Lancet Digital Health最新文献

{"title":"Navigating the landscape of medical artificial intelligence reporting guidelines.","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100925","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100925","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100925"},"PeriodicalIF":24.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276446","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}

{"title":"How can artificial intelligence transform the training of medical students and physicians?","authors":"Yilin Ning, Jasmine Chiat Ling Ong, Haoran Cheng, Haibo Wang, Daniel Shu Wei Ting, Yih Chung Tham, Tien Yin Wong, Nan Liu","doi":"10.1016/j.landig.2025.100900","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100900","url":null,"abstract":"<p><p>Advances in artificial intelligence (AI), particularly generative AI, hold promise for transforming medical education and physician training in response to increasing health-care demands and shortages in the global health-care workforce. Meanwhile, challenges remain in the effective and equitable integration of AI technology into medical education and physician training worldwide. This Viewpoint explores the opportunities and challenges of such an integration. We study the evolving role of AI in medical education, its potential to enhance high-fidelity clinical training, and its contribution to research training using real-world examples. We also highlight ethical concerns, particularly the unclear boundaries of appropriate use of AI and call for clear guidelines to govern the integration of AI into medical education and physician training. Furthermore, this Viewpoint discusses practical constraints, including human, financial, and resource constraints, in AI integration, and emphasises the need for comprehensive cost evaluations and collaborative funding models to support the sustainable implementation of AI integration. A tight collaborative network between health-care institutions and systems, medical schools and universities, industry partners, and education and health-care regulatory agencies could lead to an AI-transformed medical education and physician training scheme that ultimately supports the adoption and integration of AI into clinical medicine and potentially brings about tangible improvements in global health-care delivery.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100900"},"PeriodicalIF":24.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145233948","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}

{"title":"Digital adherence technology interventions to reduce poor end-of-treatment outcomes and recurrence among adults with drug-sensitive tuberculosis in Ethiopia: a three-arm, pragmatic, cluster-randomised, controlled trial.","authors":"Amare W Tadesse, Mamush Sahile, Nicola Foster, Christopher Finn McQuaid, Gedion Teferra Weldemichael, Tofik Abdurhman, Zemedu Mohammed, Mahilet Belachew, Amanuel Shiferaw, Demelash Assefa, Demekech Gadissa, Hiwot Yazew, Nuria Yakob, Zewdneh Shewamene, Lara Goscé, Job van Rest, Norma Madden, Salome Charalambous, Kristian van Kalmthout, Ahmed Bedru, Taye Letta, Degu Jerene, Katherine L Fielding","doi":"10.1016/j.landig.2025.100895","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100895","url":null,"abstract":"<p><strong>Background: </strong>The effect of digital adherence technologies (DATs) on long-term tuberculosis treatment outcomes remains unclear. We aimed to assess the effectiveness of DATs in improving tuberculosis treatment outcomes and recurrence.</p><p><strong>Methods: </strong>We did a pragmatic cluster-randomised trial in Ethiopia. 78 health facilities (clusters) were randomised (1:1:1) to smart pillbox, medication labels, or standard of care. Adults aged 18 years or older with drug-sensitive pulmonary tuberculosis on a fixed-dose combination tuberculosis treatment regimen were enrolled and followed up for 12 months after treatment initiation. Those in the smart pillbox group received a pillbox with customisable audio-visual reminders, whereas participants in the medications label group received their tuberculosis medication with a weekly unique code label. Opening the box or texting the code prompted real-time dose logging on the adherence platform, facilitating differentiated response to an individual's adherence by a health-care worker. The primary composite outcome comprised death, loss to follow-up, treatment failure, switch to drug-resistant tuberculosis treatment, or recurrence. Secondary outcomes were poor end-of-treatment outcome and loss to follow-up. Analysis accounted for clustered design with multiple imputation for the primary composite outcome. The trial is registered with Pan African Clinical Trials Registry (PACTR202008776694999) and is complete.</p><p><strong>Findings: </strong>From May 24, 2021, to Aug 8, 2022, 8477 individuals undergoing tuberculosis treatment were assessed for eligibility. Of the 3885 participants enrolled, 3858 were included in the intention-to-treat population. 1567 (40·6%) of 3858 participants were women and the median age of all participants was 30 years (IQR 24-40). At 12 months, using multiple imputation, neither the smart pillbox group (adjusted odds ratio [OR] 1·04 [95% CI 0·74 to 1·45]; adjusted risk difference: 0·96 percentage points [95% CI -1·19 to 3·11]) nor the medication labels group (adjusted OR 1·14 [0·83 to 1·61]; adjusted risk difference: 0·42 percentage points [-1·75 to 2·59]) reduced the risk of the primary composite outcome. There was no evidence of effect on poor end-of-treatment outcomes or loss to follow-up in either intervention group, although the label intervention showed weak evidence of reduced loss to follow-up. Results were similar in complete case and per-protocol analyses.</p><p><strong>Interpretation: </strong>The DAT interventions showed no reduction in unfavourable outcomes. This emphasises the necessity to optimise DATs to enhance tuberculosis management strategies and treatment outcomes.</p><p><strong>Funding: </strong>Unitaid.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100895"},"PeriodicalIF":24.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201808","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}

