Using a deep neural network equipped with all patient information relevant to diagnosing adult asthma, researchers in Japan have achieved 98% diagnostic accuracy.
Hoping to encourage interdisciplinary collaboration around Big Data over the next 50 to 100 years, Boston University is preparing to build a 17-story architectural marvel.
Facial recognition technology can be used to monitor sedated patients in intensive care units, alerting healthcare workers when a patient is at risk of accidentally removing a breathing tube or engaging in other risky behavior.
Tempus, a Chicago-based precision medicine company that uses machine learning, genomic sequencing and other AI innovations, has raised $200 million in a series F funding round, the company announced.
AI continues to wow healthcare watchers with sharp guidance on clinical decisionmaking, accurate aids to risk assessment and bankable workflow efficiencies. But healthcare was, is and always will be about “human-to-human relationships, trust and healing.”
Researchers in Texas and Taiwan have collaborated to develop a deep-learning tool that can precisely asses the risk of breast cancer—and with it the need for biopsy—in patients with lesions of questionable concern found in mammograms.
While AI and robotics won’t be replacing physicians any time soon, emerging applications surely will lift efficiency for human practitioners of the healing arts and sciences.
Diagnosing coronary artery disease, the most common type of heart disease in the U.S., can be improved by AI, according to a new, multicenter international study published in The Journal of Nuclear Medicine.
An AI analysis of hospital patients’ satisfaction with the care they received from their nurses has shown two things. One, the technology can guide nurses as they strive to optimize the hospital experience for patients and their families. Two, nurses can help advance AI in healthcare.
