Lancet Digital Health最新文献

{"title":"Leveraging digital technologies to reduce cancer disparities in low-income and middle-income countries","authors":"Judy W Gichoya MD ,&nbsp;Rogers Mwavu MS ,&nbsp;Frank Minja MD ,&nbsp;Nadi Kaonga MD PhD ,&nbsp;Saptarshi Purkayastha PhD ,&nbsp;Janice Newsome MD","doi":"10.1016/j.landig.2025.100937","DOIUrl":"10.1016/j.landig.2025.100937","url":null,"abstract":"<div><div>In a rural clinic in southwestern Uganda, Dr Sarah examines cervical images on her smartphone, receiving real-time artificial intelligence-powered guidance from a gynaecologic oncologist located hundreds of miles away. Once imaginary, this scenario now represents a highly probable future of digital health innovation transforming cancer care globally. With over 35 million new cases of cancer estimated by 2050, and up to 70% of deaths anticipated to disproportionately occur in low-income and middle-income countries (LMICs), digital solutions can be leveraged to accelerate the closure of these cancer care gaps. The global oncology community has responded to this imminent crisis by proposing several interventions, including promoting workforce education, mentorship, and task shifting; supporting early diagnosis and referrals through integrated diagnostics; prioritising and implementing prevention strategies such as tobacco cessation, cervical cancer screening, and vaccination; standardising and personalising treatment through increased participation in clinical trials and provision of essential cancer medications; and strengthening health-care systems. Across all these strategic pillars, digital health tools are crucial for advancing cancer care and narrowing existing global and geographical disparities in LMICs. In this Series paper, we evaluate the current status of these digital innovations in the context of cancer care.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100937"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530955","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":"Application of artificial intelligence and digital tools in cancer pathology","authors":"Lawrence A Shaktah MS ,&nbsp;Zunamys I Carrero PhD ,&nbsp;Katherine Jane Hewitt MBChB ,&nbsp;Marco Gustav MSc ,&nbsp;Matthew Cecchini MD PhD ,&nbsp;Sebastian Foersch MD ,&nbsp;Sabina Berezowska MD ,&nbsp;Prof Jakob Nikolas Kather MD","doi":"10.1016/j.landig.2025.100933","DOIUrl":"10.1016/j.landig.2025.100933","url":null,"abstract":"<div><div>Artificial intelligence (AI) is on the verge of reshaping cancer diagnostics through integration into digital pathology workflows. Despite the progression of AI towards real-world deployment, challenges in interpretability, validation, and clinical integration persist. AI models support the interpretation of stains including haematoxylin and eosin, enabling tumour classification, grading, and biomarker quantification, with clinical applications for targets such as HER2 and PD-L1. In addition, AI models enable the quantification of subtle microscopic patterns with prognostic and predictive values across tumour types. Herein, we provide an overview of the applications of AI in pathology and address emerging regulatory and ethical considerations. We also discuss the disparities in adoption across care settings and emphasise the importance of validation, human oversight, and post-deployment monitoring for the responsible implementation of AI in pathology-driven workflows. Furthermore, we highlight the technical advancements driving these developments, particularly the transition from hand-crafted machine learning workflows to deep learning, self-supervised learning for foundation models, multimodal models, and agentic AI.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100933"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145530887","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":"Transforming liver care with artificial intelligence","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100948","DOIUrl":"10.1016/j.landig.2025.100948","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100948"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651881","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 cognitive behavioural self-management programme for fatigue, pain, and faecal incontinence in inflammatory bowel disease (IBD-BOOST): a multicentre, parallel, randomised controlled trial","authors":"Prof Rona Moss-Morris PhD ,&nbsp;Prof Christine Norton PhD ,&nbsp;Prof Ailsa Hart PhD ,&nbsp;Fionn Cléirigh Büttner PhD ,&nbsp;Thomas Hamborg MSc ,&nbsp;Laura Miller BSc ,&nbsp;Imogen Stagg MSc ,&nbsp;Prof Qasim Aziz PhD ,&nbsp;Wladyslawa Czuber-Dochan PhD ,&nbsp;Prof Lesley Dibley PhD ,&nbsp;Megan English-Stevens ,&nbsp;Julie Flowers ,&nbsp;Serena McGuinness MSc ,&nbsp;Prof Borislava Mihaylova DPhil ,&nbsp;Prof Richard Pollok PhD ,&nbsp;Chris Roukas MSc ,&nbsp;Prof Sonia Saxena MD ,&nbsp;Louise Sweeney PhD ,&nbsp;Prof Stephanie Taylor MD ,&nbsp;Vari Wileman PhD","doi":"10.1016/j.landig.2025.100906","DOIUrl":"10.1016/j.landig.2025.100906","url":null,"abstract":"<div><h3>Background</h3><div>Fatigue, pain, and faecal urgency or incontinence are common, debilitating symptoms in inflammatory bowel disease (IBD). We developed IBD-BOOST, a digital, interactive, facilitator-supported, self-management intervention, and aimed to assess its effects compared with care as usual in relieving these symptoms and improving quality of life.</div></div><div><h3>Methods</h3><div>This multicentre, parallel, randomised controlled trial was conducted online in the UK, with allocation concealment maintained. Participants aged 18 years or older with IBD who rated the impact of fatigue, pain, and faecal urgency or incontinence as 5 or more on a 0–10 scale in a UK national survey were invited. Participants were randomly assigned (1:1) to the online IBD-BOOST programme or care as usual for 6 months via computer-generated randomisation. Primary outcomes were UK Inflammatory Bowel Disease Questionnaire (UK-IBDQ) and Global Rating of Symptom Relief at 6 months post-randomisation. All randomly assigned participants were included in the intention-to-treat and harms analysis. This trial is registered with ISRCTN.com (ISRCTN71618461) and is closed.</div></div><div><h3>Findings</h3><div>Between Jan 20, 2020, and July 27, 2022, 4449 participants were invited to participate, and 780 participants were randomly assigned: 391 to IBD-BOOST and 389 to care as usual. 524 (67%) of 780 participants were female and 253 (32%) were male. At 6 months, there were no statistically significant differences for UK-IBDQ between the care as usual group (unadjusted mean 62·09 [SD 14·42]) and the IBD-BOOST group (unadjusted mean 60·85 [SD 16·08]; treatment effect estimate: adjusted mean difference –1·67 [95% CI –4·13 to 0·80], p=0·19) or for Global Rating of Symptom Relief (unadjusted mean 3·65 [2·75] <em>vs</em> 4·13 [2·81]; adjusted mean difference 0·44 [95% CI –0·56 to 1·44], p=0·39). Complier-averaged causal effects analysis demonstrated that participants who complied with IBD-BOOST reported lower UK-IBDQ scores than those who would have complied in the care as usual group (mean difference –2·39 [95%CI –4·34 to –0·45], p=0·016). Adverse events and serious adverse events were similar between the IBD-BOOST group (55 [14%] of 391) and care as usual group (79 [20%] of 389). There was one possible treatment-related serious adverse event in the IBD-BOOST group (recurrent sleep disorder) and no deaths.</div></div><div><h3>Interpretation</h3><div>IBD-BOOST did not statistically significantly improve disease-specific quality of life or Global Rating of Symptom Relief in patients with IBD with fatigue, pain, or faecal urgency or incontinence compared with care as usual. People who complied with the intervention appeared to derive benefit. Future research should focus on enhancing compliance with interventions and targeting them to individuals most likely to benefit.</div></div><div><h3>Funding</h3><div>UK National Institute for Health and Care Research.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100906"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145476647","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 PhD ,&nbsp;Jasmine Chiat Ling Ong PharmD ,&nbsp;Haoran Cheng MPH ,&nbsp;Haibo Wang MPH ,&nbsp;Daniel Shu Wei Ting MD PhD ,&nbsp;Yih Chung Tham PhD ,&nbsp;Prof Tien Yin Wong MD PhD ,&nbsp;Nan Liu PhD","doi":"10.1016/j.landig.2025.100900","DOIUrl":"10.1016/j.landig.2025.100900","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100900"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","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":"Extension of the GRACE score for non-ST-elevation acute coronary syndrome: a development and validation study in ten countries","authors":"Florian A Wenzl MD ,&nbsp;Klaus F Kofoed DMSc ,&nbsp;Moa Simonsson MD ,&nbsp;Gareth Ambler PhD ,&nbsp;Niels M R van der Sangen MD ,&nbsp;Erik Lampa PhD ,&nbsp;Francesco Bruno MD ,&nbsp;Mark A de Belder MD ,&nbsp;Jiri Hlasensky PhD ,&nbsp;Matthias Mueller-Hennessen MD ,&nbsp;Maria A Smolle MD ,&nbsp;Peizhi Wang BMed ,&nbsp;José P S Henriques MD ,&nbsp;Wouter J Kikkert MD ,&nbsp;Henning Kelbæk DMSc ,&nbsp;Luboš Bouček MD ,&nbsp;Sergio Raposeiras-Roubín MD ,&nbsp;Emad Abu-Assi MD ,&nbsp;Jaouad Azzahhafi MD ,&nbsp;Matthijs A Velders PhD ,&nbsp;Thomas F