Pub Date : 2024-11-15DOI: 10.1016/S2589-7500(24)00202-4
Jeremy Y Ng, Sharleen G Maduranayagam, Nirekah Suthakar, Amy Li, Cynthia Lokker, Alfonso Iorio, R Brian Haynes, David Moher
Chatbots are artificial intelligence (AI) programs designed to simulate conversations with humans that present opportunities and challenges in scientific research. Despite growing clarity from publishing organisations on the use of AI chatbots, researchers' perceptions remain less understood. In this international cross-sectional survey, we aimed to assess researchers' attitudes, familiarity, perceived benefits, and limitations related to AI chatbots. Our online survey was open from July 9 to Aug 11, 2023, with 61 560 corresponding authors identified from 122 323 articles indexed in PubMed. 2452 (4·0%) provided responses and 2165 (94·5%) of 2292 who met eligibility criteria completed the survey. 1161 (54·0%) of 2149 respondents were male and 959 (44·6%) were female. 1294 (60·5%) of 2138 respondents were familiar with AI chatbots, and 945 (44·5%) of 2125 had previously used AI chatbots in research. Only 244 (11·4%) of 2137 reported institutional training on AI tools, and 211 (9·9%) of 2131 noted institutional policies on AI chatbot use. Despite mixed opinions on the benefits, 1428 (69·7%) of 2048 expressed interest in further training. Although many valued AI chatbots for reducing administrative workload (1299 [66·9%] of 1941), there was insufficient understanding of the decision making process (1484 [77·2%] of 1923). Overall, this study highlights substantial interest in AI chatbots among researchers, but also points to the need for more formal training and clarity on their use.
{"title":"Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey.","authors":"Jeremy Y Ng, Sharleen G Maduranayagam, Nirekah Suthakar, Amy Li, Cynthia Lokker, Alfonso Iorio, R Brian Haynes, David Moher","doi":"10.1016/S2589-7500(24)00202-4","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00202-4","url":null,"abstract":"<p><p>Chatbots are artificial intelligence (AI) programs designed to simulate conversations with humans that present opportunities and challenges in scientific research. Despite growing clarity from publishing organisations on the use of AI chatbots, researchers' perceptions remain less understood. In this international cross-sectional survey, we aimed to assess researchers' attitudes, familiarity, perceived benefits, and limitations related to AI chatbots. Our online survey was open from July 9 to Aug 11, 2023, with 61 560 corresponding authors identified from 122 323 articles indexed in PubMed. 2452 (4·0%) provided responses and 2165 (94·5%) of 2292 who met eligibility criteria completed the survey. 1161 (54·0%) of 2149 respondents were male and 959 (44·6%) were female. 1294 (60·5%) of 2138 respondents were familiar with AI chatbots, and 945 (44·5%) of 2125 had previously used AI chatbots in research. Only 244 (11·4%) of 2137 reported institutional training on AI tools, and 211 (9·9%) of 2131 noted institutional policies on AI chatbot use. Despite mixed opinions on the benefits, 1428 (69·7%) of 2048 expressed interest in further training. Although many valued AI chatbots for reducing administrative workload (1299 [66·9%] of 1941), there was insufficient understanding of the decision making process (1484 [77·2%] of 1923). Overall, this study highlights substantial interest in AI chatbots among researchers, but also points to the need for more formal training and clarity on their use.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/S2589-7500(24)00215-2
Gareth Hopkin, Richard Branson, Paul Campbell, Holly Coole, Sophie Cooper, Francesca Edelmann, Grace Gatera, Jamie Morgan, Mark Salmon
Demand for mental health services exceeds available resources globally, and access to diagnosis and evidence-based treatment is affected by long delays. Digital mental health technologies present an opportunity to reimagine the delivery of mental health support by providing innovative, effective, and tailored approaches that meet people's individual preferences and goals. These technologies also present new challenges, however, and efforts must be made to ensure they are safe and effective. The UK Medicines and Healthcare products Regulatory Agency and the National Institute for Health and Care Excellence have launched a partnership, funded by Wellcome, that explores regulation and evaluation of digital mental health technologies. This Viewpoint describes a series of key challenges across the regulatory and health technology assessment pathways and aims to facilitate discussions to ensure that approaches to regulation and evaluation are informed by patients, the public, and professionals working within mental health. We invite partners from across the mental health community to engage with, collaborate with, and provide scrutiny of this project to ensure it delivers the best possible outcomes.