{"title":"Causal deep learning for real-time detection of cardiac surgery-associated acute kidney injury: derivation and validation in seven time-series cohorts.","authors":"Qin Zhong, Yuxiao Cheng, Zongren Li, Dongjin Wang, Chongyou Rao, Yi Jiang, Lianglong Li, Ziqian Wang, Pan Liu, Hebin Che, Pei Li, Xin Lu, Jinli Suo, Kunlun He","doi":"10.1016/j.landig.2025.100901","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100901","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Cardiac surgery-associated acute kidney injury (CSA-AKI) is a complex complication substantially contributing to an increased risk of mortality. Effective CSA-AKI management relies on timely diagnosis and interventions. However, many cases are detected too late. Despite the advancements in novel biomarkers and data-driven predictive models, existing practices are primarily constrained due to the limited discriminative and generalisation capabilities and stringent application requirements, presenting major challenges to the timely and effective diagnosis and interventions in CSA-AKI management. This study aimed to develop a causal deep learning architecture, named REACT, to achieve precise and dynamic predictions of CSA-AKI within the subsequent 48 h.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;In this retrospective model development and prospective validation study, we included adult patients (aged ≥18 years) from seven distinct cohorts undergoing major open-heart surgery for model training and validation. Data for model development and internal validation were sourced from electronic health records of two large centres in Beijing, China, between Jan 1, 2000, and Dec 31, 2022. External validation was conducted on three independent centres in China between Jan 1, 2000, and Dec 31, 2022, along with cross-national data from the public databases MIMIC-IV and eICU in the USA. To facilitate implementation, we also developed a publicly accessible web calculator and applet. The model's prospective application was validated from June 1, to Oct 31, 2023, at two centres in Beijing and Nanjing, China.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;The final derivation cohort included 14 513 eligible patients with a median age of 56 years (IQR 45-65), 5515 (38·0%) patients were female, and 3047 (21·0%) developed CSA-AKI. The external validation dataset included 20 813 patients from China and 28 023 from the USA. REACT reduced 1328 input variables to six essential causal factors for CSA-AKI prediction. In internal validation, REACT achieved an average area under the receiver operating characteristic curve (AUROC) of 0·930 (SD 0·032), outperforming state-of-the-art deep learning architectures, specifically transformer-based and long short-term memory-based models, which rely on more complex variables. The model consistently outperformed in external validation across different centres (average AUROC 0·920 [SD 0·036]) and regions (0·867 [0·073]), as well as in prospective validation (0·896 [0·023]). Compared with guideline-recommended pathways, REACT detected CSA-AKI on average 16·35 h (SD 2·01) earlier in external validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Interpretation: &lt;/strong&gt;We proposed a causal deep learning approach to predict CSA-AKI risk within 48 h, distilling the complex temporal interactions between variables into only a few universal, relatively cost-effective inputs. The approach shows great potential for deployment across hospitals with minimum data require","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100901"},"PeriodicalIF":24.1,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151393","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}

