The US Food and Drug Administration has proposed a framework on how it might regulate medical devices that rely on AI and machine learning algorithms. The report published this week outlines two types of algorithms for the purposes of regulation: "locked algorithms" and "adaptive algorithms." Locked algorithms provide the same result each time they're fed the same input. The answers are normally based on things like look-up tables, decision trees, or classifiers. An adaptive algorithm, however, will "change its behavior using a defined learning process."
The US Food and Drug Administration has announced that it is preparing to regulate AI systems that can update and improve themselves as they gorge on more training data. The announcement: The agency released a white paper proposing a regulatory framework to decide how medical products that use AI should seek approval before they can go on the market. It is the biggest step the FDA has taken to date toward formalizing oversight of products that use machine learning (ML). The challenge: Machine-learning systems are tricky to regulate because they can continuously update and improve their performance through new training data. In instances where the FDA has approved ML-based medical software before, it has required the algorithms to be "frozen" before commercial deployment and to go through a reapproval process when they are changed.
The Centers for Medicare and Medicaid Services has launched a new contest it hopes will speed the development of new artificial intelligence technologies that can better predict health outcomes and boost quality of care. WHY IT MATTERS CMS says the Artificial Intelligence Health Outcomes Challenge – announced by the agency on Wednesday, in partnership with American Academy of Family Physicians and the Laura and John Arnold Foundation – seeks to uncover and "unleash" new and innovative tools to help with the push toward value-based care. To do that, CMS is calling on developers from all industries to create new predictive AI applications to help providers participating in CMS Innovation Center models to deliver better care and make quality measures more impactful. "The Artificial Intelligence Health Outcomes Challenge is a three stage competition that will begin with the Launch Stage, in which participants will submit an application at ai.cms.gov," "Up to 20 participants will be selected to participate in Stage 1 of the Challenge. We anticipate that more information about Stage 1 and Stage 2 will be announced later this year."
In medicine, diseases can be detected at a much earlier stage, and we can support the elderly to live a more independent life, simply by identifying deviations from their usual behaviour and body movements. The UK Government recently announced that AI could help the National Health Service predict those in an early stage of cancer, to ultimately prevent thousands of cancer-related deaths by 2033. The algorithms will examine medical records, habits and genetic information pooled from health charities, the NHS and AI. Virtual nurses could transform patient care, being available round the clock to answer questions, monitor patients and provide quick answers. Beyond healthcare, AI could inform a better allocation of resources in energy, logistics and transport, as well as support the digital advertising industry with more efficient marketing.
Germany and France are two flourishing AI hubs, but the UK has been named the European AI "powerhouse" in a new report from London-based investment firm MMC Ventures. Published earlier this month, the analysis sheds light on the hype around the technology, but identifies healthcare as an area of increasing focus for AI entrepreneurs as systems start to embrace emerging technologies. Out of around 1,600 European early stage AI software companies, 21 percent are said to be focusing on health and wellbeing, with the UK dubbed the "heartland of European healthcare AI". This is due to a variety of factors, the research suggests, from the UK having universities that are world-renowned for medicine and teaching hospitals, to what is described as the "flywheel" effect of companies like DeepMind, Babylon or Benevolent AI, which are "simulating, attracting and recycling talent, capital and commercial engagement in the UK ecosystem". And although engaging with the NHS is still perceived to be "challenging", the analysis indicates that early stage companies can now benefit from a wider range of "accessible deployment opportunities".
To assess the financial benefits of such a forward-thinking scheme, researchers at Aalto University's HEMA Institute (the Institute of Healthcare Engineering, Management and Architecture) in Helsinki, Finland, studied whether there is a link between the treatment costs of patients and the use of an AI-based healthcare system that directs patients to the correct care. The study examined the Klinik Pro service during its first five months of use at the Myyrmäki Health Center in Vantaa and the result was that the tool brought a 14% saving in the average service costs per patient, translating to a €31 cost reduction per patient during the period of study.
Australia's National Disability Insurance Scheme (NDIS) will be getting a chatbot, after the Joint Standing Committee on the NDIS flagged that having such technology in place would reduce the frequency of calls and improve "interactive problem solving". Following the committee's report on the NDIS ICT Systems [PDF], the federal government on Thursday agreed to implement all six IT-related recommendations. "While the NDIS is designed to assist people with disability to achieve their goals while exercising choice and control, it is acknowledged a number of challenges relating to ICT remains and requires ongoing work," the government wrote in its response [PDF]. Specifically, the joint committee asked that the National Disability Insurance Agency (NDIA) co-design, with end-users, a "fit-for-purpose chatbot for the website and portals". "The committee believes that the absence of a systems-based tool, which would integrate NDIS business processes, policies, and guidance to staff via a central repository is contributing to the current inability of [the NDIS Contact Centre], NDIA, and LAC staff to provide consistent and clear information to prospective and existing participants and service providers," it said.
Partnership between Joint Centre for Bioethics and AMS Healthcare to shape the future of artificial intelligence in Canada's health system A new partnership with AMS Healthcare is supporting the University of Toronto Joint Centre for Bioethics (JCB) accelerate knowledge and inform practice on ethical artificial intelligence (AI) in health care. "We are thrilled to partner with AMS Healthcare in exploring how AI may be a force for good to improve health and health care, particularly from the perspective of patients and providers," said Jennifer Gibson, JCB Director, based at the Dalla Lana School of Public Health. The gift is supporting JCB's AI and the Future of Caring initiative, one of four priority themes in the JCB's Ethics and AI for Good Health strategy. The other three priority areas are: Public Trust of AI for Health; Ethical Governance of AI for Health; and Equity and the Digital Divide. AI and related digital health technologies hold promise for promoting healthy behaviours, enabling prevention, diagnosis, and treatment of disease, and addressing health equity gaps in health policy and planning.
Health providers and artificial-intelligence software companies are developing tools that could help pathologists better spot diseases, prioritize critical cases and improve patient outcomes, according to a new research report from Frost & Sullivan. The Food and Drug Administration has yet to approve machine-learning pathology tools for use in patient care, but many pathologists say the day isn't far off. The FDA could put its stamp of approval on the technology in the next few years, said Dr. Liron Pantanowitz, vice...
George Bernard Shaw famously quipped that the US and the UK were two countries separated by a common language. But in natural language processing, State-side hospitals are finding a means of better assesing the appropriateness of specific tests or procedures – and Dan Kazzaz argues the NHS could valuably follow suit. Comparing the US healthcare system to that of the UK is often great sport. Points of comparison are frequently the cost of care, quality of care and waiting times. Less obvious is that the US and UK systems actually have a great deal in common, primarily how healthcare is funded.