Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.

Diagnostic errors in health care pose significant risks to patient safety and are disturbingly common. In the emergency department (ED), the chaotic and high-pressure environment increases the likelihood of these errors, as emergency clinicians must make rapid decisions with limited information, often under cognitive overload. Artificial intelligence (AI) offers promising solutions to improve diagnostic errors in three key areas: information gathering, clinical decision support (CDS), and feedback through quality improvement.

Political priority and pathways to scale-up of childhood cancer care in five nations.

Background: Despite increasing global attention to non-communicable diseases (NCDs) and their incorporation into universal health coverage (UHC), the factors that determine whether and how NCDs are prioritized in national health agendas and integrated into health systems remain poorly understood. Childhood cancer is a leading non-communicable cause of death in children aged 0-14 years worldwide. We investigated the political, social, and economic factors that influence health system priority-setting on childhood cancer care in a range of low- and middle-income countries (LMIC).

Interaction of tobacco smoke exposure and ovalbumin-sensitization promotes goblet cell and submucosal gland metaplasia in guinea pigs.

Exposure to irritants such as tobacco smoke (TS) causes acute airway inflammation. Chronic exposure may cause airway remodeling contributing to enhanced airway resistance. We hypothesize that combining airway sensitization and inhalation of irritants enhances the number of mucous producing cells beyond either agent alone.