在全球范围内,对心理健康服务的需求超过了可用资源,而获得诊断和循证治疗则受到长期拖延的影响。数字心理健康技术通过提供创新、有效和量身定制的方法,满足人们的个人偏好和目标,为重新想象心理健康支持的提供方式提供了机会。然而,这些技术也带来了新的挑战,我们必须努力确保它们的安全性和有效性。英国药品和保健品监管局与英国国家健康与护理卓越研究所(National Institute for Health and Care Excellence)在惠康公司的资助下建立了合作关系,共同探讨数字心理健康技术的监管和评估问题。本观点阐述了监管和健康技术评估途径中的一系列关键挑战,旨在促进讨论,以确保监管和评估方法由患者、公众和心理健康领域的专业人士提供信息。我们邀请整个心理健康界的合作伙伴参与、合作并对该项目进行监督,以确保其取得最佳成果。
{"title":"Building robust, proportionate, and timely approaches to regulation and evaluation of digital mental health technologies.","authors":"Gareth Hopkin, Richard Branson, Paul Campbell, Holly Coole, Sophie Cooper, Francesca Edelmann, Grace Gatera, Jamie Morgan, Mark Salmon","doi":"10.1016/S2589-7500(24)00215-2","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00215-2","url":null,"abstract":"<p><p>Demand for mental health services exceeds available resources globally, and access to diagnosis and evidence-based treatment is affected by long delays. Digital mental health technologies present an opportunity to reimagine the delivery of mental health support by providing innovative, effective, and tailored approaches that meet people's individual preferences and goals. These technologies also present new challenges, however, and efforts must be made to ensure they are safe and effective. The UK Medicines and Healthcare products Regulatory Agency and the National Institute for Health and Care Excellence have launched a partnership, funded by Wellcome, that explores regulation and evaluation of digital mental health technologies. This Viewpoint describes a series of key challenges across the regulatory and health technology assessment pathways and aims to facilitate discussions to ensure that approaches to regulation and evaluation are informed by patients, the public, and professionals working within mental health. We invite partners from across the mental health community to engage with, collaborate with, and provide scrutiny of this project to ensure it delivers the best possible outcomes.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/S2589-7500(24)00217-6
Damien K Ming, Abi Merriel, David M E Freeman, Carol Kingdon, Yamikani Chimwaza, Mohammad S Islam, Anthony Cass, Benjamin Greenfield, Address Malata, Mahbubul Hoque, Senjuti Saha, Alison H Holmes
Infections occurring in the mother and neonate exert a substantial health burden worldwide. Optimising infection management is crucial for improving individual outcomes and reducing the incidence of antimicrobial resistance. Digital health technologies, through their accessibility and scalability, hold promise in improving the quality of care across diverse health-care settings. In settings with poor access to laboratory services, innovative uses of existing data, point-of-care diagnostics, and wearables could allow for better recognition of host responses during infection and antimicrobial optimisation. The linkage and connectivity of information can support the coordinated delivery of care between health-care facilities and the community. Continuous real-time monitoring of infection markers in the mother and neonate through biosensing can provide notable opportunities for intervention and improvements in care. However, the development and implementation of these interventions should be respectful, prioritise safety, and emphasise sustainable, locally derived solutions. Addressing existing gender, economic, and health-care disparities will be essential for ensuring equitable implementation.