{"title":"Development and external validation of a clinical prediction model for new-onset atrial fibrillation in intensive care: a multicentre, retrospective cohort study.","authors":"Jonathan P Bedford, Oliver Redfern, Stephen Gerry, Robert Hatch, Liza Keating, David Clifton, Gary S Collins, Peter J Watkinson","doi":"10.1016/j.landig.2025.100896","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100896","url":null,"abstract":"<p><strong>Background: </strong>New-onset atrial fibrillation, a condition associated with adverse outcomes in the short and long term, is common in patients admitted to intensive care units (ICUs). Identifying patients at high risk could inform trials of preventive interventions and help to target such interventions. We aimed to develop and externally validate a prediction model for new-onset atrial fibrillation in patients admitted to ICUs.</p><p><strong>Methods: </strong>We conducted a multicentre, retrospective cohort study in three ICUs across the UK and four ICUs across the USA. Patients aged 16 years and older admitted to an ICU for more than 3 h without a history or presentation of clinically significant arrhythmia were eligible for inclusion. We analysed clinical variables to investigate the associations between predetermined candidate variables and risk of new-onset atrial fibrillation and to develop a model to estimate these risks. We developed the METRIC-AF model, a machine learning model incorporating dynamic variables. Model performance was assessed through internal-external cross-validation during model development and externally validated by use of multicentre data from ICUs across the UK. We then developed a simple graphical prediction tool using three important predictors.</p><p><strong>Findings: </strong>Among 39 084 eligible patients admitted to an ICU between 2008 and 2019, 2797 (7·2%) developed new-onset atrial fibrillation during the first 7 days of their ICU stay. We identified multiple non-linear associations between candidate variables and risk of new-onset atrial fibrillation, including hypomagnesaemia at serum concentrations below 0·70 mmol/L. The final METRIC-AF model contained ten routinely collected clinical variables. Compared with a published logistic regression model, the METRIC-AF model showed superior calibration, net benefit across clinically relevant risk thresholds, and discriminative performance (C statistic 0·812 [95% CI 0·805-0·822] vs 0·786 [0·778-0·801]; p=0·0003). The simple graphical tool performed well in attributing the risk of new-onset atrial fibrillation in the external validation dataset (C statistic 0·727 [95% CI 0·716-0·739]).</p><p><strong>Interpretation: </strong>The METRIC-AF model and its companion graphical tool could support the identification of patients at increased risk of developing new-onset atrial fibrillation during ICU admission, informing targeted prophylactic strategies and trial enrichment by use of routinely available clinical data. An online app also developed as part of the study allows for the exploration of prediction generation among individuals and external validation in prospective studies.</p><p><strong>Funding: </strong>National Institute for Health and Care Research (NIHR) and NIHR Oxford Biomedical Research Centre.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100896"},"PeriodicalIF":24.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034409","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}

{"title":"Computer-aided reading of chest radiographs for paediatric tuberculosis: current status and future directions.","authors":"Mackenzie DuPont, Robert Castro, Sandra V Kik, Megan Palmer, James A Seddon, Devan Jaganath","doi":"10.1016/j.landig.2025.100884","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100884","url":null,"abstract":"<p><p>Computer-aided detection (CAD) systems for automated reading of chest x-rays (CXRs) have been developed and approved for tuberculosis triage in adults but not in children. However, CXR is frequently the only adjunctive tool for clinical assessment in the evaluation of paediatric tuberculosis in primary care settings, and children would benefit from CAD models that can detect their unique clinical and radiographic features. To advance CAD for childhood tuberculosis, large, diverse paediatric CXR datasets linked to standardised tuberculosis classifications are required. These datasets would be used to train and validate paediatric-specific models for tuberculosis screening, diagnosis, and severity stratification. Previous studies on CAD algorithms for reading paediatric CXRs have highlighted promising approaches, including the use of transfer learning with existing deep learning models. Including data from children in CAD models is essential to improve equity and reduce the global burden of tuberculosis disease.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100884"},"PeriodicalIF":24.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974477","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}

{"title":"How CHART (Chatbot Assessment Reporting Tool) can help to advance clinical artificial intelligence research through clearer task definition and robust validation.","authors":"Arun James Thirunavukarasu, Ernest Lim, Bright Huo","doi":"10.1016/j.landig.2025.100910","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100910","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100910"},"PeriodicalIF":24.1,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974481","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}