Lüscher","doi":"10.1016/j.landig.2025.100907","DOIUrl":"10.1016/j.landig.2025.100907","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;The Global Registry of Acute Coronary Events (GRACE) scoring system guides the management of patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) according to current guidelines. However, broad validation of the sex-specific GRACE 3.0 in-hospital mortality model, and corresponding models for predicting long-term mortality and the personalised effect of early invasive management, are still needed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We used data of 609 063 patients with NSTE-ACS from ten countries between Jan 1, 2005, and June 24, 2024. A machine learning model for 1-year mortality was developed in 400 054 patients from England, Wales, and Northern Ireland. Both the in-hospital mortality model and the new 1-year mortality model were externally validated in patients from Sweden, Switzerland, Germany, Denmark, Spain, the Netherlands, and Czechia. A separate machine learning model to predict the individualised effect of early versus delayed invasive coronary angiography and revascularisation on a composite primary outcome of all-cause death, non-fatal recurrent myocardial infarction, hospital admission for refractory myocardial ischaemia, or hospital admission for heart failure at a median follow-up of 4·3 years was developed and externally validated in participants from geographically different sets of hospitals in the Danish VERDICT trial.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;The in-hospital mortality model (area under the receiver operating characteristic curve [AUC] 0·90, 95% CI 0·89–0·91) and the 1-year mortality model (time-dependent AUC 0·84, 95% CI 0·82–0·86) showed excellent discriminative abilities on external validation across all countries. Both models were well calibrated and decision curve analyses suggested favourable clinical utility. Compared with score version 2.0, both models provided improved discrimination and risk reclassification. The individualised treatment effect model effectively identified patients who would benefit from early invasive management on external validation. Patients with high predicted benefit had reduced risk of the composite outcome when randomly assigned to early invasive management (hazard ratio 0·60, 95% CI 0·41–0·88), whereas patients with no-to-moderate predicted benefit did not (1·06, 0·80–1·40; p&lt;sub&gt;interaction&lt;/sub&gt;=0·014). The individualised treatment effect model suggested that the group of patients with NSTE-ACS who benefit from early intervention might be incompletely captured by current treatment strategies.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;The updated GRACE 3.0 scoring system provides a validated, practical tool to support personalised risk assessment in patients with NSTE-ACS. Prediction of an individual’s long-term cardiovascular benefit from early invasive management could refine future trial design.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Funding&lt;/h3&gt;&lt;div&gt;Swiss Heart Foundation, University of Zurich Foundation, Kurt and Senta Herrmann Foundation, Theodor ","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100907"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145313905","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":"Characterising the design and methods of continuous glucose monitoring used in behavioural interventions to inform future research in prediabetes","authors":"Prof David S Black PhD ,&nbsp;Alaina P Vidmar MD ,&nbsp;Braden Barnett MD","doi":"10.1016/j.landig.2025.100904","DOIUrl":"10.1016/j.landig.2025.100904","url":null,"abstract":"<div><div>Digital health feedback technologies are expected to help address the projected 630 million individuals with prediabetes worldwide by 2045. This Viewpoint article characterises the historical use of continuous glucose monitoring (CGM) systems in behavioural research with a focus on the prediabetic population. We identified 19 peer-reviewed studies through a pragmatic literature review and reported key methodological features, including study design, sensor wear protocols, data masking strategies, the role of CGM in behavioural interventions, and approaches to generate CGM metrics. Based on our literature review, we propose four directions to advance CGM in behavioural intervention research in prediabetes: refining sampling strategies to focus recruitment on individuals with prediabetes to better understand metrics in this population; improving transparency in CGM feedback delivery protocols; reporting a comprehensive and targeted set of CGM metrics; and articulating principles that account for the effects of CGM use within behavioural interventions. This methodological characterisation of CGM is a starting point to enhance research quality and behavioural intervention effectiveness, particularly when integrating CGM systems aimed at supporting dietary, physical activity, or lifestyle modifications among people with prediabetes.