The integration of artificial intelligence (AI) into healthcare systems requires more than just the algorithm itself. A recent study by Boussina et al. evaluated the deep learning sepsis prediction model COMPOSER in two emergency departments at UC San Diego Health. The model showed a 17% relative reduction in in-hospital sepsis mortality and a 10% increase in sepsis bundle compliance. This study highlights the importance of evaluating clinically relevant outcomes, such as mortality reduction, when adopting AI tools. It also emphasizes the need for an ecosystem that supports AI algorithms in clinical settings, including interoperability standards, infrastructure, dashboards, and action plans. Despite the rapid growth of AI in healthcare, few models have progressed beyond retrospective development or validation, creating the "AI chasm." The study by Boussina et al. demonstrates the potential of AI in improving patient outcomes, but challenges remain in translating AI algorithms from in silico environments to real-world clinical settings. Factors such as bias during model development and dataset shifts during prospective validation may contribute to this translational gap. Sepsis, a life-threatening condition, is a key area where AI has been extensively studied. Early detection is crucial for timely treatment. The COMPOSER model, which uses electronic health records to predict sepsis, was implemented in two emergency departments. The study found that the model significantly improved patient outcomes, including reduced mortality and increased sepsis bundle compliance. The study also highlights the importance of an AI ecosystem that includes clinical workflow integration, explainability, and continuous monitoring. The COMPOSER model was embedded into the clinical workflow, with a nurse-facing Best Practice Advisory (BPA) that enhanced communication between nurses and physicians and expedited time-to-antibiotics. The study team implemented robust systems to monitor data quality and model performance, ensuring the sustained effectiveness of the model. However, challenges remain in scaling AI algorithms in healthcare systems, requiring substantial resources, infrastructure, expertise, and clinical endorsement. The study also notes that the benefits of AI algorithms may not always justify the costs of implementation and maintenance. The study underscores the need for health technology assessments to evaluate the costs and benefits of AI algorithms in healthcare. Finally, the study emphasizes the importance of continuous adaptation of AI algorithms to the evolving healthcare landscape.The integration of artificial intelligence (AI) into healthcare systems requires more than just the algorithm itself. A recent study by Boussina et al. evaluated the deep learning sepsis prediction model COMPOSER in two emergency departments at UC San Diego Health. The model showed a 17% relative reduction in in-hospital sepsis mortality and a 10% increase in sepsis bundle compliance. This study highlights the importance of evaluating clinically relevant outcomes, such as mortality reduction, when adopting AI tools. It also emphasizes the need for an ecosystem that supports AI algorithms in clinical settings, including interoperability standards, infrastructure, dashboards, and action plans. Despite the rapid growth of AI in healthcare, few models have progressed beyond retrospective development or validation, creating the "AI chasm." The study by Boussina et al. demonstrates the potential of AI in improving patient outcomes, but challenges remain in translating AI algorithms from in silico environments to real-world clinical settings. Factors such as bias during model development and dataset shifts during prospective validation may contribute to this translational gap. Sepsis, a life-threatening condition, is a key area where AI has been extensively studied. Early detection is crucial for timely treatment. The COMPOSER model, which uses electronic health records to predict sepsis, was implemented in two emergency departments. The study found that the model significantly improved patient outcomes, including reduced mortality and increased sepsis bundle compliance. The study also highlights the importance of an AI ecosystem that includes clinical workflow integration, explainability, and continuous monitoring. The COMPOSER model was embedded into the clinical workflow, with a nurse-facing Best Practice Advisory (BPA) that enhanced communication between nurses and physicians and expedited time-to-antibiotics. The study team implemented robust systems to monitor data quality and model performance, ensuring the sustained effectiveness of the model. However, challenges remain in scaling AI algorithms in healthcare systems, requiring substantial resources, infrastructure, expertise, and clinical endorsement. The study also notes that the benefits of AI algorithms may not always justify the costs of implementation and maintenance. The study underscores the need for health technology assessments to evaluate the costs and benefits of AI algorithms in healthcare. Finally, the study emphasizes the importance of continuous adaptation of AI algorithms to the evolving healthcare landscape.