{"title":"Advancing the management of maternal, fetal, and neonatal infection through harnessing digital health innovations.","authors":"Damien K Ming, Abi Merriel, David M E Freeman, Carol Kingdon, Yamikani Chimwaza, Mohammad S Islam, Anthony Cass, Benjamin Greenfield, Address Malata, Mahbubul Hoque, Senjuti Saha, Alison H Holmes","doi":"10.1016/S2589-7500(24)00217-6","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00217-6","url":null,"abstract":"<p><p>Infections occurring in the mother and neonate exert a substantial health burden worldwide. Optimising infection management is crucial for improving individual outcomes and reducing the incidence of antimicrobial resistance. Digital health technologies, through their accessibility and scalability, hold promise in improving the quality of care across diverse health-care settings. In settings with poor access to laboratory services, innovative uses of existing data, point-of-care diagnostics, and wearables could allow for better recognition of host responses during infection and antimicrobial optimisation. The linkage and connectivity of information can support the coordinated delivery of care between health-care facilities and the community. Continuous real-time monitoring of infection markers in the mother and neonate through biosensing can provide notable opportunities for intervention and improvements in care. However, the development and implementation of these interventions should be respectful, prioritise safety, and emphasise sustainable, locally derived solutions. Addressing existing gender, economic, and health-care disparities will be essential for ensuring equitable implementation.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/S2589-7500(24)00251-6
The Lancet Digital Health
{"title":"Using digital health to address antimicrobial resistance.","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00251-6","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00251-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/S2589-7500(24)00198-5
Timothy M Rawson, Nina Zhu, Ronald Galiwango, Derek Cocker, Mohammad Shahidul Islam, Ashleigh Myall, Vasin Vasikasin, Richard Wilson, Nusrat Shafiq, Shampa Das, Alison H Holmes
Digital health technology (DHT) describes tools and devices that generate or process health data. The application of DHTs could improve the diagnosis, treatment, and surveillance of bacterial infection and the prevention of antimicrobial resistance (AMR). DHTs to optimise antimicrobial use are rapidly being developed. To support the global adoption of DHTs and the opportunities offered to optimise antimicrobial use consensus is needed on what data are required to support antimicrobial decision making. This Series paper will explore bacterial AMR in humans and the need to optimise antimicrobial use in response to this global threat. It will also describe state-of-the-art DHTs to optimise antimicrobial prescribing in high-income and low-income and middle-income countries, and consider what fundamental data are ideally required for and from such technologies to support optimised antimicrobial use.
数字健康技术(DHT)是指生成或处理健康数据的工具和设备。数字健康技术的应用可以改善细菌感染的诊断、治疗和监测,并预防抗菌素耐药性(AMR)。用于优化抗菌药物使用的 DHT 正在迅速发展。为了支持在全球范围内采用 DHTs 并利用其提供的机会优化抗菌药物的使用,需要就支持抗菌药物决策所需的数据达成共识。本系列论文将探讨人类的细菌性 AMR 以及优化抗菌药使用以应对这一全球性威胁的必要性。它还将介绍在高收入和中低收入国家优化抗菌药物处方的最先进的 DHT 技术,并探讨这些技术需要哪些理想的基础数据来支持抗菌药物的优化使用。
{"title":"Using digital health technologies to optimise antimicrobial use globally.","authors":"Timothy M Rawson, Nina Zhu, Ronald Galiwango, Derek Cocker, Mohammad Shahidul Islam, Ashleigh Myall, Vasin Vasikasin, Richard Wilson, Nusrat Shafiq, Shampa Das, Alison H Holmes","doi":"10.1016/S2589-7500(24)00198-5","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00198-5","url":null,"abstract":"<p><p>Digital health technology (DHT) describes tools and devices that generate or process health data. The application of DHTs could improve the diagnosis, treatment, and surveillance of bacterial infection and the prevention of antimicrobial resistance (AMR). DHTs to optimise antimicrobial use are rapidly being developed. To support the global adoption of DHTs and the opportunities offered to optimise antimicrobial use consensus is needed on what data are required to support antimicrobial decision making. This Series paper will explore bacterial AMR in humans and the need to optimise antimicrobial use in response to this global threat. It will also describe state-of-the-art DHTs to optimise antimicrobial prescribing in high-income and low-income and middle-income countries, and consider what fundamental data are ideally required for and from such technologies to support optimised antimicrobial use.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-14DOI: 10.1016/S2589-7500(24)00242-5
