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.
Digital transformation requires a clear vision, buy-in at every level, and significant investment. Here are three steps to streamline the process.
Anthony Fernando, CEO and president of Asensus Surgical, discusses the potential benefits of augmented intelligence in robotic surgery for both patients and physicians.
As data, rather than document-based dossiers, become the focus for regulatory processes, regulatory affairs managers need to consider whether team skill sets need to be refreshed to reflect new ways of working.
On Tuesday, October 4, the White House released a Blueprint for an Artificial Intelligence (AI) Bill of Rights geared toward protecting the American public as the use of AI and machine learning expands throughout industry and online.
Inspecting for quality after a process is completed is reactive and outdated.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.
