What You Should Know:
– A team of experts from leading U.S. medical centers and research institutions is utilizing NVIDIA-powered federated learning to investigate the impact of this technique and AI-assisted annotation in training AI…
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Most clinical information is encoded as free text, not accessible for quantitative analysis. This study presents an open-source pipeline using the local large language model (LLM) “Llama 2” to extract quantitative information from clinical…
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The previous article in this series explained how Real Time Medical Systems, Inc. raised the ire of PointClickCare Technologies, Inc. and was prevented from using bots and screen scraping to get access to patient…
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Despite our focus on security and privacy, cyber threats are continuously increasing in both number and severity. In a much less fun way, cyber threats are on their own Eras Tour. Instead of choreography…
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Top News
Revenue cycle automation vendor Candid Health raises $29 million in a Series B funding round.
Co-founder and CEO Nick Perry, who came from Palantir Technologies, earned an MS in biomedical informatics from Stanford. …
Krista Cataldo, Business Analytics and Insights Lead at Primacy
Dan Campagna, Account Director at Primacy
Implementing web analytics and data collection in healthcare has always required extra caution. By its very nature, collecting a patient’s (or…
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The mass rollout of AI-powered genomic health prediction (AIGHP) could pose significant risks concerning privacy and discrimination, a report by the Ada Lovelace Institute and the Nuffield Council on Bioethics (NCOB) has warned.
AIGHP refers…
There are a lot of components to consider and talk about in regards to interoperability and data sharing. The inventive ideas and possibilities of where it can go and what it could do, the…
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In pandemic mitigation, strategies such as social distancing and mask-wearing are vital to prevent disease resurgence. Yet, monitoring adherence is challenging, as individuals might be reluctant to share behavioral data with public health authorities….
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Distributed collaborative learning is a promising approach for building predictive models for privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model without sharing their original data. However, concealed systematic biases can…
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