[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290804-105":59,"doc-detail-290804-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","comprehensive-structured-analysis-of-machine-learning-in-safety-models-article","Comprehensive structured analysis of machine learning in safety models - Article","","Machine learning (ML) integration across industries improves operational efficiency and predictive capability, yet rapid adoption introduces serious safety risks requiring rigorous frameworks. This review surveys scholarly studies from Scopus and Web of Science covering 2022–2024, using a PRISMA-based flow and analyzing n=40 primary datasets. Results are grouped into safety and risk management, ML/AI applications for safety, and smart safety technology. Conclusions emphasize continuous monitoring and updating of safety protocols to match the evolving ML landscape.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comprehensive-structured-analysis-of-machine-learning-in-safety-models-article/290804/",{"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/comprehensive-structured-analysis-of-machine-learning-in-safety-models-article/290804.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What method did the review use to structure the study selection and analysis?","Question",{"text":112,"@type":113},"The study used a PRISMA-based flow to search reputable databases and synthesize the final primary data (n=40).","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What main themes did the findings cover?",{"text":117,"@type":113},"The findings were divided into three themes: safety and risk management, ML/AI applications in safety, and smart technology for safety.",{"name":119,"@type":110,"acceptedAnswer":120},"Why does the conclusion emphasize continuous monitoring and updates?",{"text":121,"@type":113},"Because safety protocols must keep pace with the rapidly changing ML landscape, maintaining reliability and preventing emerging safety issues.","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},290804,1789642595,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":41,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Comprehensive structured analysis of machine learning in  \nsafety models  \nMohd Shukri Abdul Wahab1, Syed Tarmizi Syed Shazali1, Noor Hisyam Noor Mohamed1,  \nAbdul Rani Achmed Abdullah2  \n1Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Malaysia Sarawak, Samarahan,  \nMalaysia  \n2CWorks Technologies Sdn. Bhd., Bandar Kinrara, Malaysia  \nArticle history:  \nReceived Dec 15, 2024 Revised May 3, 2025 Accepted May 10, 2025  \nKeywords:  \nMachine learning PRISMA framework Risk management Safety  \nSmart technology  \nCorresponding Author:  \nMachine learning (ML) integration into various industries has revolutionized operations recently, enhancing efficiency and predictive capabilities. However, the rapid adoption of ML models also presents significant safety concerns that are highly demanded. To achieve this, scholarly articles from reputable databases such as Scopus and Web of Science (WoS) focus on studies published between 2022 and 2024, which were extensively searched. The study's flow is based on the PRISMA framework. The database found (n=40) that the final primary data was analyzed. The findings were divided into three themes: i) safety and risk management, ii) ML and artificial intelligence (AI) applications in safety, and iii) smart technology for safety. The conclusion highlights the need for continuous monitoring and updating of the safety protocols to keep in step with the growing ML landscape. This review contributes to the understanding of ML safety. It offers global lessons that can guide future research and policy-making efforts to ensure ML technologies' safe and ethical use.  \nThis is an open access article under the CC BY-SA license.  \nSyed Tarmizi Syed Shazali  \nDepartment of Mechanical and Manufacturing Engineering, Faculty of Engineering Universiti Malaysia Sarawak  \nJalan Datuk Mohd Musa, 94300 Kota Samarahan, Sarawak, Malaysia  \nEmail: [starmizi@unimas.my](starmizi@unimas.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn the rapidly evolving technological landscape, machine learning (ML) has emerged as a transformative force across diverse sectors, including healthcare, finance, transportation, and entertainment [1]–[3] . Its ability to analyze vast datasets, identify patterns, and make data-driven predictions has revolutionized decision-making processes and operational efficiency. However, integrating ML into critical systems also introduces substantial risks, necessitating a robust safety framework to ensure reliable, ethical, and secure operations. The importance of safety in ML applications cannot be overstated, as system errors, biases, or vulnerabilities can have severe consequences. For example, erroneous ML predictions in healthcare may lead to misdiagnoses, impacting patient outcomes. Similarly, flawed algorithms in financial markets can result in substantial losses, while safety failures in autonomous vehicles pose life-threatening risks.  \nDespite its advantages, ML systems face inherent challenges due to their complexity and reliance on data-driven learning rather than explicit programming. The quality and representativeness of training data are critical; biased or incomplete datasets can lead to skewed predictions, perpetuating social inequalities or producing unreliable outcomes [4]–[6] . Furthermore, the opacity of many advanced ML models, commonly called the \"black box\" problem, limits transparency and interpretability, undermining trust and  \naccountability. This lack of clarity makes it difficult to identify the factors influencing model decisions, posing challenges for developers and end-users. Consequently, ensuring the safety of ML systems requires addressing these issues while maintaining performance and reliability.  \nThis study proposes a comprehensive safety model to address the challenges of integrating ML into critical systems. The model emphasizes key components essential for mitigating risks and enhancing system reliability. First, it advoca","cbCainalSE8eFvOR","https://ap.wps.com/l/cbCainalSE8eFvOR","pdf",373788,12,"English","# Article Info ABSTRACT\n## 1. INTRODUCTION\n## 2. LITERATURE REVIEW","[{\"question\":\"What method did the review use to structure the study selection and analysis?\",\"answer\":\"The study used a PRISMA-based flow to search reputable databases and synthesize the final primary data (n=40).\"},{\"question\":\"What main themes did the findings cover?\",\"answer\":\"The findings were divided into three themes: safety and risk management, ML/AI applications in safety, and smart technology for safety.\"},{\"question\":\"Why does the conclusion emphasize continuous monitoring and updates?\",\"answer\":\"Because safety protocols must keep pace with the rapidly changing ML landscape, maintaining reliability and preventing emerging safety issues.\"}]","Comprehensive structured analysis of machine learning in safety models - Article | PDF"]