Leveraging AI & ML in MedTech: Power Consumption and Latency
When considering use cases and data classification methods for machine learning, image quality, power consumption and latency must be considered.
Tag Archives: machine learning
Data-driven Surgery: The Next Step in Improving Care and Operational Efficiency
Large sets of data are collected throughout the surgical continuum, but are chief medical officers and perioperative leaders able to use that data to drive clinical, operational, and financial improvements? Embracing data-driven surgery can help HCOs make use of their data to improve care, reduce costs and better manage staffing and workflow.
Advancing Digital Health: Huma SaMD Platform Gains U.S. Clearance
This week Huma Therapeutics received FDA Class II 510(k) clearance for its Software as a Medical Device (SaMD) platform, potentially speeding approval of a variety of AI and machine-learning (ML)-powered digital health devices. We spoke with Kaushik Gune, U.S. Head of Healthcare at Huma, about the current state of digital health technologies, the value of partnerships to enhance the use of real world data and how digital health is likely to advance in the coming years.
AAMI and BSI Partner to Publish Guidance on Artificial Intelligence
AAMI and the British Standards Institute (BSI) have jointly published new guidance documents on performing risk management for machine learning or artificial intelligence incorporating medical devices.
Leveraging AI & ML in MedTech: Purpose-Built Applications
A Venture Capitalist recently joked that to fund a startup, all one must do is choose a URL that ends in ‘.ai’. Although he was not serious, it was an acknowledgment that companies pursuing AI are getting much attention, and there is a fear of missing out (FOMO) in the investment community if one of…
FDA Offers Funding for Adoption of Advanced Manufacturing Technologies
Medtech companies can receive up to $300,000 for the adoption of advanced manufacturing technologies through the MDIC Advanced Manufacturing Clearing House. The program is designed to speed adoption and provide guidance for industry and the FDA on the most beneficial applications of advanced technologies.
FDA Releases Two Discussion Papers on Artificial Intelligence and Machine Learning in Drug Development and Manufacturing
“Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products” and “Artificial Intelligence in Drug Manufacturing” were developed to support the use of AI/ML while addressing concerns related to security, bias and risk, and spur feedback and discussion from stakeholders.
3M Health To Collaborate with Amazon To Accelerate AI in Clinical Documentation
“The innovation, security and reliability of AWS helps us accelerate the delivery of high-quality clinical documentation. Our overarching goal is to create a better, more sustainable solution and to continue to be a trusted partner that our clients can rely on to reduce administrative tasks and prioritize patient engagement.”
FDA Issues Draft Guidance on Predetermined Change Control Plans for AI/Machine Learning-Enabled Medical Devices
The CDRH notes that the goal of the guidance is to put safe and effective advancements in the hands of healthcare providers and users more quickly to help increase the pace of medical device innovation in the U.S. and enable more personalized medicine.
Instilling Trust in AI
Rama Chellappa, PhD, John Hopkins University Bloomberg Distinguished Professor in electrical, computer, and biomedical engineering, and co-author of “Can We Trust AI?” looks at the promise of AI in health care and how we can best utilize this extraordinary tool to save lives and improve health equity.
