As part of the G7's health track artificial intelligence (AI) governance workstream 2021, member states committed to the creation of 2 deliverables on the subject of governance: These papers are complementary and should therefore be read in combination to gain a more complete picture of the G7's stance on the governance of AI in health. This paper is the result of a concerted effort by G7 nations to contribute to the creation of harmonised principles for the evaluation of AI/ML-enabled medical devices, and the promotion of their effectiveness, performance, safety and ethicality. A total of 3 working group sessions were held to reach consensus on the content of this paper. The rapid emergence of AI/ML-enabled medical devices provides novel challenges to current regulatory and governance systems, which are based on more traditional forms of Software as a Medical Device (SaMD). Regulators, international standards bodies[footnote 2] and health technology assessors across the world are grappling with how they can provide assurance that AI/ML-enabled medical devices are safe, effective and performant – not just under test conditions but in the real world.
Investments in artificial intelligence and machine learning are finally on the rise in healthcare. While the industry has been slow to adopt AI in comparison to other sectors like financial services and manufacturing – with 70% of health systems yet to establish a formal program – a recent survey found that 68% of health system executives plan to invest more in AI in the next five years to help reach their strategic goals. And the investments are expected to be significant; the global AI in healthcare market size is estimated to reach $120.2 billion by 2028. The opportunities for AI in healthcare are widespread, spanning both operational and clinical use cases including fraud prevention, voice-assisted charting, registration, remote patient monitoring and more. AI holds particular promise for connected medical devices and telehealth – an integral part of the Internet of Medical Things (IoMT) – as it enables faster triage, intake, detection and decision making.
Nearly one-third of IoT medical device manfacturers, healthcare organizations, regulators, and users cite identifying and addressing cybersecurity issues as their top concern when dealing with both network-connected medical devices and older legacy equipment, according to a Deloitte survey released today. He notes that all it will take is one major event where a patient is put at risk or dies to change the way IoT medical device-makers and the healthcare industry prioritize IoT cybersecurity spending versus taking a backseat to capital spending and operating budget costs. "The sheer number of connected devices - tens of thousands - makes asset-tracking challenging, complicated by the fact that many IoT devices move around the hospital, jumping from port to port or access point to access point," says Phelan. In order to bring substantial change to cybersecurity of IoT devices in the medical industry, Jones notes it will require collaboration among manufacturers, healthcare providers, and regulators.
Nearly one-third of IoT medical device manfacturers, healthcare organizations, regulators, and users cite identifying and addressing cybersecurity issues as their top concern when dealing with both network-connected medical devices and older legacy equipment, according to a Deloitte survey released today. The survey, which queried more than 370 professionals tied to the IoT medical device industry, also found that 35.6% of these organizations experienced a cybersecurity incident in the past year. The number of attacks via IoT medical devices was not measured in the report. There will be other attacks that address these IoT devices," says Russell Jones, Deloitte Risk and Financial Advisory partner at Deloitte & Touche. He notes that all it will take is one major event where a patient is put at risk or dies to change the way IoT medical device-makers and the healthcare industry prioritize IoT cybersecurity spending versus taking a backseat to capital spending and operating budget costs.
Increasing availability of machine learning (ML) frameworks and tools, as well as their promise to improve solutions to data-driven decision problems, has resulted in popularity of using ML techniques in software systems. However, end-to-end development of ML-enabled systems, as well as their seamless deployment and operations, remain a challenge. One reason is that development and deployment of ML-enabled systems involves three distinct workflows, perspectives, and roles, which include data science, software engineering, and operations. These three distinct perspectives, when misaligned due to incorrect assumptions, cause ML mismatches which can result in failed systems. We conducted an interview and survey study where we collected and validated common types of mismatches that occur in end-to-end development of ML-enabled systems. Our analysis shows that how each role prioritizes the importance of relevant mismatches varies, potentially contributing to these mismatched assumptions. In addition, the mismatch categories we identified can be specified as machine readable descriptors contributing to improved ML-enabled system development. In this paper, we report our findings and their implications for improving end-to-end ML-enabled system development.