Lancet Digital Health最新文献

{"title":"In the era of digitalisation and biosignatures, is C-reactive protein still the one to beat? – Authors' reply","authors":"Myrsini Kaforou , Heather R Jackson , Taco W Kuijpers , Marien I de Jonge , Michael Levin","doi":"10.1016/S2589-7500(24)00178-X","DOIUrl":"10.1016/S2589-7500(24)00178-X","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e678-e679"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320338","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}

{"title":"Correction to Lancet Digit Health 2024; 6: e755–66","authors":"","doi":"10.1016/S2589-7500(24)00201-2","DOIUrl":"10.1016/S2589-7500(24)00201-2","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Page e680"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320339","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}

{"title":"Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist","authors":"Yilin Ning PhD ,&nbsp;Salinelat Teixayavong BSS ,&nbsp;Yuqing Shang MSc ,&nbsp;Prof Julian Savulescu PhD ,&nbsp;Vaishaanth Nagaraj ,&nbsp;Di Miao MSc ,&nbsp;Mayli Mertens PhD ,&nbsp;Daniel Shu Wei Ting PhD ,&nbsp;Jasmine Chiat Ling Ong PharmD ,&nbsp;Mingxuan Liu MSc ,&nbsp;Prof Jiuwen Cao PhD ,&nbsp;Michael Dunn PhD ,&nbsp;Prof Roger Vaughan PhD ,&nbsp;Prof Marcus Eng Hock Ong MPH ,&nbsp;Prof Joseph Jao-Yiu Sung MD ,&nbsp;Prof Eric J Topol MD ,&nbsp;Nan Liu PhD","doi":"10.1016/S2589-7500(24)00143-2","DOIUrl":"10.1016/S2589-7500(24)00143-2","url":null,"abstract":"<div><div>The widespread use of Chat Generative Pre-trained Transformer (known as ChatGPT) and other emerging technology that is powered by generative artificial intelligence (GenAI) has drawn attention to the potential ethical issues they can cause, especially in high-stakes applications such as health care, but ethical discussions have not yet been translated into operationalisable solutions. Furthermore, ongoing ethical discussions often neglect other types of GenAI that have been used to synthesise data (eg, images) for research and practical purposes, which resolve some ethical issues and expose others. We did a scoping review of the ethical discussions on GenAI in health care to comprehensively analyse gaps in the research. To reduce the gaps, we have developed a checklist for comprehensive assessment and evaluation of ethical discussions in GenAI research. The checklist can be integrated into peer review and publication systems to enhance GenAI research and might be useful for ethics-related disclosures for GenAI-powered products and health-care applications of such products and beyond.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e848-e856"},"PeriodicalIF":23.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142251498","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}

{"title":"Correction to Lancet Digit Health 2024; 6: e281–90","authors":"","doi":"10.1016/S2589-7500(24)00195-X","DOIUrl":"10.1016/S2589-7500(24)00195-X","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Page e680"},"PeriodicalIF":23.8,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134234","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}

{"title":"Digital technology and new care pathways will redefine the cardiovascular workforce","authors":"Virimchi Pillutla ,&nbsp;Adam B Landman ,&nbsp;Jagmeet P Singh","doi":"10.1016/S2589-7500(24)00193-6","DOIUrl":"10.1016/S2589-7500(24)00193-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e674-e676"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113571","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}

{"title":"Promises and challenges of digital tools in cardiovascular care","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00194-8","DOIUrl":"10.1016/S2589-7500(24)00194-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Page e673"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113574","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}

{"title":"Digital tools in heart failure: addressing unmet needs","authors":"Prof Peder L Myhre MD PhD ,&nbsp;Jasper Tromp MD PhD ,&nbsp;Wouter Ouwerkerk MD PhD ,&nbsp;Daniel S W Ting MD PhD ,&nbsp;Kieran F Docherty MD PhD ,&nbsp;Prof C Michael Gibson MS MD ,&nbsp;Prof Carolyn S P Lam MD PhD","doi":"10.1016/S2589-7500(24)00158-4","DOIUrl":"10.1016/S2589-7500(24)00158-4","url":null,"abstract":"<div><div>This Series paper provides an overview of digital tools in heart failure care, encompassing screening, early diagnosis, treatment initiation and optimisation, and monitoring, and the implications these tools could have for research. The current medical environment favours the implementation of digital tools in heart failure due to rapid advancements in technology and computing power, unprecedented global connectivity, and the paradigm shift towards digitisation. Despite available effective therapies for heart failure, substantial inadequacies in managing the condition have hindered improvements in patient outcomes, particularly in low-income and middle-income countries. As digital health tools continue to evolve and exert a growing influence on both clinical care and research, establishing clinical frameworks and supportive ecosystems that enable their effective use on a global scale is crucial.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e755-e766"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113572","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}

