With interest we read the recent article on methemoglobinemia by Chan et al.[1] Through this letter, we would like to add few additional comments regarding methemoglobinemia and its relevance in medicine practice with regards to food poisoning.

Artificial intelligence (AI) has shown promise for revolutionizing healthcare. This narrative review focuses on the evolving discussion of the utility of AI and clinical informatics in critical care and acute care medicine, specifically focusing on digital twin (DT) technology. The improved computational power and iterative validation of these intelligent tools have enhanced medical education, in silico research, and clinical decision support in critical care settings. Integrating DTs into critical care opens vast opportunities, but simultaneously poses complex challenges, from data safety and privacy concerns to potentially increasing healthcare disparities. In medicine, DTs can significantly improve the efficiency of critical care systems. Stakeholder investment is essential for successful implementation and integration of these technologies.

Artificial intelligence (AI) has shown promise for revolutionizing healthcare. This narrative review focuses on the evolving discussion of the utility of AI and clinical informatics in critical care and acute care medicine, specifically focusing on digital twin (DT) technology. The improved computational power and iterative validation of these intelligent tools have enhanced medical education, in silico research, and clinical decision support in critical care settings. Integrating DTs into critical care opens vast opportunities, but simultaneously poses complex challenges, from data safety and privacy concerns to potentially increasing healthcare disparities. In medicine, DTs can significantly improve the efficiency of critical care systems. Stakeholder investment is essential for successful implementation and integration of these technologies.

Artificial intelligence (AI) has shown promise for revolutionizing healthcare. This narrative review focuses on the evolving discussion of the utility of AI and clinical informatics in critical care and acute care medicine, specifically focusing on digital twin (DT) technology. The improved computational power and iterative validation of these intelligent tools have enhanced medical education, in silico research, and clinical decision support in critical care settings. Integrating DTs into critical care opens vast opportunities, but simultaneously poses complex challenges, from data safety and privacy concerns to potentially increasing healthcare disparities. In medicine, DTs can significantly improve the efficiency of critical care systems. Stakeholder investment is essential for successful implementation and integration of these technologies.

Artificial intelligence (AI) has shown promise for revolutionizing healthcare. This narrative review focuses on the evolving discussion of the utility of AI and clinical informatics in critical care and acute care medicine, specifically focusing on digital twin (DT) technology. The improved computational power and iterative validation of these intelligent tools have enhanced medical education, in silico research, and clinical decision support in critical care settings. Integrating DTs into critical care opens vast opportunities, but simultaneously poses complex challenges, from data safety and privacy concerns to potentially increasing healthcare disparities. In medicine, DTs can significantly improve the efficiency of critical care systems. Stakeholder investment is essential for successful implementation and integration of these technologies.

BACKGROUND We read with great interest the recent article by Badheka et al published in your esteemed journal. Hereby we would like to address few additional points related to childhood retroperitoneal hematoma (RPH).