Jesus Rodriguez-Manzano, Sumithra Subramaniam, Chibuzor Uchea, Katarzyna M Szostak-Lipowicz, Jane Freeman, Marcus Rauch, Halidou Tinto, Heather J Zar, Umberto D'Alessandro, Alison H Holmes, Gordon A Awandare
Diagnostic tools are key to guiding patient management and informing public health policies to control infectious diseases. However, many diseases still do not have effective diagnostics and much of the global population faces restricted access to reliable, affordable testing. This limitation underscores the urgent need for innovation to enhance diagnostic availability and effectiveness. Developing diagnostics presents distinct challenges, especially for innovators and regulators. Unlike medicines, regulatory pathways for diagnostics are often less defined and more complex due to their diverse risk profiles and wide range of products. These challenges are amplified in low-income and middle-income countries, which often do not have regulatory frameworks for this specific purpose. In the UK, initiatives aim to support innovation by providing clearer regulatory pathways and ensuring that diagnostics are safe and effective. Regulators are also collaborating internationally to expedite diagnostics for high-need regions. Harmonised standards, regulatory frameworks, and approval processes are essential to ensure consistent quality and safety across regions and facilitate faster development and global access. This Series paper explores the regulatory challenges in infectious disease and antimicrobial resistance diagnostics, focusing on the UK's response and the broader global efforts to address these issues.
{"title":"Innovative diagnostic technologies: navigating regulatory frameworks through advances, challenges, and future prospects.","authors":"Jesus Rodriguez-Manzano, Sumithra Subramaniam, Chibuzor Uchea, Katarzyna M Szostak-Lipowicz, Jane Freeman, Marcus Rauch, Halidou Tinto, Heather J Zar, Umberto D'Alessandro, Alison H Holmes, Gordon A Awandare","doi":"10.1016/S2589-7500(24)00242-5","DOIUrl":"https://doi.org/10.1016/S2589-7500(24)00242-5","url":null,"abstract":"<p><p>Diagnostic tools are key to guiding patient management and informing public health policies to control infectious diseases. However, many diseases still do not have effective diagnostics and much of the global population faces restricted access to reliable, affordable testing. This limitation underscores the urgent need for innovation to enhance diagnostic availability and effectiveness. Developing diagnostics presents distinct challenges, especially for innovators and regulators. Unlike medicines, regulatory pathways for diagnostics are often less defined and more complex due to their diverse risk profiles and wide range of products. These challenges are amplified in low-income and middle-income countries, which often do not have regulatory frameworks for this specific purpose. In the UK, initiatives aim to support innovation by providing clearer regulatory pathways and ensuring that diagnostics are safe and effective. Regulators are also collaborating internationally to expedite diagnostics for high-need regions. Harmonised standards, regulatory frameworks, and approval processes are essential to ensure consistent quality and safety across regions and facilitate faster development and global access. This Series paper explores the regulatory challenges in infectious disease and antimicrobial resistance diagnostics, focusing on the UK's response and the broader global efforts to address these issues.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":""},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/S2589-7500(24)00169-9
Sumali Bajaj SM , Siyu Chen DPhil , Richard Creswell DPhil , Reshania Naidoo MD , Joseph L-H Tsui MSc , Olumide Kolade BSc , George Nicholson DPhil , Brieuc Lehmann PhD , James A Hay PhD , Prof Moritz U G Kraemer DPhil , Ricardo Aguas PhD , Prof Christl A Donnelly ScD , Tom Fowler FFPH , Prof Susan Hopkins FMedSci , Liberty Cantrell MSc , Prabin Dahal DPhil , Prof Lisa J White PhD , Kasia Stepniewska PhD , Merryn Voysey DPhil , Ben Lambert DPhil , Lisa J White