{"title":"Challenges for augmenting intelligence in cardiac imaging","authors":"Prof Partho P Sengupta MD ,&nbsp;Prof Damini Dey PhD ,&nbsp;Rhodri H Davies PhD ,&nbsp;Nicolas Duchateau PhD ,&nbsp;Naveena Yanamala PhD","doi":"10.1016/S2589-7500(24)00142-0","DOIUrl":"10.1016/S2589-7500(24)00142-0","url":null,"abstract":"<div><div>Artificial Intelligence (AI), through deep learning, has brought automation and predictive capabilities to cardiac imaging. However, despite considerable investment, tangible health-care cost reductions remain unproven. Although AI holds promise, there has been insufficient time for both methodological development and prospective clinical trials to establish its advantage over human interpretations in terms of its effect on patient outcomes. Challenges such as data scarcity, privacy issues, and ethical concerns impede optimal AI training. Furthermore, the absence of a unified model for the complex structure and function of the heart and evolving domain knowledge can introduce heuristic biases and influence underlying assumptions in model development. Integrating AI into diverse institutional picture archiving and communication systems and devices also presents a clinical hurdle. This hurdle is further compounded by an absence of high-quality labelled data, difficulty sharing data between institutions, and non-uniform and inadequate gold standards for external validations and comparisons of model performance in real-world settings. Nevertheless, there is a strong push in industry and academia for AI solutions in medical imaging. This Series paper reviews key studies and identifies challenges that require a pragmatic change in the approach for using AI for cardiac imaging, whereby AI is viewed as augmented intelligence to complement, not replace, human judgement. The focus should shift from isolated measurements to integrating non-linear and complex data towards identifying disease phenotypes—emphasising pattern recognition where AI excels. Algorithms should enhance imaging reports, enriching patients' understanding, communication between patients and clinicians, and shared decision making. The emergence of professional standards and guidelines is essential to address these developments and ensure the safe and effective integration of AI in cardiac imaging.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e739-e748"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113569","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}

{"title":"Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study","authors":"Betül Toprak MD ,&nbsp;Hugo Solleder PhD ,&nbsp;Eleonora Di Carluccio MSc ,&nbsp;Jaimi H Greenslade PhD ,&nbsp;Prof William A Parsonage MD ,&nbsp;Karen Schulz DC ,&nbsp;Prof Louise Cullen MD ,&nbsp;Prof Fred S Apple PhD ,&nbsp;Prof Andreas Ziegler PhD ,&nbsp;Prof Stefan Blankenberg MD","doi":"10.1016/S2589-7500(24)00191-2","DOIUrl":"10.1016/S2589-7500(24)00191-2","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Point-of-care (POC) high-sensitivity cardiac troponin (hs-cTn) assays have been shown to provide similar analytical precision despite substantially shorter turnaround times compared with laboratory-based hs-cTn assays. We applied the previously developed machine learning based personalised Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) algorithm, which can predict the individual probability of myocardial infarction, with a single POC hs-cTn measurement, and compared its diagnostic performance with standard-of-care pathways for rapid rule-out of myocardial infarction.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We retrospectively analysed pooled data from consecutive patients of two prospective observational cohorts in geographically distinct regions (the Safe Emergency Department Discharge Rate cohort from the USA and the Suspected Acute Myocardial Infarction in Emergency cohort from Australia) who presented to the emergency department with suspected myocardial infarction. Patients with ST-segment elevation myocardial infarction were excluded. Safety and efficacy of direct rule-out of myocardial infarction by the ARTEMIS algorithm (at a pre-specified probability threshold of &lt;0·5%) were compared with the European Society of Cardiology (ESC)-recommended and the American College of Cardiology (ACC)-recommended 0 h pathways using a single POC high-sensitivity cardiac troponin I (hs-cTnI) measurement (Siemens Atellica VTLi as investigational assay). The primary diagnostic outcome was an adjudicated index diagnosis of type 1 or type 2 myocardial infarction according to the Fourth Universal Definition of Myocardial Infarction. The safety outcome was a composite of incident myocardial infarction and cardiovascular death (follow-up events) at 30 days. Additional analyses were performed for type I myocardial infarction only (secondary diagnostic outcome), and for each cohort separately. Subgroup analyses were performed for age (&lt;65 years &lt;em&gt;vs&lt;/em&gt; ≥65 years), sex, symptom onset (≤3 h &lt;em&gt;vs&lt;/em&gt; &gt;3 h), estimated glomerular filtration rate (&lt;60 mL/min per 1·73 m&lt;sup&gt;2&lt;/sup&gt; &lt;em&gt;vs&lt;/em&gt; ≥60 mL/min per 1·73 m&lt;sup&gt;2&lt;/sup&gt;), and absence or presence of arterial hypertension, diabetes, a history of coronary artery disease, myocardial infarction, or heart failure, smoking, and ischaemic electrocardiogram signs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Among 2560 patients (1075 [42%] women, median age 58 years [IQR 48·0–69·0]), prevalence of myocardial infarction was 6·5% (166/2560). The ARTEMIS-POC algorithm classified 899 patients (35·1%) as suitable for rapid rule-out with a negative predictive value of 99·96% (95% CI 99·64–99·96) and a sensitivity of 99·68% (97·21–99·70). For type I myocardial infarction only, negative predictive value and sensitivity were both 100%. Proportions of missed index myocardial infarction (0·05% [0·04–0·42]) and follow-up events at 30 days (0·07% [95% CI 0·06–0·59]) were l","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e729-e738"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113570","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}

{"title":"The potential for large language models to transform cardiovascular medicine","authors":"Giorgio Quer PhD ,&nbsp;Prof Eric J Topol MD","doi":"10.1016/S2589-7500(24)00151-1","DOIUrl":"10.1016/S2589-7500(24)00151-1","url":null,"abstract":"<div><div>Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early diagnosis and prediction of occurrence of these diseases. Deep neural networks can improve the accuracy of medical image interpretation and their outputs can provide rich information that otherwise would not be detected by cardiologists. With recent advances in transformer models, multimodal AI, and large language models, the ability to integrate electronic health record data with images, genomics, biosensors, and other data has the potential to improve diagnosis and partition patients who are at high risk for primary preventive strategies. Although much emphasis has been placed on AI supporting clinicians, AI can also serve patients and provide immediate help with diagnosis, such as that of arrhythmia, and is being studied for automated self-imaging. Potential risks, such as loss of data privacy or potential diagnostic errors, should be addressed before use in clinical practice. This Series paper explores opportunities and limitations of AI models for cardiovascular medicine, and aims to identify specific barriers to and solutions in the application of AI models, facilitating their integration into health-care systems.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e767-e771"},"PeriodicalIF":23.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113575","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}