Inspecting for quality after a process is completed is reactive and outdated. Instead, the future lies in predicting quality and quality issues. For medical device manufacturers, the advantages in predictive quality are so great they simply cannot be ignored.
The reality of biased data is becoming all too clear, which raises important questions for clinicians as well as device and drug developers. In a world full of biased data what are the most ethical practices to achieve equitable health care?
Artificial intelligence has numerous practical applications in diagnostic imaging; the key to making them work for clinicians and patients lies in developing and embracing integrated workflow networks.
Peter O’Blenis, CEO of Evidence Partners, discusses the growing role—and challenges –of literature reviews in the medtech market.
Remote care in the home relies both on the quality of patient monitoring and on the insights provided to the care team. There is a real danger that data overload and alert fatigue will undermine otherwise well-designed remote patient monitoring (RPM) and Hospital at Home programs. The software platform and algorithms tasked with integrating and evaluating data must identify the data that matters, when it matters.
Double-digit growth of the company’s literature review software platform, DistillerSR, spurred $20 million in funding from the growth investment consortium led by Thomvest Ventures.
The partnership brings together Evidence Partners’ literature review platform, DistillerSR, and Akra Team’s strategic regulatory services.
AI can support device innovation and surgical training, but it requires data and collaboration.
Combining imaging technology with artificial intelligence can help address both challenges in healthcare disparities as well as in patient care.
How can AI become a beneficial medical device manufacturing technology? This article reviews some examples of how it excels.