<div><h3>Background</h3><div>Understanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour during an infectious disease outbreak can help to protect vulnerable populations and guide equity-driven interventions. The COVID-19 pandemic probably exerted different stresses on individuals in different sociodemographic groups and ensuring fair access to and usage of COVID-19 tests was a crucial element of England's testing programme. We aimed to investigate the relationship between sociodemographic factors and COVID-19 testing behaviours in England during the COVID-19 pandemic.</div></div><div><h3>Methods</h3><div>We did a population-based study of COVID-19 testing behaviours with mass COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from Oct 1, 2020, to March 30, 2022. We used mass testing data for lateral flow device (LFD; data for approximately 290 million tests performed and reported) and PCR (data for approximately 107 million tests performed and returned from the laboratory) tests made available for the general public and provided by date and self-reported age and ethnicity at the lower tier local authority (LTLA) level. We also used publicly available data on mean population size estimates for individual LTLAs, and data on ethnic groups, age groups, and deprivation indices for LTLAs. We did not have access to REACT-1 or ONS-CIS prevalence data disaggregated by sex or gender. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by both self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. With confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability subsequent to reporting a positive LFD for PCR tests by sociodemographic groups. We also estimated the daily incidence, allowing us to calculate the fraction of cases captured by the testing programme.</div></div><div><h3>Findings</h3><div>From March, 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per capita as individuals in the least deprived areas (median ratio 0·50 [IQR 0·44–0·54]). During the period October, 2020, to June, 2021, PCR testing patterns showed the opposite trend, with individuals in the most deprived areas performing almost double the number of PCR tests per capita than those in the least deprived areas (1·8 [1·7–1·9]). Infection prevalences in Asian or Asian British individuals were considerably higher than those of other ethnic groups during the alpha (B.1.1.7) and omicron (B.1.1.529
{"title":"COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys","authors":"Sumali Bajaj SM , Siyu Chen DPhil , Richard Creswell DPhil , Reshania Naidoo MD , Joseph L-H Tsui MSc , Olumide Kolade BSc , George Nicholson DPhil , Brieuc Lehmann PhD , James A Hay PhD , Prof Moritz U G Kraemer DPhil , Ricardo Aguas PhD , Prof Christl A Donnelly ScD , Tom Fowler FFPH , Prof Susan Hopkins FMedSci , Liberty Cantrell MSc , Prabin Dahal DPhil , Prof Lisa J White PhD , Kasia Stepniewska PhD , Merryn Voysey DPhil , Ben Lambert DPhil , Lisa J White","doi":"10.1016/S2589-7500(24)00169-9","DOIUrl":"10.1016/S2589-7500(24)00169-9","url":null,"abstract":"<div><h3>Background</h3><div>Understanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour during an infectious disease outbreak can help to protect vulnerable populations and guide equity-driven interventions. The COVID-19 pandemic probably exerted different stresses on individuals in different sociodemographic groups and ensuring fair access to and usage of COVID-19 tests was a crucial element of England's testing programme. We aimed to investigate the relationship between sociodemographic factors and COVID-19 testing behaviours in England during the COVID-19 pandemic.</div></div><div><h3>Methods</h3><div>We did a population-based study of COVID-19 testing behaviours with mass COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from Oct 1, 2020, to March 30, 2022. We used mass testing data for lateral flow device (LFD; data for approximately 290 million tests performed and reported) and PCR (data for approximately 107 million tests performed and returned from the laboratory) tests made available for the general public and provided by date and self-reported age and ethnicity at the lower tier local authority (LTLA) level. We also used publicly available data on mean population size estimates for individual LTLAs, and data on ethnic groups, age groups, and deprivation indices for LTLAs. We did not have access to REACT-1 or ONS-CIS prevalence data disaggregated by sex or gender. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by both self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. With confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability subsequent to reporting a positive LFD for PCR tests by sociodemographic groups. We also estimated the daily incidence, allowing us to calculate the fraction of cases captured by the testing programme.