Artificial intelligence (AI) in critical care presents significant opportunities but also major challenges. AI-based clinical decision support systems (CDSS) are evolving and will play a key role in future healthcare. However, their adoption faces obstacles such as data quality, bias in databases, technical barriers, and ethical concerns. AI systems must be transparent, trustworthy, and aligned with clinical workflows to be accepted by healthcare professionals. The integration of AI into critical care requires careful planning, stakeholder involvement, and rigorous validation to ensure safety and effectiveness. Key challenges include ensuring fairness and generalizability of AI models across diverse populations, addressing data privacy and security, and overcoming technical and logistical barriers to data sharing. AI systems must be designed to improve situational awareness and support clinical decision-making without replacing clinicians. Real-world data from electronic health records (EHRs) is valuable for training AI models, but issues such as data bias and dataset shifts can affect model performance. Successful implementation of AI in critical care requires collaboration between stakeholders, including clinicians, researchers, and policymakers. AI tools must be tested rigorously, with emphasis on safety, fairness, and usability. Regulatory frameworks and ethical guidelines are essential to ensure AI is used responsibly and equitably. Training and education for healthcare professionals are also critical to ensure they can effectively use and interpret AI tools. The future of AI in critical care depends on addressing these challenges through interdisciplinary collaboration, continuous improvement, and a commitment to ethical and equitable AI development. AI has the potential to enhance patient care, improve outcomes, and reduce clinical burden, but its success depends on overcoming the barriers to effective implementation and adoption.Artificial intelligence (AI) in critical care presents significant opportunities but also major challenges. AI-based clinical decision support systems (CDSS) are evolving and will play a key role in future healthcare. However, their adoption faces obstacles such as data quality, bias in databases, technical barriers, and ethical concerns. AI systems must be transparent, trustworthy, and aligned with clinical workflows to be accepted by healthcare professionals. The integration of AI into critical care requires careful planning, stakeholder involvement, and rigorous validation to ensure safety and effectiveness. Key challenges include ensuring fairness and generalizability of AI models across diverse populations, addressing data privacy and security, and overcoming technical and logistical barriers to data sharing. AI systems must be designed to improve situational awareness and support clinical decision-making without replacing clinicians. Real-world data from electronic health records (EHRs) is valuable for training AI models, but issues such as data bias and dataset shifts can affect model performance. Successful implementation of AI in critical care requires collaboration between stakeholders, including clinicians, researchers, and policymakers. AI tools must be tested rigorously, with emphasis on safety, fairness, and usability. Regulatory frameworks and ethical guidelines are essential to ensure AI is used responsibly and equitably. Training and education for healthcare professionals are also critical to ensure they can effectively use and interpret AI tools. The future of AI in critical care depends on addressing these challenges through interdisciplinary collaboration, continuous improvement, and a commitment to ethical and equitable AI development. AI has the potential to enhance patient care, improve outcomes, and reduce clinical burden, but its success depends on overcoming the barriers to effective implementation and adoption.