The article "Use of Artificial Intelligence in Critical Care: Opportunities and Obstacles" by Michael R. Pinsky et al. discusses the challenges and opportunities of integrating artificial intelligence (AI) into critical care environments. The authors highlight that while AI techniques are prevalent in modern life, their application in acute care medicine has been slow and uneven. Key obstacles include the immaturity of AI systems in situational awareness, biases in large databases, technical barriers to accessing and processing real-time data, and the "black-box" nature of many predictive algorithms, which makes trustworthiness and acceptance difficult. The article also addresses the need for responsible data sharing, regulatory frameworks, and user-centered design to ensure effective and ethical implementation of AI-based clinical decision support systems (CDSS). Despite these challenges, the authors emphasize the potential of AI to improve patient care and operational efficiency, and call for meticulous planning, stakeholder involvement, and continuous monitoring to achieve successful adoption.The article "Use of Artificial Intelligence in Critical Care: Opportunities and Obstacles" by Michael R. Pinsky et al. discusses the challenges and opportunities of integrating artificial intelligence (AI) into critical care environments. The authors highlight that while AI techniques are prevalent in modern life, their application in acute care medicine has been slow and uneven. Key obstacles include the immaturity of AI systems in situational awareness, biases in large databases, technical barriers to accessing and processing real-time data, and the "black-box" nature of many predictive algorithms, which makes trustworthiness and acceptance difficult. The article also addresses the need for responsible data sharing, regulatory frameworks, and user-centered design to ensure effective and ethical implementation of AI-based clinical decision support systems (CDSS). Despite these challenges, the authors emphasize the potential of AI to improve patient care and operational efficiency, and call for meticulous planning, stakeholder involvement, and continuous monitoring to achieve successful adoption.