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Attitudes and perceptions of medical researchers towards the use of artificial intelligence chatbots in the scientific process: an international cross-sectional survey. 医学研究人员对在科研过程中使用人工智能聊天机器人的态度和看法:一项国际横断面调查。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-11-15 DOI: 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.

聊天机器人是一种人工智能(AI)程序,旨在模拟与人类的对话,为科学研究带来了机遇和挑战。尽管出版机构对人工智能聊天机器人的使用越来越明确,但对研究人员的看法仍不甚了解。在这项国际横断面调查中,我们旨在评估研究人员对人工智能聊天机器人的态度、熟悉程度、感知到的益处和局限性。我们的在线调查于 2023 年 7 月 9 日至 8 月 11 日开放,从 PubMed 索引的 122 323 篇文章中确定了 61 560 位通讯作者。有 2452 人(4-0%)提供了回复,符合资格标准的 2292 人中有 2165 人(94-5%)完成了调查。2149名受访者中有1161人(54-0%)为男性,959人(44-6%)为女性。2138名受访者中有1294人(60-5%)熟悉人工智能聊天机器人,2125名受访者中有945人(44-5%)曾在研究中使用过人工智能聊天机器人。2137位受访者中只有244位(11-4%)报告了机构对人工智能工具的培训,2131位受访者中有211位(9-9%)指出了机构对人工智能聊天机器人使用的政策。尽管对人工智能聊天机器人的益处看法不一,但 2048 人中有 1428 人(69-7%)表示有兴趣接受进一步培训。尽管许多人重视人工智能聊天机器人减少行政工作量的作用(1941 人中有 1299 人[66-9%]),但对决策过程的了解却不够(1923 人中有 1484 人[77-2%])。总之,本研究强调了研究人员对人工智能聊天机器人的浓厚兴趣,但也指出需要对其使用进行更正式的培训和说明。
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引用次数: 0
Building robust, proportionate, and timely approaches to regulation and evaluation of digital mental health technologies. 建立健全、适度、及时的数字心理健康技术监管和评估方法。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-11-15 DOI: 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。
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引用次数: 0
Advancing the management of maternal, fetal, and neonatal infection through harnessing digital health innovations. 通过利用数字医疗创新,推进孕产妇、胎儿和新生儿感染的管理。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-11-14 DOI: 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.

发生在母亲和新生儿身上的感染给全世界造成了巨大的健康负担。优化感染管理对于改善个人治疗效果和降低抗菌药耐药性的发生率至关重要。数字医疗技术通过其可获取性和可扩展性,有望在不同的医疗环境中提高护理质量。在难以获得实验室服务的环境中,对现有数据、护理点诊断和可穿戴设备的创新使用可以更好地识别感染过程中的宿主反应并优化抗菌药物。信息的链接和连接可以支持医疗机构和社区之间协调提供护理服务。通过生物传感技术对母亲和新生儿的感染指标进行连续实时监测,可为干预和改善护理提供显著的机会。然而,在制定和实施这些干预措施时,应尊重他人,将安全放在首位,并强调可持续的、源自当地的解决方案。解决目前存在的性别、经济和医疗保健方面的差异对于确保公平实施至关重要。
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引用次数: 0
Using digital health to address antimicrobial resistance. 利用数字健康技术解决抗菌药耐药性问题。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-11-14 DOI: 10.1016/S2589-7500(24)00251-6
The Lancet Digital Health
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引用次数: 0
Using digital health technologies to optimise antimicrobial use globally. 利用数字医疗技术在全球范围内优化抗菌药物的使用。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-11-14 DOI: 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。
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引用次数: 0
Innovative diagnostic technologies: navigating regulatory frameworks through advances, challenges, and future prospects. 创新诊断技术:通过进步、挑战和未来前景驾驭监管框架。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-11-14 DOI: 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.

