Knowingly or unknowingly, Microsoft kicked off a race to integrate generative AI into search engines when it introduced Bing AI in February. Google seemingly rushed into an announcement just a day before Microsoft's launch event, telling the world its generative AI chatbot would be called Bard. Since then, Google has opened up access to its ChatGPT and Bing AI rival, but while Microsoft's offering has been embedded into its search and browser products, Bard remains a separate chatbot. That doesn't mean Google hasn't been busy with generative AI. It's infused basically all of its products with the stuff, while leaving Search largely untouched.

Lower back pain (LBP) recently became a severe and common problem for most office workers. The majority of people, including office workers and students with poor sitting postures whilst working, are experiencing lower back pain, which causes difficulty in daily moving and other inconveniences. There are many treatments dealing with lower back pain, but most treatments includes ergonomic equipment, so physiotherapy can only offer light relief, rather than solving the problem at the source. The aim of this project is to develop a novel way to designing a dynamic siting posture recognition and correction system. This system can identify people's sitting posture and provide its real time information to patients, letting them realise what posture they are acting now, aiming to re-build the awareness of their muscle and spatial position.

Researchers from the Icahn School of Medicine at Mount Sinai developed an artificial intelligence model that can scan physicians' notes and distinguish between acute and chronic lower back pain, according to findings published in the Journal of Medical Internet Research. "Several studies have documented increases in medication prescriptions and visits to physicians, physical therapists, and chiropractors for lower back pain episodes," Ismail Nabeel, MD, MPH, associate professor of environmental medicine and public health at the Icahn School of Medicine at Mount Sinai, said in a press release. "This study is important because artificial intelligence can potentially more accurately distinguish whether the pain is acute or chronic, which would determine whether a patient should return to normal activities quickly or rest and schedule follow-up visits with a physician." "This study also has implications for diagnosis, treatment and billing purposes in other musculoskeletal conditions, such as the knee, elbow, and shoulder pain, where the medical codes also do not differentiate by pain level and acuity," he added. To examine the feasibility of a system that automatically distinguishes acute lower back pain based on free-text clinical notes, Nabeel and colleagues used a dataset of 17,409 clinical notes from various primary care practices in the Mount Sinai Health System.

About 80 percent of adults experience lower back pain in their lifetime; it is the most common cause of job-related disability. Many argue that prescribing opioids for lower back pain contributed to the opioid crisis; thus, determining the quality of lower back pain in clinical practice could provide an effective tool not only to improve the management of lower back pain but also to curb unnecessary opioid prescriptions. Acute and chronic lower back pain are different conditions with different treatments. However, they are coded in electronic health records with the same code and can be differentiated only by retrospective reviews of the patient's chart, which includes the review of clinical notes. The single code for two different conditions prevents appropriate billing and therapy recommendations, including different return-to-work scenarios.