</div></div><div><h3>Findings</h3><div>From March, 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per capita as individuals in the least deprived areas (median ratio 0·50 [IQR 0·44–0·54]). During the period October, 2020, to June, 2021, PCR testing patterns showed the opposite trend, with individuals in the most deprived areas performing almost double the number of PCR tests per capita than those in the least deprived areas (1·8 [1·7–1·9]). Infection prevalences in Asian or Asian British individuals were considerably higher than those of other ethnic groups during the alpha (B.1.1.7) and omicron (B.1.1.529","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e778-e790"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510623","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 : 2024-10-23DOI: 10.1016/S2589-7500(24)00146-8
Joseph E Alderman MB ChB , Maria Charalambides MB ChB , Gagandeep Sachdeva MB ChB , Elinor Laws MB BCh , Joanne Palmer PhD , Elsa Lee MSc , Vaishnavi Menon MB ChB , Qasim Malik MB ChB , Sonam Vadera MB BS , Prof Melanie Calvert PhD , Marzyeh Ghassemi PhD , Melissa D McCradden PhD , Johan Ordish MA , Bilal Mateen MBBS , Prof Charlotte Summers PhD , Jacqui Gath , Rubeta N Matin PhD , Prof Alastair K Denniston PhD , Xiaoxuan Liu PhD
During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic review assessed datasets used for AI development during the pandemic, identifying several deficiencies. Datasets were identified by screening articles from MEDLINE and using Google Dataset Search. 192 datasets were analysed for metadata completeness, composition, data accessibility, and ethical considerations. Findings revealed substantial gaps: only 48% of datasets documented individuals’ country of origin, 43% reported age, and under 25% included sex, gender, race, or ethnicity. Information on data labelling, ethical review, or consent was frequently missing. Many datasets reused data with inadequate traceability. Notably, historical paediatric chest x-rays appeared in some datasets without acknowledgment. These deficiencies highlight the need for better data quality and transparent documentation to lessen the risk that biased AI models are developed in future health emergencies.
{"title":"Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development—a systematic review","authors":"Joseph E Alderman MB ChB , Maria Charalambides MB ChB , Gagandeep Sachdeva MB ChB , Elinor Laws MB BCh , Joanne Palmer PhD , Elsa Lee MSc , Vaishnavi Menon MB ChB , Qasim Malik MB ChB , Sonam Vadera MB BS , Prof Melanie Calvert PhD , Marzyeh Ghassemi PhD , Melissa D McCradden PhD , Johan Ordish MA , Bilal Mateen MBBS , Prof Charlotte Summers PhD , Jacqui Gath , Rubeta N Matin PhD , Prof Alastair K Denniston PhD , Xiaoxuan Liu PhD","doi":"10.1016/S2589-7500(24)00146-8","DOIUrl":"10.1016/S2589-7500(24)00146-8","url":null,"abstract":"<div><div>During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic review assessed datasets used for AI development during the pandemic, identifying several deficiencies. Datasets were identified by screening articles from MEDLINE and using Google Dataset Search. 192 datasets were analysed for metadata completeness, composition, data accessibility, and ethical considerations. Findings revealed substantial gaps: only 48% of datasets documented individuals’ country of origin, 43% reported age, and under 25% included sex, gender, race, or ethnicity. Information on data labelling, ethical review, or consent was frequently missing. Many datasets reused data with inadequate traceability. Notably, historical paediatric chest x-rays appeared in some datasets without acknowledgment. These deficiencies highlight the need for better data quality and transparent documentation to lessen the risk that biased AI models are developed in future health emergencies.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e827-e847"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510627","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 : 2024-10-23DOI: 10.1016/S2589-7500(24)00173-0
Zacharias V Fisches MSc , Michael Ball ScB , Trasias Mukama PhD , Vilim Štih PhD , Nicholas R Payne PhD , Sarah E Hickman PhD , Prof Fiona J Gilbert PhD , Stefan Bunk MSc , Christian Leibig PhD
Background
Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains understudied. We compared programme-level performance metrics of seven AI integration strategies.
