Pub Date : 2024-08-21DOI: 10.1038/s41746-024-01198-2
Stefanie Brückner, Celia Brightwell, Stephen Gilbert
A highly ambitious FDA initiative will explore, through a hub and ideas lab, how equitable healthcare at home can be delivered, recognizing that this is unlikely to come about without intervention. Market forces, as shaped by current regulations, are leading to digital health tools developed and operating in islands rather than enabling integrated digital care. Can the initiative, which adopts system-level regulatory thinking, solve this issue?
{"title":"FDA launches health care at home initiative to drive equity in digital medical care","authors":"Stefanie Brückner, Celia Brightwell, Stephen Gilbert","doi":"10.1038/s41746-024-01198-2","DOIUrl":"10.1038/s41746-024-01198-2","url":null,"abstract":"A highly ambitious FDA initiative will explore, through a hub and ideas lab, how equitable healthcare at home can be delivered, recognizing that this is unlikely to come about without intervention. Market forces, as shaped by current regulations, are leading to digital health tools developed and operating in islands rather than enabling integrated digital care. Can the initiative, which adopts system-level regulatory thinking, solve this issue?","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01198-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-16DOI: 10.1038/s41746-024-01207-4
Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H. Van Tassel, Kyle Kovacs, Emily Y. Chew, Zhiyong Lu, Zhangyang Wang, Yifan Peng
Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.
{"title":"Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modeling","authors":"Gregory Holste, Mingquan Lin, Ruiwen Zhou, Fei Wang, Lei Liu, Qi Yan, Sarah H. Van Tassel, Kyle Kovacs, Emily Y. Chew, Zhiyong Lu, Zhangyang Wang, Yifan Peng","doi":"10.1038/s41746-024-01207-4","DOIUrl":"10.1038/s41746-024-01207-4","url":null,"abstract":"Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing a disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01207-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-16DOI: 10.1038/s41746-024-01205-6
Teodora Lalova-Spinks, Peggy Valcke, John P. A. Ioannidis, Isabelle Huys
EU-US data transfers for health research remain a particularly thorny issue in view of the stringent rules of the EU General Data Protection Regulation (GDPR) and the challenges related to US mass surveillance programs, particularly the manner in which US law enforcement and national security agencies can access personal data originating from the EU. Since the entry into force of the GDPR, evidence of impeded collaborations is increasing, particularly in the case of sharing data with US public institutions. The adoption of a new EU-US adequacy decision in July 2023 does not hold the promise for a long-lasting solution due to the risks of being challenged and invalidated – yet again – at the Court of Justice of the EU. As the research community is calling for answers, the new proposal for a European Health Data Space regulation may hold a key to solving some of the existing issues. In this paper, we critically discuss the current rules and outline a possible way forward for transfers between public bodies.
{"title":"EU-US data transfers: an enduring challenge for health research collaborations","authors":"Teodora Lalova-Spinks, Peggy Valcke, John P. A. Ioannidis, Isabelle Huys","doi":"10.1038/s41746-024-01205-6","DOIUrl":"10.1038/s41746-024-01205-6","url":null,"abstract":"EU-US data transfers for health research remain a particularly thorny issue in view of the stringent rules of the EU General Data Protection Regulation (GDPR) and the challenges related to US mass surveillance programs, particularly the manner in which US law enforcement and national security agencies can access personal data originating from the EU. Since the entry into force of the GDPR, evidence of impeded collaborations is increasing, particularly in the case of sharing data with US public institutions. The adoption of a new EU-US adequacy decision in July 2023 does not hold the promise for a long-lasting solution due to the risks of being challenged and invalidated – yet again – at the Court of Justice of the EU. As the research community is calling for answers, the new proposal for a European Health Data Space regulation may hold a key to solving some of the existing issues. In this paper, we critically discuss the current rules and outline a possible way forward for transfers between public bodies.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01205-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-15DOI: 10.1038/s41746-024-01210-9
Liyang Liu, Haibo Wang, Yi Xing, Ziheng Zhang, Qingge Zhang, Ming Dong, Zhujiang Ma, Longjun Cai, Xiaoyi Wang, Yi Tang
Although computerized cognitive training (CCT) is an effective digital intervention for cognitive impairment, its dose-response relationship is understudied. This retrospective cohort study explores the association between training dose and cognitive improvement to find the optimal CCT dose. From 2017 to 2022, 8,709 participants with subjective cognitive decline, mild cognitive impairment, and mild dementia were analyzed. CCT exposure varied in daily dose and frequency, with cognitive improvement measured weekly using Cognitive Index. A mixed-effects model revealed significant Cognitive Index increases across most dose groups before reaching the optimal dose. For participants under 60 years, the optimal dose was 25 to <30 min per day for 6 days a week. For those 60 years or older, it was 50 to <55 min per day for 6 days a week. These findings highlight a dose-dependent effect in CCT, suggesting age-specific optimal dosing for cognitive improvement.
