AI not only improves data collection and analysis, it impacts which products are engaged in clinical trials, determines necessary medical criteria, helps design the trials and can even choose the best participating facilities. The result is, organizations that leverage AI will be more successful and will go to market faster than those that don’t.
For life sciences companies seeking to develop, deploy and successfully commercialize digital health products and solutions, the pathway to success can have many twists, turns and roadblocks. This article examines some of the major challenges to bringing digital health products to market and explores potential opportunities to ease the journey.
The FDA CDRH is seeking input from industry and the public on expanding access to home use medical technologies. The comment period closes on August 30, 2023.
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.
Market research and forecasting help ensure that you are investing in a device or drug that is needed and will be well received. In this article, Sanobar Syed, Associate Director of Forecasting, Market Insights and Strategies, BeiGene, highlights strategies to support data collection for forecasting in emerging markets.
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.
Not all use cases are good candidates for machine learning. In this column we look at cases where AI/ML may be appropriate and when building a traditional algorithm to solve a problem is a better choice.
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.
The regulatory landscape for medical devices is rapidly evolving, catapulted most recently by European Union Medical Device Regulations (EU MDR). In this article, Monali Bhansali, Practice Lead of Regulatory Affairs at Tata Elxsi, highlights challenges and advancements in the MedTech regulatory landscape, and what the future has in store.
Regulatory requirements for computer systems validation (CSV) have long been in place, but their compliance requires considerable time and resources. In the life sciences industry, traditional validation processes add to project timelines and costs, affecting time to market and preventing the deployment of newer versions of software. In addition to improving accuracy and coverage, automated CSV processes can create the same artifacts as manual execution and expedite the upgrade timeline.
