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Sep 11 2020 | Open Health News | Posted In: News
Fans of data in health care often speculate about what clinicians and researchers could achieve by reducing friction in data sharing. What if we had easy access to group repositories, expert annotations and labels, robust and consistent metadata, and standards without inconsistencies? Since 2017, the Radiological Society of North America (RSNA) has been displaying a model for such data sharing.
Aug 26 2020 | Stanford HAI News | Posted In: News
Experts across disciplines examine the promise and opportunities in artificial intelligence in the medical sciences during a recent AIMI virtual conference. Artificial intelligence’s remarkable ability to ingest huge amounts of data, make sense of images, and spot patterns that escape even the most-skilled human eye has inspired hope that the technology will transform medicine. Realizing the full potential of this opportunity will require the combined efforts of experts in computer science, medicine, policy, mathematics, ethics and more.
Jun 22 2020 | JAMA Network Open | Posted In: News
Rajpurkar P, Yang J, Dass N, et al. Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open. 2020;3(6):e206653.
Jun 17 2020 | Posted In: News
The AIMI Center in collaboration with Google Cloud, is offering Stanford researchers the opportunity to receive up to $20,000 per year of Google Cloud research credits on any Google Cloud product. This call for proposals aims to stimulate and support research in the field of artificial intelligence in medicine and imaging that distinctively takes advantage of cloud capabilities. Proposals of all sizes will be considered, from initial exploration of cloud computing usability for projects to more advanced-stage projects. Applications are accepted on a rolling basis.
May 18 2020 | Nature Machine Intelligence | Posted In: News
Mukherjee P, Zhou M, Lee E, Schicht A, Balagurunathan Y, Napel S, Gillies R, Wong S, Thieme A, Leung A, Gevaert O. A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. Nat Mach Intell 2, 274–282 (2020).