Methods
We performed a retrospective comparative evaluation of seven strategies for integrating AI into mammography screening using datasets generated from screening programmes in Germany (n=1 657 068), the UK (n=223 603) and Sweden (n=22 779). The commercially available AI model used was Vara version 2.10, trained from scratch on German data. We simulated the performance of each strategy in terms of cancer detection rate (CDR), recall rate, and workload reduction, and compared the metrics with those of the screening programmes. We also assessed the distribution of the stages and grades of the cancers detected by each strategy and the AI model's ability to correctly localise those cancers.
Findings
Compared with the German screening programme (CDR 6·32 per 1000 examinations, recall rate 4·11 per 100 examinations), replacement of both readers (standalone AI strategy) achieved a non-inferior CDR of 6·37 (95% CI 6·10–6·64) at a recall rate of 3·80 (95% CI 3·67–3·93), whereas single reader replacement achieved a CDR of 6·49 (6·31–6·67), a recall rate of 4·01 (3·92–4·10), and a 49% workload reduction. Programme-level decision referral achieved a CDR of 6·85 (6·61–7·11), a recall rate of 3·55 (3·43–3·68), and an 84% workload reduction. Compared with the UK programme CDR of 8·19, the reader-level, programme-level, and deferral to single reader strategies achieved CDRs of 8·24 (7·82–8·71), 8·59 (8·12–9·06), and 8·28 (7·86–8·71), without increasing recall and while reducing workload by 37%, 81%, and 95%, respectively. On the Swedish dataset, programme-level decision referral increased the CDR by 17·7% without increasing recall and while reducing reading workload by 92%.
Interpretation
The decision referral strategies offered the largest improvements in cancer detection rates and reduction in recall rates, and all strategies except normal triaging showed potential to improve screening metrics.
{"title":"Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis","authors":"Zacharias V Fisches MSc , Michael Ball ScB , Trasias Mukama PhD , Vilim Štih PhD , Nicholas R Payne PhD , Sarah E Hickman PhD , Prof Fiona J Gilbert PhD , Stefan Bunk MSc , Christian Leibig PhD","doi":"10.1016/S2589-7500(24)00173-0","DOIUrl":"10.1016/S2589-7500(24)00173-0","url":null,"abstract":"<div><h3>Background</h3><div>Integrating artificial intelligence (AI) into mammography screening can support radiologists and improve programme metrics, yet the potential of different strategies for integrating the technology remains understudied. We compared programme-level performance metrics of seven AI integration strategies.</div></div><div><h3>Methods</h3><div>We performed a retrospective comparative evaluation of seven strategies for integrating AI into mammography screening using datasets generated from screening programmes in Germany (n=1 657 068), the UK (n=223 603) and Sweden (n=22 779). The commercially available AI model used was Vara version 2.10, trained from scratch on German data. We simulated the performance of each strategy in terms of cancer detection rate (CDR), recall rate, and workload reduction, and compared the metrics with those of the screening programmes. We also assessed the distribution of the stages and grades of the cancers detected by each strategy and the AI model's ability to correctly localise those cancers.</div></div><div><h3>Findings</h3><div>Compared with the German screening programme (CDR 6·32 per 1000 examinations, recall rate 4·11 per 100 examinations), replacement of both readers (standalone AI strategy) achieved a non-inferior CDR of 6·37 (95% CI 6·10–6·64) at a recall rate of 3·80 (95% CI 3·67–3·93), whereas single reader replacement achieved a CDR of 6·49 (6·31–6·67), a recall rate of 4·01 (3·92–4·10), and a 49% workload reduction. Programme-level decision referral achieved a CDR of 6·85 (6·61–7·11), a recall rate of 3·55 (3·43–3·68), and an 84% workload reduction. Compared with the UK programme CDR of 8·19, the reader-level, programme-level, and deferral to single reader strategies achieved CDRs of 8·24 (7·82–8·71), 8·59 (8·12–9·06), and 8·28 (7·86–8·71), without increasing recall and while reducing workload by 37%, 81%, and 95%, respectively. On the Swedish dataset, programme-level decision referral increased the CDR by 17·7% without increasing recall and while reducing reading workload by 92%.