{"title":"Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions.","authors":"Bardia Khosravi, Saptarshi Purkayastha, Bradley J Erickson, Hari M Trivedi, Judy W Gichoya","doi":"10.1016/j.landig.2025.100890","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100890","url":null,"abstract":"<p><p>Generative artificial intelligence has emerged as a transformative force in medical imaging since 2022, enabling the creation of derivative synthetic datasets that closely resemble real-world data. This Viewpoint examines key aspects of synthetic data, focusing on its advancements, applications, and challenges in medical imaging. Various generative artificial intelligence image generation paradigms, such as physics-informed and statistical models, and their potential to augment and diversify medical research resources are explored. The promises of synthetic datasets, including increased diversity, privacy preservation, and multifunctionality, are also discussed, along with their ability to model complex biological phenomena. Next, specific applications using synthetic data such as enhancing medical education, augmenting rare disease datasets, improving radiology workflows, and enabling privacy-preserving multicentre collaborations are highlighted. The challenges and ethical considerations surrounding generative artificial intelligence, including patient privacy, data copying, and potential biases that could impede clinical translation, are also addressed. Finally, future directions for research and development in this rapidly evolving field are outlined, emphasising the need for robust evaluation frameworks and responsible utilisation of generative artificial intelligence in medical imaging.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100890"},"PeriodicalIF":24.1,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859803","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}

{"title":"Value of artificial intelligence in neuro-oncology.","authors":"Sebastian Voigtlaender, Thomas A Nelson, Philipp Karschnia, Eugene J Vaios, Michelle M Kim, Philipp Lohmann, Norbert Galldiks, Mariella G Filbin, Shekoofeh Azizi, Vivek Natarajan, Michelle Monje, Jorg Dietrich, Sebastian F Winter","doi":"10.1016/j.landig.2025.100876","DOIUrl":"10.1016/j.landig.2025.100876","url":null,"abstract":"<p><p>CNS cancers are complex, difficult-to-treat malignancies that remain insufficiently understood and mostly incurable, despite decades of research efforts. Artificial intelligence (AI) is poised to reshape neuro-oncological practice and research, driving advances in medical image analysis, neuro-molecular-genetic characterisation, biomarker discovery, therapeutic target identification, tailored management strategies, and neurorehabilitation. This Review examines key opportunities and challenges associated with AI applications along the neuro-oncological care trajectory. We highlight emerging trends in foundation models, biophysical modelling, synthetic data, and drug development and discuss regulatory, operational, and ethical hurdles across data, translation, and implementation gaps. Near-term clinical translation depends on scaling validated AI solutions for well defined clinical tasks. In contrast, more experimental AI solutions offer broader potential but require technical refinement and resolution of data and regulatory challenges. Addressing both general and neuro-oncology-specific issues is essential to unlock the full potential of AI and ensure its responsible, effective, and needs-based integration into neuro-oncological practice.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100876"},"PeriodicalIF":24.1,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12338051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144812596","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 health interventions for mental health disorders: an umbrella review of meta-analyses of randomised controlled trials","authors":"Cristina Crocamo PhD ,&nbsp;Dario Palpella MD ,&nbsp;Daniele Cavaleri MD ,&nbsp;Christian Nasti MD ,&nbsp;Susanna Piacenti MD ,&nbsp;Pietro Morello MD ,&nbsp;Giada Lauria MD ,&nbsp;Oliviero Villa MD ,&nbsp;Ilaria Riboldi PhD ,&nbsp;Francesco Bartoli PhD ,&nbsp;John Torous MD ,&nbsp;Prof Giuseppe Carrà PhD","doi":"10.1016/j.landig.2025.100878","DOIUrl":"10.1016/j.landig.2025.100878","url":null,"abstract":"<div><div>Digital health interventions (DHIs) show promise for the treatment of mental health disorders. However, existing meta-analytical research is methodologically heterogeneous, with studies including a mix of clinical, non-clinical, and transdiagnostic populations, hindering a comprehensive understanding of DHI effectiveness. Thus, we conducted an umbrella review of meta-analyses of randomised controlled trials investigating the effectiveness of DHIs for specific mental health disorders and evaluating the quality of evidence. We searched three public electronic databases from inception to February, 2024 and included 16 studies. DHIs were effective compared with active interventions for schizophrenia spectrum disorders, major depressive disorder, social anxiety disorder, and panic disorder. Notable treatment effects compared with a waiting list were also observed for specific phobias, generalised anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, and bulimia nervosa. Certainty of evidence was rated as very low or low in most cases, except for generalised anxiety disorder-related outcomes, which showed a moderate rating. To integrate DHIs into clinical practice, further high-quality studies with clearly defined target populations and robust comparators are needed.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100878"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561613","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}