Artificial intelligence (AI) is making inroads in the healthcare industry in various ways. From improving medical diagnoses to finding new cures for diseases, AI is revolutionizing the way healthcare professionals approach their work. One exciting example of AI in healthcare is its application to electrocardiograms (ECGs), which are used to monitor and diagnose heart health. Researchers in northern Alberta, Canada, are utilizing AI to glean more information from ECGs and improve patient care and the healthcare system as a whole. ECGs are a standard test in hospitals, used to check the rhythm and electrical activity of the heart.

Heart Test Laboratories, d/b/a HeartSciences, a medical technology company focused on saving lives by making an ECG (also known as an EKG) a far more valuable screening tool through the use of AI, announced that an independent study utilizing its MyoVista proprietary technology was featured in Advocate Aurora Health's Journal of Patient-Centered Research and Reviews, an open access, peer-reviewed medical journal devoted to advancing patient centered care practices, health outcomes, and patient experiences. The publication concluded that the MyoVista technology ECG-derived machine learning model "provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high-risk of major adverse cardiovascular events (MACE)." "This independent study demonstrates the opportunity that AI-ECG algorithms could bring to improving health outcomes. I believe the solution to unnecessary cardiac deaths will come from low-cost, front-line screening using AI-ECGs. Imagine the day where you can go to your primary care physician and a simple 20-second ECG test shows not only whether you have early-stage heart disease, but also whether you are at high-risk of a major adverse cardiovascular event in the next three years," stated Andrew Simpson, CEO of HeartSciences.

Researchers at Mayo Clinic have used artificial intelligence (AI) to evaluate patients' electrocardiograms (ECGs) in a targeted strategy to screen for atrial fibrillation, a common heart rhythm disorder. Atrial fibrillation is an irregular heartbeat that can lead to blood clots that may travel to the brain and cause a stroke, but it is largely underdiagnosed. In the digitally-enabled, decentralized study, AI identified new cases of atrial fibrillation that would not have come to clinical attention during routine care. Earlier research had already developed an AI algorithm to identify patients with a high likelihood of previously unknown atrial fibrillation. "We believe that atrial fibrillation screening has great potential, but currently the yield is too low and the cost is too high to make widespread screening a reality," says Peter Noseworthy, M.D., a cardiac electrophysiologist at Mayo Clinic and lead author of the study.

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

You are free to share this article under the Attribution 4.0 International license. Patients are 20% less likely to die of sepsis because a new AI system catches symptoms hours earlier than traditional methods, new research shows. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," says Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins University, and lead author of the studies, which evaluated more than a half million patients over two years.

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system, created by a Johns Hopkins researcher whose young nephew died from sepsis, scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and npj Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

Patients are 20% less likely to die of sepsis because a new AI system developed at Johns Hopkins University catches symptoms hours earlier than traditional methods, an extensive hospital study demonstrates. The system scours medical records and clinical notes to identify patients at risk of life-threatening complications. The work, which could significantly cut patient mortality from one of the top causes of hospital deaths worldwide, is published today in Nature Medicine and Nature Digital Medicine. "It is the first instance where AI is implemented at the bedside, used by thousands of providers, and where we're seeing lives saved," said Suchi Saria, founding research director of the Malone Center for Engineering in Healthcare at Johns Hopkins and lead author of the studies, which evaluated more than a half million patients over two years. "This is an extraordinary leap that will save thousands of sepsis patients annually. And the approach is now being applied to improve outcomes in other important problem areas beyond sepsis."

Artificial intelligence and machine learning promise to transform healthcare across the board, but particularly through the use of precision medicine. Precision medicine is often defined differently than the common phrase "personalized medicine," which simply means tailoring treatments to the patient. Precision medicine, on the other hand, specifically applies machine learning to the genetic material of patients with less-common conditions. The AI finds patterns within material to identify common phenotypes, while pharmaceutical companies use that information to develop drugs targeted to the specific need. Palo Alto, California-based Endpoint Health is one player in this space looking to tap the potential machine learning has for precision medicine.

Atrial fibrillation (AF) is associated with significant morbidity but remains underdiagnosed. We hypothesize a deep learning model can identify patients at risk of AF in the 2 weeks following a 24-hour ambulatory ECG with no documented AF. We identified a training set of Holter recordings of 7 to 15 days duration, in which no AF could be found in the first 24 h. We trained a neural network to predict the presence or absence of AF in the 15 following days, using only the first 24 h of the recording. We evaluated the neural network on a testing set and an external dataset not used during algorithm development.

At the American Academy of Orthopaedic Surgeons Annual Meeting, Zimmer Biomet announced the debut of the WalkAI, a model that helps identify patients with a lower gait speed outcome at 90 days after hip or knee surgery. WalkAI, which adds predictive analytic capabilities to Zimmer Biomet's ZBEdge technology, offers surgeons data-driven guidance to identify patients with lagging gait speed during their recovery from total hip arthroplasty or total knee arthroplasty, according to the release. "Using a proprietary, Zimmer Biomet-developed artificial intelligence [AI] algorithm, WalkAI is the orthopedic industry's first and only AI-based model to create daily, personalized predictions and identify patients who may be exceptions to typical recovery curves in an effort to help surgeons mitigate or minimize poor outcomes," Liane Teplitsky, president of Global Robotics and Technology and Data Solutions at Zimmer Biomet, said in the release. "WalkAI is built from our wealth of anonymized ZBEdge data and is the first model to demonstrate our unique capability to deliver actionable predictions by connecting real-world data and AI through ZBEdge products and experiences."