</div></div><div><h3>Interpretation</h3><div>The decision referral strategies offered the largest improvements in cancer detection rates and reduction in recall rates, and all strategies except normal triaging showed potential to improve screening metrics.</div></div><div><h3>Funding</h3><div>Vara.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e803-e814"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510628","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 : 2024-10-23DOI: 10.1016/S2589-7500(24)00172-9
Arunashis Sau PhD , Libor Pastika MBBS , Ewa Sieliwonczyk PhD , Konstantinos Patlatzoglou PhD , Antônio H Ribeiro PhD , Kathryn A McGurk PhD , Boroumand Zeidaabadi BSc , Henry Zhang BSc , Krzysztof Macierzanka BSc , Prof Danilo Mandic PhD , Prof Ester Sabino MD , Luana Giatti PhD , Prof Sandhi M Barreto PhD , Lidyane do Valle Camelo PhD , Prof Ioanna Tzoulaki PhD , Prof Declan P O'Regan PhD , Prof Nicholas S Peters MD , Prof James S Ware PhD , Prof Antonio Luiz P Ribeiro PhD , Daniel B Kramer MD , Fu Siong Ng PhD
Background
Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.
Methods
The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients.
Findings
AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773–0·776; C-indices on external validation datasets 0·638–0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756–0·763; UKB C-index 0·719, 95% CI 0·635–0·803), future atherosclerotic cardiovascular disease (0·696, 0·694–0·698; 0·643, 0·624–0·662), and future heart failure (0·787, 0·785–0·789; 0·768, 0·733–0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome.
Interpretation
AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation.
Funding
British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.
{"title":"Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study","authors":"Arunashis Sau PhD , Libor Pastika MBBS , Ewa Sieliwonczyk PhD , Konstantinos Patlatzoglou PhD , Antônio H Ribeiro PhD , Kathryn A McGurk PhD , Boroumand Zeidaabadi BSc , Henry Zhang BSc , Krzysztof Macierzanka BSc , Prof Danilo Mandic PhD , Prof Ester Sabino MD , Luana Giatti PhD , Prof Sandhi M Barreto PhD , Lidyane do Valle Camelo PhD , Prof Ioanna Tzoulaki PhD , Prof Declan P O'Regan PhD , Prof Nicholas S Peters MD , Prof James S Ware PhD , Prof Antonio Luiz P Ribeiro PhD , Daniel B Kramer MD , Fu Siong Ng PhD","doi":"10.1016/S2589-7500(24)00172-9","DOIUrl":"10.1016/S2589-7500(24)00172-9","url":null,"abstract":"<div><h3>Background</h3><div>Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.</div></div><div><h3>Methods</h3><div>The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients.</div></div><div><h3>Findings</h3><div>AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773–0·776; C-indices on external validation datasets 0·638–0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756–0·763; UKB C-index 0·719, 95% CI 0·635–0·803), future atherosclerotic cardiovascular disease (0·696, 0·694–0·698; 0·643, 0·624–0·662), and future heart failure (0·787, 0·785–0·789; 0·768, 0·733–0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome.</div></div><div><h3>Interpretation</h3><div>AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation.</div></div><div><h3>Funding</h3><div>British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e791-e802"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510622","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}