Blog

By Kwanwoo Jun

  LG Electronics' third-quarter operating profit likely fell 8.4%, but the forecast still beat market consensus thanks to its affiliates' solid performance despite tough business conditions. 

  The South Korean consumer-electronics giant said in a preliminary earnings report Monday that its operating profit will likely come in at 688.90 billion won, equivalent to $481.9 million, for the July-September period, compared with 751.90 billion a year earlier. 

  The projected earnings were above a FactSet-compiled consensus estimate of 618.79 billion won. 

  Revenue is expected to have fallen 1.4% from a year earlier to 21.875 trillion won, LG Electronics said, also beating analysts' estimate in the FactSet survey. 

  Despite challenges from higher U.S. tariffs and a delayed recovery in global demand, its home-appliance segment remained competitive and continued to be the market leader, while its vehicle-component segment achieved record-high profitability, the company said. 

  LG Electronics said that its media and entertainment segment, which includes its television business, faced higher marketing costs amid intensifying global competition. 

  The company, which recently raised $1.3 billion by selling a 15% stake in its Indian unit, LG Electronics India, in an initial public offering, said it expects the proceeds to provide significant funding to accelerate business structure improvements and future growth initiatives. The Indian unit is set to list Tuesday. 

  The company is scheduled to release its full quarterly results later this month. 

  Write to Kwanwoo Jun at kwanwoo.jun@wsj.com 

  (END) Dow Jones Newswires

  October 12, 2025 22:43 ET (02:43 GMT)
Copyright (c) 2025 Dow Jones & Company, Inc.

Artificial intelligence (AI) could soon help anesthesiologists keep children safer in the operating room and improve their recovery with better pain management, suggests a systematic review presented at the ANESTHESIOLOGY^® 2025 annual meeting.

Providing anesthesia care for children is especially challenging because their anatomy can vary dramatically, even among patients of the same age. The researchers found AI performed better than standard methods for determining the appropriate size and placement of breathing tubes, monitoring oxygen levels and assessing postoperative pain. AI consistently: improved the prediction, mitigation and management of complications; enhanced clinical accuracy and decision-making; and allowed anesthesiologists to intervene sooner when complications occurred.

Think of AI as the co-pilot, while the anesthesiologist makes all the final decisions. AI can continuously analyze thousands of data points in real time and learn patterns from past cases, spotting subtle changes sooner and helping tailor decisions to each child’s unique anatomy. However, it does not replace the anesthesiologist’s training and expertise; it simply adds another layer of safety and support.”

Aditya Shah, B.S., lead author of the study and medical student at Central Michigan University College of Medicine, Saginaw

The researchers analyzed 10 studies and found that AI tools were more effective than current screening/analysis methods. Although AI tools for pediatric anesthesia are still in the research stage, their significant benefits make it likely they will be incorporated into practice in the near future, Shah said.

The studies show AI can improve:

• Oxygen level monitoring: Anesthesiologists use monitors to track a child’s oxygen level in the blood, but alarms don’t go off until the levels are already dangerously low. The anesthesiologist must act immediately and only has seconds to prevent serious harm. Researchers trained AI systems to continuously analyze second-by-second data of oxygen levels from anesthesia machines based on more than 13,000 surgeries. The most efficient AI model analyzes the child’s breathing, oxygen and heart data in real time, spotting tiny changes that humans can’t detect. It can warn anesthesiologists up to 60 seconds before the standard alarm system sounds. This gives anesthesiologists an extra minute to adjust the ventilator, clear secretions or fix the airway problem before a child’s oxygen level becomes dangerously low, potentially preventing heart or brain injury. The difference is like putting out a fire as soon as it starts versus being warned when smoke first appears, Shah said.
• Postoperative pain assessment: Pain is challenging to assess in children, who often can’t communicate how they feel. Current methods are about 85%-88% accurate, including the FLACC scale (Face, Legs, Activity, Cry, Consolability), a 0-10 point tool that health care professionals use to assess pain in children based on what they observe, and the Wong-Baker scale, which shows a series of faces from smiling to crying that the child points to. Researchers recorded more than 1,000 pain assessments in 149 toddlers – such as crying, agitation, guarding of the throat and facial expressions – and trained an AI system to recognize which clues were most important for detecting pain. The AI tool measured children’s pain with 95% accuracy.
• Accuracy of breathing tube size and placement: The size of breathing tubes and depth of placement in the throat are critical to avoiding serious complications, including injuring the airway lining and providing inadequate levels of oxygen. Current formulas use the child’s age or height, but children’s anatomy can vary quite a bit. Various studies show AI can make this process more accurate. In a study of 37,000 children, machine-learning models (a type of AI) used patient characteristics to predict breathing tube size and depth far more accurately, reducing errors by 40%-50%.

“AI can offer personalized, real-time decision support to anesthesiologists, potentially reducing complications and outcomes in children, where precision is especially critical,” said Patrick Fakhoury, B.S., co-author and a medical student at Central Michigan University College of Medicine. “For parents, the real value of AI is peace of mind.”