诊断工具是指导病人管理和为控制传染病的公共卫生政策提供信息的关键。然而,许多疾病仍然没有有效的诊断方法,全球大部分人口难以获得可靠、负担得起的检测。这种限制凸显了创新的迫切需要,以提高诊断的可用性和有效性。开发诊断技术面临着独特的挑战,尤其是对创新者和监管者而言。与药品不同,诊断产品的监管途径往往不太明确,而且由于其风险特征各异、产品种类繁多而更加复杂。这些挑战在低收入和中等收入国家更为严峻,因为这些国家往往没有针对这一特定目的的监管框架。在英国,相关举措旨在通过提供更清晰的监管途径来支持创新,并确保诊断产品安全有效。监管机构也在开展国际合作,以加快高需求地区的诊断工作。统一的标准、监管框架和审批流程对于确保各地区质量和安全性的一致性、促进更快的开发和全球普及至关重要。本系列文件探讨了传染病和抗菌药耐药性诊断方面的监管挑战,重点介绍了英国的应对措施以及全球为解决这些问题所做的广泛努力。
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引用次数: 0
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 英格兰不同社会人口群体的 COVID-19 检测和报告行为:一项利用检测数据和社区流行病监测调查数据进行的人口研究。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 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
背景:了解传染病爆发期间寻求检测和报告行为异质性的内在机制有助于保护易感人群并指导以公平为导向的干预措施。COVID-19 大流行可能对不同社会人口群体的个人造成了不同的压力,而确保公平获得和使用 COVID-19 检测是英格兰检测计划的关键要素。我们旨在调查 COVID-19 大流行期间英格兰社会人口因素与 COVID-19 检测行为之间的关系:我们利用 2020 年 10 月 1 日至 2022 年 3 月 30 日期间英格兰的大规模 COVID-19 检测数据和社区流行率监测调查(REACT-1 和 ONS-CIS)数据,对 COVID-19 检测行为进行了基于人群的研究。我们使用了面向公众的侧流装置(LFD;已进行并报告的约 2.9 亿次检测数据)和 PCR(已进行并从实验室返回的约 1.07 亿次检测数据)检测的大规模检测数据,这些数据按日期和自我报告的年龄和种族在下级地方当局(LTLA)一级提供。我们还使用了可公开获得的单个低级别地方当局的平均人口规模估计数据,以及低级别地方当局的种族群体、年龄组和贫困指数数据。我们无法获得按性别分列的 REACT-1 或 ONS-CIS 患病率数据。我们利用机理因果模型对 PCR 检测数据进行去伪存真,得出了英格兰长期病患按自我报告的种族群体和年龄组别分列的每周 SARS-CoV-2 流行率估计值。这种对 PCR(或 LFD)检测数据去伪存真的方法还估算出了检测偏差参数,该参数被定义为感染者与未感染者的检测几率,如果无论感染状况如何,寻求检测(或寻求检测并报告)的几率相同,则该参数接近零。通过 PCR 确证数据,我们估算了假阳性率、灵敏度、特异性,以及按社会人口组别分列的 PCR 检测报告 LFD 阳性后的检测概率下降率。我们还估算了每天的发病率,从而计算出检测计划捕获的病例比例:从 2021 年 3 月起,最贫困地区的人均 LFD 检测次数约为最不贫困地区的一半(中位数比率为 0-50 [IQR为 0-44-0-54])。在 2020 年 10 月至 2021 年 6 月期间,PCR 检测模式呈现出相反的趋势,最贫困地区的人均 PCR 检测次数几乎是最不贫困地区的两倍(1-8 [1-7-1-9])。在阿尔法(B.1.1.7)和奥米克隆(B.1.1.529)BA.1 波中,亚裔或亚裔英国人的感染率大大高于其他种族群体。我们的估计结果表明,在研究期间,英格兰第二支柱部门 COVID-19 检测项目发现了 26-40% 的病例(包括无症状病例),不同贫困水平或种族群体之间没有一致的差异。PCR 的检测偏倚通常高于 LFD,这与无症状和无症状使用这些检测方法的总体政策一致。贫困程度和年龄与平均检测偏差有关;不过,不同贫困程度的不确定区间有所重叠,但特定年龄的模式更为明显。我们还发现,在疫情的大部分时间里,少数民族和老年人不太可能使用 PCR 确证检测,而在自称为 "黑人、非洲人、英国黑人或加勒比海人 "的人群中,报告 LFD 检测阳性的延迟时间可能更长:不同社会人口群体在检测行为上的差异可能反映了弱势人群自我隔离的成本较高、检测可及性的差异、数字扫盲的差异以及对检测效用和感染风险的不同认识。这项研究展示了如何将大规模检测数据与监测调查结合起来使用,以确定公共卫生干预措施在细微层面和不同社会人口群体中的吸收差距。它为监测地方干预措施提供了一个框架,并为政策制定者提供了宝贵的经验,以确保公平应对未来的流行病:资金来源:英国卫生安全局。
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引用次数: 0
Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development—a systematic review 揭示用于医学人工智能开发的 COVID-19 公开数据集的透明度差距--系统综述。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 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.
在 COVID-19 大流行期间,人们创建了人工智能(AI)模型来解决医疗资源紧张的问题。以往的研究表明,医疗数据集往往存在局限性,从而导致人工智能技术出现偏差。本系统性综述评估了大流行期间用于人工智能开发的数据集,发现了一些不足之处。数据集是通过筛选MEDLINE上的文章和使用谷歌数据集搜索确定的。对 192 个数据集的元数据完整性、组成、数据可访问性和伦理因素进行了分析。研究结果显示存在很大差距:只有 48% 的数据集记录了个人的原籍国,43% 的数据集报告了年龄,不到 25% 的数据集包含性、性别、种族或民族。数据标签、伦理审查或同意书方面的信息经常缺失。许多数据集重复使用了可追溯性不足的数据。值得注意的是,一些数据集中出现了历史性的儿科胸部 X 光片,但并未注明。这些缺陷凸显了提高数据质量和文档透明度的必要性,以降低在未来的突发卫生事件中开发出有偏见的人工智能模型的风险。
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引用次数: 0
Strategies for integrating artificial intelligence into mammography screening programmes: a retrospective simulation analysis 将人工智能融入乳腺 X 射线摄影筛查计划的策略:回顾性模拟分析。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 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.