{"title":"Dose–response relationship between computerized cognitive training and cognitive improvement","authors":"Liyang Liu, Haibo Wang, Yi Xing, Ziheng Zhang, Qingge Zhang, Ming Dong, Zhujiang Ma, Longjun Cai, Xiaoyi Wang, Yi Tang","doi":"10.1038/s41746-024-01210-9","DOIUrl":"10.1038/s41746-024-01210-9","url":null,"abstract":"Although computerized cognitive training (CCT) is an effective digital intervention for cognitive impairment, its dose-response relationship is understudied. This retrospective cohort study explores the association between training dose and cognitive improvement to find the optimal CCT dose. From 2017 to 2022, 8,709 participants with subjective cognitive decline, mild cognitive impairment, and mild dementia were analyzed. CCT exposure varied in daily dose and frequency, with cognitive improvement measured weekly using Cognitive Index. A mixed-effects model revealed significant Cognitive Index increases across most dose groups before reaching the optimal dose. For participants under 60 years, the optimal dose was 25 to <30 min per day for 6 days a week. For those 60 years or older, it was 50 to <55 min per day for 6 days a week. These findings highlight a dose-dependent effect in CCT, suggesting age-specific optimal dosing for cognitive improvement.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01210-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-15DOI: 10.1038/s41746-024-01202-9
Daniel H. Pak, Minliang Liu, Theodore Kim, Caglar Ozturk, Raymond McKay, Ellen T. Roche, Rudolph Gleason, James S. Duncan
Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcium deposits on cardiovascular structures are still often manually reconstructed for physics-driven simulations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated image-to-mesh algorithm that enables robust incorporation of patient-specific calcification onto a given cardiovascular tissue mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to ~1 min of automated computation, and it solves an important problem that cannot be addressed with recent template-based meshing techniques. We validated our final calcified tissue meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of personalized cardiovascular biomechanics.
{"title":"Robust automated calcification meshing for personalized cardiovascular biomechanics","authors":"Daniel H. Pak, Minliang Liu, Theodore Kim, Caglar Ozturk, Raymond McKay, Ellen T. Roche, Rudolph Gleason, James S. Duncan","doi":"10.1038/s41746-024-01202-9","DOIUrl":"10.1038/s41746-024-01202-9","url":null,"abstract":"Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcium deposits on cardiovascular structures are still often manually reconstructed for physics-driven simulations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated image-to-mesh algorithm that enables robust incorporation of patient-specific calcification onto a given cardiovascular tissue mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to ~1 min of automated computation, and it solves an important problem that cannot be addressed with recent template-based meshing techniques. We validated our final calcified tissue meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of personalized cardiovascular biomechanics.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1038/s41746-024-01214-5
Antonis Valachis, Henrik Lindman
Decentralized clinical trials have gained in popularity over the last years due to their advantages related to broadening recruitment strategies and resource saving possibilities. As more clinical trials adopt decentralized strategies, it is essential to share the knowledge about both successful and unsuccessful efforts in the research community. In the present commentary, we explore potential reasons that led to early termination of a decentralized clinical trial in Oncology.
