Allowing natural language processing to pore over disparate data stored in electronic health records, researchers in Canada have shown the AI-based technology can reveal real-world experiences and outcomes of patients with stage III breast cancer.
A Harvard-affiliated academic data science center is partnering with a major manufacturer of portable ultrasound systems to boost the diagnostic powers of point-of-care ultrasound, aka “POCUS,” using AI.
The venture arm of 32-hospital UnityPoint Health is partnering with VIDA Diagnostics, investing $1 million to fine-tune and broaden the use of VIDA’s AI software for flagging signs of trouble in lung images.
It’s not unusual for hospitalized patients to take a sudden turn for the worse. A continuous inspection of electronic medical records by machine-learning algorithms can warn of impending trouble in real time, giving physicians a chance to proactively intervene.
Using a dataset of records from nearly 3 million pediatric patients, South Korean researchers have developed and validated a deep-learning algorithm that can tell emergency doctors which children will need to be admitted to critical-care units.
Hospital inpatients who are likely to turn violent can be identified by algorithmic analysis of routine clinical notes stored in electronic health records, according to a study published in JAMA Network Open July 3.