Funding

Vara.
背景:将人工智能(AI)整合到乳腺 X 射线摄影筛查中可以为放射科医生提供支持并改善项目指标,但不同技术整合策略的潜力仍未得到充分研究。我们比较了七种人工智能整合策略的项目级绩效指标:我们使用德国(n=1 657 068)、英国(n=223 603)和瑞典(n=22 779)筛查项目中生成的数据集,对将人工智能整合到乳腺放射摄影筛查中的七种策略进行了回顾性比较评估。使用的商用人工智能模型是 Vara 2.10 版,该模型是在德国数据基础上从头开始训练的。我们模拟了每种策略在癌症检出率 (CDR)、召回率和工作量减少方面的表现,并将这些指标与筛查计划的指标进行了比较。我们还评估了每种策略检测出的癌症的分期和等级分布情况,以及人工智能模型对这些癌症进行正确定位的能力:与德国筛查计划(每 1000 次检查的 CDR 为 6-32,每 100 次检查的召回率为 4-11)相比,更换两名读片员(独立人工智能策略)的 CDR 为 6-37(95% CI 6-10-6-64),召回率为 3-80(95% CI 3-67-3-93);而更换一名读片员的 CDR 为 6-49(6-31-6-67),召回率为 4-01(3-92-4-10),工作量减少了 49%。计划级决策转介的 CDR 为 6-85 (6-61-7-11),召回率为 3-55 (3-43-3-68),工作量减少了 84%。与英国方案 8-19 的 CDR 相比,读者级、方案级和推迟到单个读者策略的 CDR 分别为 8-24 (7-82-8-71)、8-59 (8-12-9-06) 和 8-28 (7-86-8-71),召回率没有增加,工作量分别减少了 37%、81% 和 95%。在瑞典数据集上,程序级决策转介将 CDR 提高了 17-7%,但召回率并未提高,同时阅读工作量减少了 92%:决策转诊策略对癌症检出率和召回率的改善最大,除正常分流外,所有策略都显示出改善筛查指标的潜力:资助:瓦拉
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引用次数: 0
Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study 人工智能心电图用于死亡率和心血管风险评估:模型开发和验证研究。
IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Pub Date : 2024-10-23 DOI: 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.
背景:人工智能(AI)支持的心电图(ECG)可用于预测未来疾病和死亡风险,但尚未被临床实践所采用。现有的模型预测不具备个体患者层面的可操作性、可解释性或生物合理性。我们试图通过开发人工智能心电图风险估算器(AIRE)平台来解决以往人工智能心电图方法的这些局限性:AIRE 平台是在一个二级医疗数据集(贝斯以色列女执事医疗中心 [BIDMC])中开发的,该数据集包含来自 189 539 名患者的 1 163 401 张心电图,利用深度学习和离散时间生存模型,通过单张心电图创建患者特异性生存曲线。因此,AIRE 不仅能预测死亡风险,还能预测死亡时间。AIRE 在来自美国、巴西和英国(UK Biobank [UKB])的五个不同的跨国队列中进行了验证,包括志愿者、初级保健患者和二级保健患者:未来的动脉粥样硬化性心血管疾病(0-696,0-694-0-698;0-643,0-624-0-662)和未来的心力衰竭(0-787,0-785-0-789;0-768,0-733-0-802)。通过全表型和全基因组关联研究,我们确定了预测风险增加的候选生物通路,包括心脏结构和功能的变化,以及与心脏结构、生物老化和代谢综合征相关的基因:AIRE 是一个可操作、可解释、生物学上合理的 AI-ECG 风险评估平台,有望在全球广泛的临床环境中用于短期和长期风险评估:资金来源:英国心脏基金会、国家健康与护理研究所和医学研究委员会。
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Lancet Digital Health
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