{"title":"Lessons learned from an unsuccessful decentralized clinical trial in Oncology","authors":"Antonis Valachis, Henrik Lindman","doi":"10.1038/s41746-024-01214-5","DOIUrl":"10.1038/s41746-024-01214-5","url":null,"abstract":"Decentralized clinical trials have gained in popularity over the last years due to their advantages related to broadening recruitment strategies and resource saving possibilities. As more clinical trials adopt decentralized strategies, it is essential to share the knowledge about both successful and unsuccessful efforts in the research community. In the present commentary, we explore potential reasons that led to early termination of a decentralized clinical trial in Oncology.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1038/s41746-024-01209-2
Megan Coder, Lacey McBride, Samantha McClenahan
Digital health technologies (DHT) offer the ability to deliver personalized care, lower barriers to access, and positively impact health outcomes. However, DHT utilization is impacted by insufficient market access pathways. A policy “full-stack”—including regulatory authorization, product value assessment, pricing and reimbursement, and patient access infrastructure—offers a framework for DHT integration into national healthcare ecosystems. Consistent clinical evidence requirements across national jurisdictions will further increase DHT scalability.
{"title":"Core elements of national policy for digital health technology evidence and access","authors":"Megan Coder, Lacey McBride, Samantha McClenahan","doi":"10.1038/s41746-024-01209-2","DOIUrl":"10.1038/s41746-024-01209-2","url":null,"abstract":"Digital health technologies (DHT) offer the ability to deliver personalized care, lower barriers to access, and positively impact health outcomes. However, DHT utilization is impacted by insufficient market access pathways. A policy “full-stack”—including regulatory authorization, product value assessment, pricing and reimbursement, and patient access infrastructure—offers a framework for DHT integration into national healthcare ecosystems. Consistent clinical evidence requirements across national jurisdictions will further increase DHT scalability.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1038/s41746-024-01213-6
Felix Busch, Jakob Nikolas Kather, Christian Johner, Marina Moser, Daniel Truhn, Lisa C. Adams, Keno K. Bressem
The European Union’s recently adopted Artificial Intelligence (AI) Act is the first comprehensive legal framework specifically on AI. This is particularly important for the healthcare domain, as other existing harmonisation legislation, such as the Medical Device Regulation, do not explicitly cover medical AI applications. Given the far-reaching impact of this regulation on the medical AI sector, this commentary provides an overview of the key elements of the AI Act, with easy-to-follow references to the relevant chapters.
{"title":"Navigating the European Union Artificial Intelligence Act for Healthcare","authors":"Felix Busch, Jakob Nikolas Kather, Christian Johner, Marina Moser, Daniel Truhn, Lisa C. Adams, Keno K. Bressem","doi":"10.1038/s41746-024-01213-6","DOIUrl":"10.1038/s41746-024-01213-6","url":null,"abstract":"The European Union’s recently adopted Artificial Intelligence (AI) Act is the first comprehensive legal framework specifically on AI. This is particularly important for the healthcare domain, as other existing harmonisation legislation, such as the Medical Device Regulation, do not explicitly cover medical AI applications. Given the far-reaching impact of this regulation on the medical AI sector, this commentary provides an overview of the key elements of the AI Act, with easy-to-follow references to the relevant chapters.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01213-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1038/s41746-024-01183-9
Lauryn Keeler Bruce, Dalila González, Subhasis Dasgupta, Benjamin L. Smarr
In the United States, normal-risk pregnancies are monitored with the recommended average of 14 prenatal visits. Check-ins every few weeks are the standard of care. This low time resolution and reliance on subjective feedback instead of direct physiological measurement, could be augmented by remote monitoring. To date, continuous physiological measurements have not been characterized across all of pregnancy, so there is little basis of comparison to support the development of the specific monitoring capabilities. Wearables have been shown to enable the detection and prediction of acute illness, often faster than subjective symptom reporting. Wearables have also been used for years to monitor chronic conditions, such as continuous glucose monitors. Here we perform a retrospective analysis on multimodal wearable device data (Oura Ring) generated across pregnancy within 120 individuals. These data reveal clear trajectories of pregnancy from cycling to conception through postpartum recovery. We assessed individuals in whom pregnancy did not progress past the first trimester, and found associated deviations, corroborating that continuous monitoring adds new information that could support decision-making even in the early stages of pregnancy. By contrast, we did not find significant deviations between full-term pregnancies of people younger than 35 and of people with “advanced maternal age”, suggesting that analysis of continuous data within individuals can augment risk assessment beyond standard population comparisons. Our findings demonstrate that low-cost, high-resolution monitoring at all stages of pregnancy in real-world settings is feasible and that many studies into specific demographics, risks, etc., could be carried out using this newer technology.
