation to how clinical evaluation will challenge manufacturers. A quantitative survey study was utilized with responses from 68 senior or functional area subject matter experts working in medical device manufacturing Regulatory or Quality roles. The findings from the study demonstrated that the highest source of reactive Post-Market Surveillance data was customer complaints and proactive data were Post-Market Clinical Follow-Up. In contrast, the top 3 sources for generating clinical evaluation data for legacy devices under the new Medical Device Regulations were Post-Market Surveillance data, Scientific literature reviews, and Post-Market Clinical Follow-Up studies. Manufacturers’ biggest challenge under the new Medical Device Regulations is determining the amount of data needed to generate sufficient clinical evidence, while over 60% of high-risk device manufacturers have outsourced the writing of their clinical evaluation reports. Manufacturers also reported a high investment in clinical evaluation training and highlighted inconsistencies in the requirements for clinical data by different notified bodies. These challenges may lead to a potential shortage of certain medical devices in the E.U. and a delay in access to new devices, negatively impacting patient quality of life (1). This study provides a unique insight into the challenges currently experienced by medical device manufacturers as they transition to the MDR clinical evaluation requirements and the subsequent impact on the continued availability of medical devices in the E.U.

Advances in the medical industry has become a major trend because of the new developments in information technologies. This research offers a novel approach for estimating the smart medical devices (SMDs) selection process in a group decision making (GDM) in a vague decision environment. The complexity of the selected decision criteria for the smart medical devices is a significant feature of this analysis. To simulate these processes, a methodology that combines neutrosophics using bipolar numbers with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under GDM is suggested. Neutrosophics with TOPSIS approach is applied in the decision making process to deal with the vagueness, incomplete data and the uncertainty, considering the decisions criteria in the data collected by the decision makers (DMs). In this research, the stress is placed upon the choosing of sugar analyzing smart medical devices for diabetics’ patients. The main objective is to present the complications of the problem, raising interest among specialists in the healthcare industry and assessing smart medical devices under different evaluation criteria. The problem is formulated as a multi criteria decision type with seven alternatives and seven criteria, and then edited as a multi criteria decision model with seven alternatives and seven criteria. The results of the neutrosophics with TOPSIS model are analyzed, showing that the competence of the acquired results and the rankings are sufficiently stable. The results of the suggested method are also compared with the neutrosophic extensions AHP and MOORA models in order to validate and prove the acquired results. In addition, we used the SPSS program to check the stability of the variations in the rankings by the Spearman coefficient of correlation. The selection methodology is applied on a numerical case, to prove the validity of the suggested approach.

Indian healthcare sector is a fast-growing industry which is expected to reach $280 billion by 2025. Medical devices market in India is one of the top 20 medical device markets in the world. It is currently valued at $5.2 billion and is expected to reach $50 billion by 2025. However, India does not manufacture many devices indigenously and still imports approximately 70% of its medical devices. Manufacturing and monitoring of medical devices are highly regulated activities. In India, there were no specific medical device regulations and devices were regulated under the Drugs and Cosmetics Act, 1940. To fulfill this gap, Central Drug Standard Control Organization released Indian Medical Device Rules, 2017, which are the new regulations for medical devices in India. Keeping pace with the requirements, these were amended as Medical Devices (Amendment) Rules, 2020, which has come into force in April 2020. These rules cover various aspects of device related regulations, including classification, registration, manufacturing and import, labeling, sales, and postmarket requirements, etc. The rules are a positive step and encompass most of the European Union (EU) approval process, which mandates that the devices are safe and performs its intended function. However, with rapid advancements in medical device technology, much is desired in clarity and revamping of the current regulatory system to harmonize standards to be in-line with advanced regulations like EU.

Under the background of the aging population and the improvement of people's quality of life, the demand for household medical devices is expanding, which has huge market potential. However, the recycling of waste household medical devices has become a problem that must be faced by the market expansion. In order to reduce the environmental pollution caused by abandoned household medical devices, based on the dynamic punishment and dynamic subsidy measures adopted by the government, the evolutionary game model between the government and the household medical device enterprises is constructed. The strategic choice of the government and the domestic medical equipment enterprises is studied from the perspective of system dynamics. It is found that when the government adopts static measures, there is no stable equilibrium point in the game between the government and enterprises, while when the government adopts dynamic punishment or subsidies, there is a stable equilibrium point in the evolutionary game. In addition, the government can increase the penalty or reduce the subsidy to promote the probability of household medical device enterprises to choose recycling strategy and reduce environmental pollution.

