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The article links Tanzania’s demographic transition and rising cardiovascular disease burden to gaps in hypertension awareness and continuous treatment. It reviews AI’s preventive promise while addressing barriers such as digital exclusion, limited digital and health literacy, and restricted access to ongoing care. A descriptive, experience-informed framework is proposed to guide responsible, context-specific adoption.",{"@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/ethical-and-logistical-imperatives-for-ai-driven-cardiovascular-risk-prediction-among-older-adults-in-tanzania-framing-a-digital-health-agenda-for-low-income-settings/411109/",{"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/ethical-and-logistical-imperatives-for-ai-driven-cardiovascular-risk-prediction-among-older-adults-in-tanzania-framing-a-digital-health-agenda-for-low-income-settings/411109.png","ImageObject",300,407,{"name":92,"@type":93},"Putri","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-28",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 problem does the article address regarding cardiovascular risk prediction in Tanzania?","Question",{"text":112,"@type":113},"The article addresses the urgent need for preventive risk prediction because Tanzania faces a severe, growing cardiovascular disease burden among older adults, alongside low awareness of hypertension and limited continuous treatment.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Why is AI described as promising in this context?",{"text":117,"@type":113},"AI, especially machine learning models, is presented as a tool that can enhance preventive care and improve resource allocation through earlier intervention, with similar AI-driven integrations already occurring in higher-income health systems.",{"name":119,"@type":110,"acceptedAnswer":120},"Which barriers could prevent effective AI adoption in low-income settings like Tanzania?",{"text":121,"@type":113},"Barriers include digital exclusion, low digital and health literacy, and limited access to continuous care, creating risks that Tanzania could forfeit AI’s potential benefits without contextualized implementation frameworks.","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},411109,1790718428,{"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":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"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":144,"preview_page_count":4},962085571259,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","TYPE Opinion  \nPUBLISHED 18 December 2025 DOI 10.3389/fragi.2025.1673926  \nOPEN ACCESS  \nEDITED BY  \nDaniela Bakula,  \nUniversity of Copenhagen, Denmark  \nREVIEWED BY  \nGaurav Mishra,  \nDatta Meghe Institute of Medical Sciences, India  \n*CORRESPONDENCE  \nInnocent Arnold Tesha,  \n [drinnocenttesha@gmail.com](drinnocenttesha@gmail.com)  \n†PRESENT ADDRESS  \nInnocent Arnold Tesha,  \nInternal Medicine Department, Muhimbili National Hospital, Dar es Salaam, Tanzania  \nRECEIVED 26 July 2025  \nREVISED 17 November 2025  \nACCEPTED 17 November 2025  \nPUBLISHED 18 December 2025  \nCITATION  \nTesha IA (2025) Ethical and logistical imperatives for AI-driven cardiovascular risk prediction among older adults in Tanzania: framing a digital health agenda for low-income settings. Front. Aging 6:1673926 .  \ndoi: 10.3389/fragi.2025.1673926  \nCOPYRIGHT  \n© 2025 Tesha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nEthical and logistical imperatives for AI-driven cardiovascular risk prediction among older adults in Tanzania: framing a digital health agenda for low-income settings  \nInnocent Arnold Tesha *†  \nGeriatrics Medicine Department, First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning, China  \nKEYWORDS  \nartificial intelligence, cardiovascular risk prediction, older adults, digital health, geriatric cardiology, aging, Tanzania, low-and middle-income countries  \nIntroduction  \nThe global increase in life expectancy, which was initially evident in developed countries, has recently begun to manifest in developing countries. This trend is primarily driven by two key factors: declining fertility rates and increasing life expectancy (O’Sullivan, 2023) . Africa is also experiencing this demographic shift, driven by increasing life expectancy and a rising burden of non-communicable diseases, particularly cardiovascular disease (CVD) among older adults (Minja et al., 2022) . Similarly, Tanzania is undergoing a significant demographic shift, marked by rapidly increasing life expectancy and increasing rates of non-communicable diseases, particularly CVD in older adults (Bennett et al., 2018) . This shift is attributed to several factors, including aging population: the proportion of older adults in Tanzania has increased from 4% in the 2012 to 5.7% in the 2022 census, indicating a significant increase in the aging population over that decade (Findings, 2022) . Urbanization: Tanzania’s rapid urbanization has led to changes in lifestyle and living conditions such as sedentary lifestyles, unhealthy diets, and increased exposure to environmental pollutants (United Republic of Tanzania, 2014) . Collectively, these factors have contributed to a surge in CVD and diabetes across both urban and rural communities (Hamid et al., 2019) .  \nRecent national data for Tanzania reveal a severe and growing CVD epidemic. According to the 2023 Tanzania STEPS Survey, 23.1% of adults are living with hypertension. The health crisis escalates dramatically across age groups as prevalence surges to 43.5% in adults aged 46 and older, marking a nearly 3.5-fold increase in risk for older populations (Non-communicable Disease Risk Factors STEPS, 2023) Yet, fewer than 34.3% of hypertensive individuals are aware of their condition, and only one-third receive continuous treatment (Muhihi et al., 2020) These trends illustrate the urgent need for innovative risk prediction and prevention strategies.  \nArtificial intelligence (AI), particularly machine learning models for cardiovascular risk prediction, has gained global traction as a tool to enhance preventive care and ","cbCaifWxbyBdDCkT","https://ap.wps.com/l/cbCaifWxbyBdDCkT","pdf",2009713,"English","# Introduction\n# Materials and methods\n## Settings","[{\"question\":\"What problem does the article address regarding cardiovascular risk prediction in Tanzania?\",\"answer\":\"The article addresses the urgent need for preventive risk prediction because Tanzania faces a severe, growing cardiovascular disease burden among older adults, alongside low awareness of hypertension and limited continuous treatment.\"},{\"question\":\"Why is AI described as promising in this context?\",\"answer\":\"AI, especially machine learning models, is presented as a tool that can enhance preventive care and improve resource allocation through earlier intervention, with similar AI-driven integrations already occurring in higher-income health systems.\"},{\"question\":\"Which barriers could prevent effective AI adoption in low-income settings like Tanzania?\",\"answer\":\"Barriers include digital exclusion, low digital and health literacy, and limited access to continuous care, creating risks that Tanzania could forfeit AI’s potential benefits without contextualized implementation frameworks.\"}]","Ethical and logistical imperatives for AI-driven cardiovascular risk prediction among older adults in Tanzania - framing a digital health agenda for low-income settings | PDF",1790587453,13]