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The authors emphasize translational and implementation barriers, including inconsistent variables, limited prospective validation, and insufficient explainability. They advocate multicenter consensus for minimal standardized datasets, stronger calibration and uncertainty reporting beyond SHAP, and prospective workflow-integrated evaluations aligned with sepsis bundles. They also call for governance on model drift, subgroup performance reporting, adapted AI reporting checklists, separation of technical metrics from clinical utility, and systematic failure-case risk mitigation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/author-response-from-narrative-to-navigation-a-translational-roadmap-for-ai-enabled-early-sepsis-prediction-comment-on-shanmugam-et-al/448946/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/author-response-from-narrative-to-navigation-a-translational-roadmap-for-ai-enabled-early-sepsis-prediction-comment-on-shanmugam-et-al/448946.png","ImageObject",300,407,{"name":92,"@type":93},"Chumphorn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-08","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this author response letter?","Question",{"text":112,"@type":113},"It responds to comments on the authors’ scoping review about machine learning and deep learning models for early sepsis prediction, expanding the discussion toward translational and implementation challenges.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What dataset approach do the authors recommend?",{"text":117,"@type":113},"They stress the need for low-burden standardized datasets, supported by multicenter consensus building and benchmarking frameworks to enable reproducible algorithm comparisons across sites and countries.",{"name":119,"@type":110,"acceptedAnswer":120},"How should future evaluation and reporting be handled?",{"text":121,"@type":113},"Future work should include prospective, workflow-integrated evaluations in real-time clinical settings, and journals should require separation of algorithmic performance metrics from clinical utility outcomes, along with clearer reporting on failure cases and risk mitigation.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},448946,1790915376,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":24,"preview_page_count":4},2336475401981,"https://ap-avatar.wpscdn.com/avatar/22000c94efd8d5204d?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786935347598174694","LETTER TO THE EDITOR – RESPONSE TO COMMENTS  \nAuthor Response: From Narrative to Navigation: A Translational Roadmap for AI-enabled Early Sepsis Prediction—Comment on Shanmugam et al.  \nHemalatha Shanmugam1, Lavanya Airen2, Saumya Rawat3  \nKeywords: AI safety, AI transparency, Clinical deployment, Sepsis prediction. Indian Journal of Critical Care Medicine (2025): 10.5005/jp-journals-10071-25113  \nDear Editor,  \nWe thank the authors for their insightful comments on our scoping review, “Machine Learning and Deep Learning Models for Early Sepsis Prediction.” Their observations primarily focus on translational and implementation challenges—an important dimension that complements the evidence-mapping objective of our review.  \nScope of Our Review  \nOur review synthesized methodological trends in early sepsis prediction models and identified persistent gaps, such as inconsistent variable sets, limited prospective validation, and inadequate explainability. The translational roadmap presented in the letter aligns with these findings but extends the discussion toward operational deployment, which is indeed the next critical step for the field.  \nMinimal, Standardized Datasets  \nWhile we agree with the need for low-burden standardized datasets, we emphasize the necessity of multicenter consensus building and benchmarking frameworks that allow reproducible comparisons across algorithms. Establishing a national minimum dataset for AI-enabled sepsis prediction could significantly accelerate model evaluation and adoption in Indian ICUs.  \nExplainability and Uncertainty  \nWe concur that explainability and uncertainty quantification remain underdeveloped components in current research. Beyond SHAPbased approaches, journals should increasingly expect robust calibration assessment, transparent risk communication, and clear documentation of model behavior in edge cases, as recommended in recent critiques of XAI in health care.1  \nProspective, Workflow-integrated Evaluation  \nWe agree that retrospective studies alone are insufficient. Future work should mandate prospective, workflow-integrated evaluations where algorithms are tested in real-time clinical settings, aligned with sepsis bundles and EWS pathways. Such studies should clearly differentiate algorithmic performance from clinical impact measures—a distinction highlighted as essential for the translation of ML in health care.2  \nEquity, Governance, and Model Updating  \nWe recognize the importance of addressing model drift, subgroup performance, and governance. Moving forward, journals should require authors to describe:  \n1–3Research Department, PanScience Innovations, New Delhi, India Corresponding Author: Hemalatha Shanmugam, Research Department, PanScience Innovations, New Delhi, India, Phone: +91  \n8883526903, e-mail: [hemalatha@parchaa.com](hemalatha@parchaa.com)  \nHow to cite this article: Shanmugam H, Airen L, Rawat S. Author Response: From Narrative to Navigation: A Translational Roadmap for AI-enabled Early Sepsis Prediction—Comment on Shanmugam et al. Indian J Crit Care Med 2025;29(12):1052–1053 .  \nSource of support: Nil  \nConflict of interest: None  \n• Model updating strategies,  \n• Dataset shift monitoring processes, and  \n• Subgroup-specific performance reporting.  \nThese components are central to safe and equitable deployment of clinical artificial intelligence (AI) tools, as detailed in model drift frameworks and equity analyses.3,4  \nAdditional Recommendations for IJCCM  \nAdoption of Standardized AI Reporting Requirements Indian Journal of Critical Care Medicine (IJCCM) may consider mandating an adapted checklist based on CONSORT-AI and SPIRIT-AI, requiring explicit details on dataset characteristics, preprocessing, feature handling, external validation, drift monitoring, and clinician– AI interaction design.5 Submissions should also clarify how model outputs influence decision-making or patient outcomes.5 This would ensure the completeness and transparency of AI studies ","cbCaicPKxijlRNBG","https://ap.wps.com/l/cbCaicPKxijlRNBG","pdf",181566,"English","# Scope of Our Review\n# Minimal, Standardized Datasets\n## Explainability and Uncertainty\n## Prospective, Workflow-integrated Evaluation\n## Equity, Governance, and Model Updating\n# Additional Recommendations for IJCCM","[{\"question\":\"What is the purpose of this author response letter?\",\"answer\":\"It responds to comments on the authors’ scoping review about machine learning and deep learning models for early sepsis prediction, expanding the discussion toward translational and implementation challenges.\"},{\"question\":\"What dataset approach do the authors recommend?\",\"answer\":\"They stress the need for low-burden standardized datasets, supported by multicenter consensus building and benchmarking frameworks to enable reproducible algorithm comparisons across sites and countries.\"},{\"question\":\"How should future evaluation and reporting be handled?\",\"answer\":\"Future work should include prospective, workflow-integrated evaluations in real-time clinical settings, and journals should require separation of algorithmic performance metrics from clinical utility outcomes, along with clearer reporting on failure cases and risk mitigation.\"}]","Author Response - From Narrative to Navigation - A Translational Roadmap for AI-enabled Early Sepsis Prediction - Comment on Shanmugam et al. | PDF",1790729061]