The large blue warning signs read “Video recording for fall detection and prevention” on the third-floor dementia care unit of the Trousdale, a private-pay senior living community in Silicon Valley where a studio starts from about $7,000 per month.

In late 2019, AI-based fall detection technology from a Bay Area startup, SafelyYou, was installed to monitor its 23 apartments (it is turned on in all but one apartment where the family didn’t consent). A single camera unobtrusively positioned high on each bedroom wall continuously monitors the scene.

If the system, which has been trained on SafelyYou’s ever expanding library of falls, detects a fall, staff are alerted. The footage, which is kept only if an event triggers the system, can then be viewed in the Trousdale’s control room by paramedics to help decide whether someone needs to go to hospital – did they hit their head? – and by designated staff to analyze what changes could prevent the person falling again.

Nearly five years ago, DeepMind, one of Google’s more prolific AI-centered research labs, debuted AlphaFold, an AI system that can accurately predict the structures of many proteins inside the human body. Since then, DeepMind has improved on the system, releasing an updated and more capable version of AlphaFold — AlphaFold 2 — in 2020.

And the lab’s work continues.

Today, DeepMind revealed that the newest release of AlphaFold, the successor to AlphaFold 2, can generate predictions for nearly all molecules in the Protein Data Bank, the world’s largest open access database of biological molecules.

Helen Waters, chief operating officer at the EHR giant, describes the company’s artificial intelligence strategy and shows how provider organizations can benefit today from NLP, LLMs, generative AI and more.

In a study of more than 2,000 chest X-rays, radiologists outperformed AI in accurately identifying the presence and absence of three common lung diseases, according to a study published in Radiology, a journal of the Radiological Society of North America (RSNA).

“Chest radiography is a common diagnostic tool, but significant training and experience is required to interpret exams correctly,” said lead researcher Louis L. Plesner, M.D., resident radiologist and Ph.D. fellow in the Department of Radiology at Herlev and Gentofte Hospital in Copenhagen, Denmark.

While commercially available and FDA-approved AI tools are available to assist radiologists, Dr. Plesner said the clinical use of deep-learning-based AI tools for radiological diagnosis is in its infancy.