Evaluating the Presence of Software-as-a-Medical-Device in the Australian Therapeutic Goods Register

Regulatory Frameworks for AI-Enabled Medical Device Software in China: Comparative Analysis and Review of Implications for Global Manufacturer

The China State Council released the new generation artificial intelligence (AI) development plan, outlining China's ambitious aspiration to assume global leadership in AI by the year 2030. This initiative underscores the extensive applicability of AI across diverse domains, including manufacturing, law, and medicine. With China establishing itself as a major producer and consumer of medical devices, there has been a notable increase in software registrations. This study aims to study the proliferation of health care-related software development within China. This work presents an overview of the Chinese regulatory framework for medical device software. The analysis covers both software as a medical device and software in a medical device. A comparative approach is employed to examine the regulations governing medical devices with AI and machine learning in China, the United States, and Europe. The study highlights the significant proliferation of health care-related software development within China, which has led to an increased demand for comprehensive regulatory guidance, particularly for international manufacturers. The comparative analysis reveals distinct regulatory frameworks and requirements across the three regions. This paper provides a useful outline of the current state of regulations for medical software in China and identifies the regulatory challenges posed by the rapid advancements in AI and machine learning technologies. Understanding these challenges is crucial for international manufacturers and stakeholders aiming to navigate the complex regulatory landscape.

A Taxonomy for Health Information Systems

The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems.

Webinar and survey on quality management principles within the Australian and New Zealand ACPSEM Workforce

Healthcare relies upon the accurate and safe delivery of patient care. This is only achievable when systems are developed to ensure high quality, robust outcomes, for instance quality management systems. The concept of quality management can take on a different meaning depending on the context in which it is found. To add complication, the amount of education required for quality management will vary depending on one's exposure to the implementation of quality systems. In part to address these issues, the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) Queensland Branch held a quality management webinar for members and non-members across Australia and New Zealand. The purpose of the webinar was to educate and facilitate discussion regarding the application of quality management principles for the ACPSEM profession. In conjunction, a pre- and post-webinar survey was conducted to gain an insight into existing knowledge and attitudes within the professions governed by the ACPSEM and students undertaking related studies. This paper authored by the webinar speakers reintroduces the quality management principles that were discussed in webinar, exemplifies the importance of quality management skills within the ACPSEM professions and presents the results of the surveys, promoting the need for more educational resources on quality management tools.

The Emerging Field of Medical Regulatory Technology and Data Science

Regulations contain rules setup by (governmental) authorities to control specific aspects of certain industries, which often influences the way companies operate [...]

Using Rule-Based Decision Trees to Digitize Legislation

This article introduces a novel approach to digitize legislation using rule based-decision trees (RBDTs). As regulation is one of the major barriers to innovation, novel methods for helping stakeholders better understand, and conform to, legislation are becoming increasingly important. Newly introduced medical device regulation has resulted in an increased complexity of regulatory strategy for manufacturers, and the pressure on notified body resources to support this process is making this an increasing concern in industry. This paper explores a real-world classification problem that arises for medical device manufacturers when they want to be certified according to the In Vitro Diagnostic Regulation (IVDR). A modification to an existing RBDT algorithm is introduced (RBDT-1C) and a case study demonstrates how this method can be applied. The RBDT-1C algorithm is used to design a decision tree to classify IVD devices according to their risk-based classes: Class A, Class B, Class C and Class D. The applied RBDT-1C algorithm demonstrated accurate classification in-line with published ground-truth data. This approach should enable users to better understand the legislation, has informed policy makers about potential areas for future guidance, and allowed for the identification of errors in the regulations that have already been recognized and amended by the European Commission.

The Complexity of Medical Device Regulations Has Increased, as Assessed through Data-Driven Techniques

Medical device regulations are dynamic, as they need to cover an ever changing landscape. In Europe this has led to a new set of regulations (both for Medical Devices and In Vitro Diagnostics), which replaced the old rules. This study is interested in how the complexity of these medical regulations changed over time and if additional time-based metrics can be associated with any of the complexity metrics. Complexity is defined in terms of readability of the text and it is computed using established linguistic measures, as well as Halstead complexity scores. It was shown that the regulatory complexity of new EU medical device regulations was higher than their predecessors, especially when Halstead complexity measures were considered. The complexity metrics obtained for the new regulations were subsequently associated with the time it took to consider these regulations. Only very weak Pearson’s correlation coefficients were found between the complexity scores and the obtained response times for the new regulations. This could indicate that there are issues with how complexity is perceived by those that need to apply these regulations. Taking the complexity of regulations into account can greatly help with the development of more user friendly regulations. The results from the data-driven methods that are applied in this research indicate that governments could benefit from focusing on making regulations more accessible and utilitarian. This would improve the stakeholder adherence and facilitate effective implementation. This work also highlighted the need to develop more suitable methods to analyse regulatory text to further inform the wider research community.