Sunny Jung Kim, Viktor Clark, Jeff T Hancock, Reza Rawassizadeh, Hongfang Liu, Emmanuel A Taylor, Vanessa B Sheppard
{"title":"利用人工智能介导的交流来预防和控制癌症和药物成瘾：系统综述。","authors":"Sunny Jung Kim, Viktor Clark, Jeff T Hancock, Reza Rawassizadeh, Hongfang Liu, Emmanuel A Taylor, Vanessa B Sheppard","doi":"10.1093/tbm/ibaf007","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.</p><p><strong>Methods: </strong>Eight databases were searched from 2017 to 2022 using the Population Intervention Control Outcome Study (PICOS) framework. We synthesized findings of AIMC-based interventions for adult populations in cancer prevention/control or substance use, applying SIGN Methodology Checklist 2 for quality assessments and reviewing retention and engagement.</p><p><strong>Results: </strong>Initial screening identified 187 studies; seven met inclusion criteria, involving 2768 participants. Females comprised 67.6% (n = 1870). Mean participant age was 42.73 years (SD = 7.00). Five studies demonstrated significant improvements in substance use recovery, physical activity, genetic testing, or dietary habits.</p><p><strong>Conclusions: </strong>AIMC shows promise in enhancing health behaviors, but further exploration is needed on privacy risks, biases, safety concerns, chatbot features, and serving underserved populations.</p><p><strong>Implications: </strong>There is a critical need to foster comprehensive fully powered studies and collaborations between technology developers, healthcare providers, and researchers. Policymakers can facilitate the responsible integration of AIMC technologies into healthcare systems, ensuring equitable access and maximizing their impact on public health outcomes.</p>","PeriodicalId":48679,"journal":{"name":"Translational Behavioral Medicine","volume":"15 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging artificial intelligence-mediated communication for cancer prevention and control and drug addiction: A systematic review.\",\"authors\":\"Sunny Jung Kim, Viktor Clark, Jeff T Hancock, Reza Rawassizadeh, Hongfang Liu, Emmanuel A Taylor, Vanessa B Sheppard\",\"doi\":\"10.1093/tbm/ibaf007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.</p><p><strong>Methods: </strong>Eight databases were searched from 2017 to 2022 using the Population Intervention Control Outcome Study (PICOS) framework. We synthesized findings of AIMC-based interventions for adult populations in cancer prevention/control or substance use, applying SIGN Methodology Checklist 2 for quality assessments and reviewing retention and engagement.</p><p><strong>Results: </strong>Initial screening identified 187 studies; seven met inclusion criteria, involving 2768 participants. Females comprised 67.6% (n = 1870). Mean participant age was 42.73 years (SD = 7.00). Five studies demonstrated significant improvements in substance use recovery, physical activity, genetic testing, or dietary habits.</p><p><strong>Conclusions: </strong>AIMC shows promise in enhancing health behaviors, but further exploration is needed on privacy risks, biases, safety concerns, chatbot features, and serving underserved populations.</p><p><strong>Implications: </strong>There is a critical need to foster comprehensive fully powered studies and collaborations between technology developers, healthcare providers, and researchers. Policymakers can facilitate the responsible integration of AIMC technologies into healthcare systems, ensuring equitable access and maximizing their impact on public health outcomes.</p>\",\"PeriodicalId\":48679,\"journal\":{\"name\":\"Translational Behavioral Medicine\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2025-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational Behavioral Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/tbm/ibaf007\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational Behavioral Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/tbm/ibaf007","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Leveraging artificial intelligence-mediated communication for cancer prevention and control and drug addiction: A systematic review.
Objective: To conduct a systematic review on Artificial Intelligence-Mediated Communication (AIMC) behavioral interventions in cancer prevention/control and substance use.
Methods: Eight databases were searched from 2017 to 2022 using the Population Intervention Control Outcome Study (PICOS) framework. We synthesized findings of AIMC-based interventions for adult populations in cancer prevention/control or substance use, applying SIGN Methodology Checklist 2 for quality assessments and reviewing retention and engagement.
Results: Initial screening identified 187 studies; seven met inclusion criteria, involving 2768 participants. Females comprised 67.6% (n = 1870). Mean participant age was 42.73 years (SD = 7.00). Five studies demonstrated significant improvements in substance use recovery, physical activity, genetic testing, or dietary habits.
Conclusions: AIMC shows promise in enhancing health behaviors, but further exploration is needed on privacy risks, biases, safety concerns, chatbot features, and serving underserved populations.
Implications: There is a critical need to foster comprehensive fully powered studies and collaborations between technology developers, healthcare providers, and researchers. Policymakers can facilitate the responsible integration of AIMC technologies into healthcare systems, ensuring equitable access and maximizing their impact on public health outcomes.
期刊介绍：
Translational Behavioral Medicine publishes content that engages, informs, and catalyzes dialogue about behavioral medicine among the research, practice, and policy communities. TBM began receiving an Impact Factor in 2015 and currently holds an Impact Factor of 2.989.
TBM is one of two journals published by the Society of Behavioral Medicine. The Society of Behavioral Medicine is a multidisciplinary organization of clinicians, educators, and scientists dedicated to promoting the study of the interactions of behavior with biology and the environment, and then applying that knowledge to improve the health and well-being of individuals, families, communities, and populations.
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