There is a disparity between low and middle-income countries (LMICs) and high-income countries (HICs) in translating medical device innovations to the market, affecting health care service delivery. Whereas medical technologies developed in HICs face substantial challenges in getting to the bedside, there are at least clear pathways in most of the major markets, such as the UK, the EU, and the USA. Much less is known about the challenges that innovators of medical technologies face in LMICs. The aim of this study was to map out current bottlenecks in medical device innovation in Uganda, a LMIC in Sub-Saharan East Africa. A cross-sectional survey was carried out using a digital questionnaire. Twenty-one individuals completed the questionnaire, with the majority being medical device innovators (n = 12). Only one of these had undertaken all the innovation stages, up to clinical validation. Very few innovators had established companies, and/or acquired intellectual property. It is evident from similar studies that challenges in medical device translations are multidimensional, and hence interdisciplinary collaborations are key to accelerating translation processes, especially for LMICs.

High profile device failures have highlighted the inadequacies of current regulation. Art Sedrakyan and colleagues call for a move to a graduated model of approval and suggest a framework to achieve this goal

Early feasibility studies (EFSs) are small-scale clinical investigations conducted during the early development of medical devices to assess initial safety and performance, especially when bench or in-silico testing is insufficient. While EFSs are well established for hardware devices, their application to digital health technologies (DHTs) including artificial intelligence (AI)-enabled medical devices remains limited. The rapidly evolving regulatory landscape, including the European Union Medical Device Regulation (EU MDR 2017/745) and the phased introduction of the European Union Artificial Intelligence (EU AI) Act, creates additional complexity for DHT developers. Despite the recognized potential of EFSs to support iterative, user-centered innovation, little is known about how European DHT companies and contract research organizations (CROs) perceive and implement EFSs, or what barriers and opportunities exist for broader adoption. This study aimed to explore stakeholder perspectives on the use, barriers, and opportunities of EFSs for DHTs in the European Union, and to generate stakeholder-driven recommendations for a harmonized EU-wide EFS framework. A qualitative descriptive study was conducted using semistructured interviews with representatives from 12 DHT companies and 3 CROs across a range of company sizes, MDR device risk classes, and clinical domains. Participants were recruited through purposive maximum-variation sampling until saturation was reached to capture diverse experiences in regulatory and clinical evidence generation. Interviews, conducted in November 2024 and January 2025, were transcribed and analyzed using thematic analysis, combining deductive and inductive coding. Interviews revealed that while EFSs are valued for providing early human-factor feedback and facilitating iterative design improvements, their current use in DHT development is limited. Key barriers include unclear and hardware-centric regulatory requirements under MDR, fragmented and inconsistent interpretations across EU member states, resource and expertise constraints, and limited dialog with regulatory authorities. The anticipated introduction of the EU AI Act is expected to further increase regulatory complexity, with stakeholders expressing uncertainty about overlapping obligations and the risk of slowed innovation. Some companies, particularly larger or AI-focused ones, have proactively prepared for these changes, while others, especially small and medium-sized enterprises, face significant resource challenges. Several companies reported prioritizing the US Food and Drug Administration pathway due to clearer guidance for DHTs and structured timelines. Stakeholders advocated for a harmonized EU EFS program with DHT-specific guidelines, standardized documentation, predictable timelines, and improved communication channels. Several international models were highlighted as best practices. EFSs remain underused in the EU DHT sector, primarily due to regulatory complexity, fragmentation, and a lack of tailored guidance. A harmonized, DHT-specific EFS framework featuring clearer definitions, standardized processes, and structured dialog between innovators and regulators could accelerate safe, effective, and user-centric digital health innovation. As the MDR and AI Act converge, coordinated regulatory approaches will be critical to balancing innovation, safety, and patient benefit in Europe.