Since 2018, Health Canada has undertaken an initiative to adapt its regulatory approach to better support digital health technologies, specifically medical devices. Key focus areas include artificial intelligence, software as a medical device, cybersecurity, medical device interoperability, wireless medical devices, mobile medical apps and telemedicine. To meet this goal, Health Canada established the Digital Health Division under the Medical Devices Bureau and has been increasing its efforts to build in-house expertise. On October 27, 2021, Health Canada, the US Food and Drug Administration (FDA), and the United Kingdom's Medicines and Healthcare Products Regulatory Agency (MHRA) jointly published the Good Machine Learning Practice for Medical Device Development: Guiding Principles. The document consists of 10 guiding principles to help promote safe, effective, and high-quality use of artificial intelligence and machine learning (AI/ML) in medical devices.
The forecasting tool assesses multiple patient-specific biological and clinical factors to predict the degree of response to immune checkpoint inhibitors and survival outcomes. It markedly outperforms individual biomarkers or other combinations of variables developed so far, according to findings published in Nature Biotechnology. With further validation, the tool may help oncologists better identify patients most likely to benefit from ICB. Discerning, prior to treatment, patients for whom ICB would be ineffective could reduce unnecessary expense and exposure to potential side effects. It could also indicate the need to pursue alternate treatment strategies, such as combination therapies. "It's important to know which treatment modalities patients are most suited for," said Dr. Chan, director of Cleveland Clinic's Center for Immunotherapy & Precision Immuno-Oncology.
The Food and Drug Administration recently sought comments on the role of transparency for artificial intelligence and machine learning-enabled medical devices. The FDA invited comments in follow up to a recent workshop on the topic. The workshop was part of a series of efforts the FDA has had in this space. These include its Digital Health Center of Excellence and a five-part Action Plan for AI and machine-learning enabled medical devices. As part of the action plan, the FDA indicated it wants to issue guidance on software learning over time and help the industry be "patient-centered."
From research to diagnosis to treatment, AI has the potential to improve outcomes for some treatments by 30 to 40 percent and reduce costs by up to 50 percent. Although healthcare algorithms are predicted to represent a $42.5B market by 2026, less than 35 algorithms have been approved by the FDA, and only two of those are classified as truly novel.1 Obtaining the large data sets necessary for generalizability, transparency, and reducing bias has historically been difficult and time-consuming, due in large part to regulatory restrictions enacted to protect patient data privacy. That's why the University of California, San Francisco (UCSF) collaborated with Microsoft, Fortanix, and Intel to create BeeKeeperAI. It enables secure collaboration between algorithm owners and data stewards (for example, healthy systems, etc.) in a Zero Trust environment (enabled by Azure Confidential Computing), protecting the algorithm intellectual property (IP) and the data in ways that eliminate the need to de-identify or anonymize Protected Health Information (PHI)--because the data is never visible or exposed. By uncovering powerful insights in vast amounts of information, AI and machine learning can help healthcare providers to improve care, increase efficiency, and reduce costs.
The U.S. Food and Drug Administration recently partnered with Health Canada and the UK's Medicines and Healthcare products Regulatory Agency to issue guiding principles to align efforts and standards for artificial intelligence and machine learning medical device development in health care. "The FDA believes that artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day," said Jim McKinney, public affairs specialist at the FDA, in an email to The Well News. McKinney said the 10 guiding principles grew out of collaborative discussions with Health Canada and MHRA, and learning from several sectors that applied AI and ML technologies for years and have developed good practices that can be readily applied to the medical device industry. Evidence from published information, expert and other public perspectives and review experience was used to develop the guiding principles that will be used by the agency to lay the foundation for the development of Good Machine Learning Practice, which will unify international efforts for medical device development. Over the past decade the FDA has reviewed and authorized a growing number of devices legally marketed with machine learning and expects this trend to continue.
Time has been doing funny things lately. It's already Thanksgiving, and yet that cargo ship got stuck in the Suez canal last spring, not ten years ago, which is what it feels like. Speaking of shipping delays, this is definitely not the year to procrastinate on holiday shopping. But don't worry, we've got you covered. This year has harvested a bountiful Thanksgiving feast of books to devour.
VIDA quantifies imaging biomarkers of lung diseases to provide clear, measurable evidence that accelerates the therapy pipeline and empowers precise diagnoses and treatments to advance lung care. Through quantitative data intelligence and impactful visualizations, VIDA helps physicians manage patients with or at risk of chronic obstructive pulmonary disease (COPD), interstitial lung disease, asthma, emphysema, lung cancer, and COVID-19. VIDA's software is FDA cleared, CE-marked, Health Canada licensed and TGA registered for clinical use in the US, European Economic Area, Canada, and Australia. Follow @vidalung on Twitter and LinkedIn.
While the term "healthcare consumerism" has been used since the 1930s, today the term refers to the importance of creating a more patient or consumer-centered experience. Patients want a more integrated, seamless healthcare experience that focuses on their particular needs. Artificial intelligence (A.I.), big tech, and big data give patients more transparency, more choice, and more flexibility across the healthcare ecosystem, which helps to facilitate a more positive healthcare experience. But the use of A.I. and machine learning to improve the patient experience, particularly and most importantly in treatment outcomes, begins long before the application of telemedicine, online appointment setting, digitalization, access to real-time information and price transparency, all of which are being used within the ecosystem with varying degrees of success. Where does healthcare consumerism really begin?
"The skull acts as a bastion of privacy; the brain is the last private part of ourselves," Australian neurosurgeon Tom Oxley says from New York. Oxley is the CEO of Synchron, a neurotechnology company born in Melbourne that has successfully trialled hi-tech brain implants that allow people to send emails and texts purely by thought. In July this year, it became the first company in the world, ahead of competitors like Elon Musk's Neuralink, to gain approval from the US Food and Drug Administration (FDA) to conduct clinical trials of brain computer interfaces (BCIs) in humans in the US. Synchron has already successfully fed electrodes into paralysed patients' brains via their blood vessels. The electrodes record brain activity and feed the data wirelessly to a computer, where it is interpreted and used as a set of commands, allowing the patients to send emails and texts.