Things to Do in L.A. Tap to enable a layout that focuses on the article. Your morning catch-up: Mayor Lurie has SF feeling better, California's job market is taking a hit and more big stories Why outrage is erupting over Trump plan to exclude nursing from'professional' designation This is read by an automated voice. Please report any issues or inconsistencies here . Trump administration proposes excluding nursing and other fields from "professional" designation, capping graduate student loans. Nursing leaders warn the policy will worsen California's severe nurse shortage by discouraging graduate degrees required for teaching and specialized patient care.

We conducted a prospective analysis of a machine learning algorithm to predict hypoglycemia. The algorithm was trained, validated, and tested using data from 2013 to 2019. The details of the machine learning methods have been published, but in brief we employed multiple supervised machine learning techniques (e.g., extreme gradient boosting) to predict inpatient hypoglycemia and severe hypoglycemia using a wide-range of patient-level data (i.e., features) including medications, labs, nursing notes, comorbid conditions, among others. Our deployed model was an extreme gradient boosting model. The pre-implementation period for the model was Jan 1, 2018, to May 31, 2020, and the model was implemented on the cardiovascular surgery and vascular surgery ward at St. Michael's Hospital of Unity Health January 1, 2021 and evaluated until April 30, 2022.

Whether we're ready or not, artificial intelligence (AI) already plays a role in many health care settings. However, cautiously developing, deploying, and even defining further AI advancements will determine its impact and efficacy in the years ahead, according to a new University of Western Ontario study. Interdisciplinary researchers from family medicine, computer science, and epidemiology have identified key issues regarding the use of AI tools in primary health care by connecting directly with family physicians, nurses, nurse practitioners and digital health stakeholders. Overwhelmingly, the responses show AI could have a positive impact in clinical practice, but many factors must be considered regarding its implementation. "We are ready for AI, but we must be thoughtful about how and when we use it," said Dan Lizotte, an associate professor in computer science and the Schulich School of Medicine & Dentistry and senior author on the study.

As technology and automation rapidly remake a very different future of work, some economists predict that women will benefit the most from the coming disruptions. Although women have no doubt been hardest hit by the COVID-19 economy, in the coming years, women-dominated caring jobs--like nursing, teaching, and providing child and elder care--that aren't easily replaced by machines will be among the fastest-growing occupations and thus more likely to be "future-proof." It's not that many women's jobs won't be automated away. Just as men-dominated mechanical and machine operating jobs are predicted to disappear, so too are women-dominated administrative and clerical jobs. But most of these future-of-work predictions assume women will continue to dominate the care economy. And all because men aren't expected to care.

Because of this healthcare worker shortage, some US hospitals with patient populations dominated by the older, the sicker, and the inactive are even closing their doors. According to analyses conducted by Mercer and the Association of American Medical Colleges, by 2025 – the year the World Economic Forum predicts machines will do half of human workers' tasks, no less – healthcare will face a shortfall of more than 731,000 total jobs – including roles like doctors, surgeons, nursing assistants, nurse practitioners, lab technicians, and home health aides. Talk of robots replacing human doctors misses the point. When it comes down to it, even the most advanced AI tool is extremely limited, knowing a great deal about a very narrow area of knowledge – kind of like if someone could recite every work of Shakespeare from memory but didn't know Shakespeare's first name. Humans, in contrast, know a little bit about many different things – like if a doctor knew how to chop garlic, how to perform heart surgery, and how to write in iambic pentameter.

Novartis announced the next step in reimagining medicine by founding the Novartis AI innovation lab and by selecting Microsoft Corp. as its strategic AI and data-science partner for this effort. The new lab aims to significantly bolster Novartis AI capabilities from research through commercialisation and help accelerate the discovery and development of transformative medicines for patients worldwide. As part of the strategic collaboration announced, Novartis and Microsoft have committed to a multi-year research and development effort. Microsoft and Novartis will also collaborate to develop and apply next-generation AI platforms and processes that support future programs across these two focus areas. The overall investment will include project funding, subject-matter experts, technology, and tools.

A Google research team has recently created an artificial intelligence (AI) system that can detect 26 different skin diseases with the same accuracy as a licensed dermatologist. This deep learning technology evaluates images and metadata, such as self-reported symptoms and demographic information, to generate a ranked list of possible diagnoses just as a trained professional would. The Google team's findings were covered in a paper titled "A deep learning system for differential diagnosis of skin diseases" and in a blog post penned by, Yuan Liu, PhD, Software Engineer and Peggy Bui, MD, Technical Program Manager, Google Health. With nearly 2 billion patients having some form of skin condition globally and many areas lacking dermatologists, patients must often take such concerns up with their primary care physicians. Research has shown that while dermatologists diagnose these skin conditions with accuracies between 77-96%, the general practitioner does so with only 24-70% accuracy.

Skin conditions are among the most common kind of ailment globally, just behind colds, fatigue, and headaches. In fact, it's estimated that 25% of all treatments provided to patients around the world are for skin conditions and that up to 37% of patients seen in the clinic have at least one skin complaint. The enormous case workload and a global shortage of dermatologists have forced sufferers to seek out general practitioners, who tend to be less accurate than specialists when it comes to identifying conditions. This trend motivated researchers at Google to investigate an AI system capable of spotting the most common dermatological disorders seen in primary care. In a paper ("A Deep Learning System for Differential Diagnosis of Skin Diseases") and accompanying blog post, they report that it achieves accuracy across 26 skin conditions when presented with images and metadata about a patient case, and they claim that it's on par with U.S. board-certified dermatologists.

One afternoon in the summer of 2018, Bob Gramling dropped by the small suite that serves as his lab in the basement of the University of Vermont's medical school. There, in a grey lounge chair, an undergrad research assistant named Brigitte Durieux was doing her summer job, earphones plugged into a laptop. Then he saw her tears. Bob doesn't balk at tears. As a palliative care doctor, he has been at thousands of bedsides and had thousands of conversations, often wrenchingly difficult ones, about dying. But in 2007, when his father was dying of Alzheimer's, Bob was struck by his own sensitivity to every word choice of the doctors and nurses, even though he was medically trained. "If we [doctors] are feeling that vulnerable, and we theoretically have access to all the information we would want, it was a reminder to me of how vulnerable people without those types of resources are," he says. He began to do research into how dying patients, family members, and doctors talk in these moments about end of treatment, pain management, and imminent death. Six years later, he received over $1 million from the American Cancer Society to undertake what became the most extensive study of palliative care conversations in the US.

Yes, the robots are definitely coming for the jobs of America's 3.5 million cashiers. Just ask the retail workers who've already been displaced by automated checkout machines. Robots may also be coming for radiologists, whose expertise diagnosing diseases through X-rays and MRIs is facing stiff competition from artificial intelligence. And robots are starting to do some of the work in professions as diverse as chef, office clerk and tractor-trailer operator. For most of us, though, the robot invasion will simply change the tasks we do, not destroy our jobs altogether.