{"title":"Biometrics of complete human pregnancy recorded by wearable devices","authors":"Lauryn Keeler Bruce, Dalila González, Subhasis Dasgupta, Benjamin L. Smarr","doi":"10.1038/s41746-024-01183-9","DOIUrl":"10.1038/s41746-024-01183-9","url":null,"abstract":"In the United States, normal-risk pregnancies are monitored with the recommended average of 14 prenatal visits. Check-ins every few weeks are the standard of care. This low time resolution and reliance on subjective feedback instead of direct physiological measurement, could be augmented by remote monitoring. To date, continuous physiological measurements have not been characterized across all of pregnancy, so there is little basis of comparison to support the development of the specific monitoring capabilities. Wearables have been shown to enable the detection and prediction of acute illness, often faster than subjective symptom reporting. Wearables have also been used for years to monitor chronic conditions, such as continuous glucose monitors. Here we perform a retrospective analysis on multimodal wearable device data (Oura Ring) generated across pregnancy within 120 individuals. These data reveal clear trajectories of pregnancy from cycling to conception through postpartum recovery. We assessed individuals in whom pregnancy did not progress past the first trimester, and found associated deviations, corroborating that continuous monitoring adds new information that could support decision-making even in the early stages of pregnancy. By contrast, we did not find significant deviations between full-term pregnancies of people younger than 35 and of people with “advanced maternal age”, suggesting that analysis of continuous data within individuals can augment risk assessment beyond standard population comparisons. Our findings demonstrate that low-cost, high-resolution monitoring at all stages of pregnancy in real-world settings is feasible and that many studies into specific demographics, risks, etc., could be carried out using this newer technology.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01183-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141918748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-10DOI: 10.1038/s41746-024-01212-7
Rui Yang, Sabarinath Vinod Nair, Yuhe Ke, Danny D’Agostino, Mingxuan Liu, Yilin Ning, Nan Liu
Artificial intelligence (AI) has been extensively researched in medicine, but its practical application remains limited. Meanwhile, there are various disparities in existing AI-enabled clinical studies, which pose a challenge to global health equity. In this study, we conducted an in-depth analysis of the geo-economic distribution of 159 AI-enabled clinical studies, as well as the gender disparities among these studies. We aim to reveal these disparities from a global literature perspective, thus highlighting the need for equitable access to medical AI technologies.
{"title":"Disparities in clinical studies of AI enabled applications from a global perspective","authors":"Rui Yang, Sabarinath Vinod Nair, Yuhe Ke, Danny D’Agostino, Mingxuan Liu, Yilin Ning, Nan Liu","doi":"10.1038/s41746-024-01212-7","DOIUrl":"10.1038/s41746-024-01212-7","url":null,"abstract":"Artificial intelligence (AI) has been extensively researched in medicine, but its practical application remains limited. Meanwhile, there are various disparities in existing AI-enabled clinical studies, which pose a challenge to global health equity. In this study, we conducted an in-depth analysis of the geo-economic distribution of 159 AI-enabled clinical studies, as well as the gender disparities among these studies. We aim to reveal these disparities from a global literature perspective, thus highlighting the need for equitable access to medical AI technologies.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141913465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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