Radiologists review thousands of images a day. The hope is that artificial intelligence (AI) applications will become useful soon to verify diagnoses, prioritize queued images and even to offer a level of detection and measurement that aren't feasible for humans. One of the latest efforts on this front is by researchers at the University of California at San Francisco (UCSF) and the University of California at Berkeley.
CLEVELAND – What are some of the biggest challenges related using to artificial intelligence (AI) in health care? A panel of experts tackled that question during a session Tuesday during the 2019 Medical Innovations Summit, while also discussing what their organizations have done in this space to advance patient care.
Palo Alto, Calif.-based startup Doc.ai is training its sights on the $9.5 billion global epilepsy market, with the aim of using artificial intelligence to help patients find the best medication to control their seizures. To that end, the company is teaming up with the Stanford University School of Medicine and the Stanford Epilepsy Center on a digital health trial to develop a predictive treatment model that will identify the right treatment at the right time for individuals living with epilepsy.
Mayo Clinic has entered a 10-year partnership with Google "to expand on the more than 200 projects already incorporating artificial intelligence (AI) and machine learning," Mayo Chief Medical Information Officer Steve Peters told BioWorld MedTech. The Rochester, Minn.-based health care organization expects Google's expertise in data science and search technology will help the clinic improve treatment and outcomes by developing machine learning models.
The field of artificial intelligence (AI) in medical practice is in its infancy, but a group of medical societies has published a paper that proposes the development of a code of ethics for artificial intelligence (AI) in radiology. The paper underscores a number of concerns, including that some developers fail to fully appreciate the potential consequences of seemingly innocent slip-ups in the development and validation of that algorithm.
Information technology (IT) has been promising for decades, largely since the advent of electronic medical records (EMR), to improve and streamline health care as it has multiplied productivity in countless other industries. In addition to the long-standing problems with EMRs, more recently there have been early disappointments with the latest iteration of IT focused on artificial intelligence (AI) and machine learning (ML), as big players like IBM Watson and Google have tended to over-promise and under-deliver with algorithms that are poorly matched to the data or the patient need.
TEL AVIV, Israel – Generating all kinds of data that can feed artificial intelligence (AI) and machine learning engines is increasingly cheap and, in many ways, easy but interpreting all that data and translating it into information that is useful to users that range from drug developers to patients remains a significant challenge. Addressing this challenge has blurred the boundaries between traditional technology companies and medical technology companies and forced a rethinking of how treatments are provided, and drugs are developed. And the challenge also creates opportunities for companies that can address this gap.