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100904"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145446398","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 validation of a machine-learning model to reduce futile procurements in donations after circulatory death in liver transplantation in the USA: a multicentre study","authors":"Rintaro Yanagawa ,&nbsp;Kazuhiro Iwadoh PhD ,&nbsp;Toshihiro Nakayama MD ,&nbsp;Daniel J Firl MD ,&nbsp;Chase J Wehrle MD ,&nbsp;Yuki Bekki PhD ,&nbsp;Daiki Soma MD ,&nbsp;Jiro Kusakabe MD ,&nbsp;Yuzuru Sambommatsu MD ,&nbsp;Yutaka Endo PhD ,&nbsp;Kliment K Bozhilov MD ,&nbsp;Jenny H Pan MD ,&nbsp;Masaru Kubota PhD ,&nbsp;Koji Tomiyama PhD ,&nbsp;Masato Fujiki PhD ,&nbsp;Magdy Attia MD ,&nbsp;Prof Marc L Melcher PhD ,&nbsp;Prof Kazunari Sasaki MD","doi":"10.1016/j.landig.2025.100918","DOIUrl":"10.1016/j.landig.2025.100918","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;The number of liver transplants from donors after the circulatory determination of death continues to increase, helping to alleviate the existing organ shortage. However, the rate of attempted but subsequently terminated procurements, known as futile procurements, remains high—mainly because many potential donors do not progress to death within a timeframe after extubation that maintains the suitability of the organ for donation. Futile procurements pose considerable financial and workload burdens to the transplant system. We aimed to develop and validate a machine-learning model to better predict progression to death and reduce futile procurements in cases of donation after circulatory death (DCD).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This study included data from 2221 donors from six centres in the USA. Using a retrospective dataset obtained from 1616 donors between December 1, 2022, and June 30, 2023, we developed a prediction model using the Light Gradient Boosting Machine (LightGBM) framework, with neurological, biochemical, respiratory, and circulatory parameters as predictors. The model was validated retrospectively with data from 398 donors (July 1–Aug 31, 2023) and prospectively with data from 207 donors (March 1–Sept 30, 2024). The performance of the model was evaluated through the area under the receiver operating characteristic curve (AUC), accuracy, futile procurement rate, and missed opportunity rate. We also compared the performance of the model with that of two existing risk-prediction tools (the DCD-N score and the Colorado Calculator) and surgeon predictions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Of the 2221 DCD donors in this study, 1260 progressed to death, 927 of whom died within 30 min after extubation. Cross-validation of the LightGBM model yielded AUCs for predicting donor progression to death of 0·833 (95% CI 0·798–0·868) at 30 min, 0·801 (0·767–0·834) at 45 min, and 0·805 (0·770–0·841) at 60 min after extubation. This performance was maintained in both retrospective (0·834 [0·772–0·891], 0·819 [0·757–0·870], and 0·799 [0·737–0·855]) and prospective (0·831 [0·768–0·885], 0·812 [0·749–0·874], and 0·805 [0·740–0·868]) validation cohorts. Compared with surgeon predictions, the LightGBM model had lower futile procurement rates (0·195 &lt;em&gt;vs&lt;/em&gt; 0·078, respectively), higher accuracy in cases of poor intersurgeon agreement (0·08 &lt;em&gt;vs&lt;/em&gt; 0·29) at 30 min, and similar missed opportunity rates (0·155 &lt;em&gt;vs&lt;/em&gt; 0·167). By contrast, the DCD-N score had AUCs of 0·799 (95% CI 0·730–0·860) at 30 min, 0·760 (0·695–0·824) at 45 min, and 0·739 (0·668–0·801) at 60 min, and the Colorado Calculator had AUCs of 0·694 (0·616–0·768), 0·669 (0·596–0·742), and 0·663 (0·585–0·736) at the same timepoints.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Interpretation&lt;/h3&gt;&lt;div&gt;We show that, compared with surgeon predictions and existing risk-prediction tools, our machine-learning model can enhance the accuracy of the prediction of progression","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100918"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524521","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":"Reducing futile donation after circulatory death procurement with machine learning","authors":"Bima J Hasjim ,&nbsp;Mamatha Bhat","doi":"10.1016/j.landig.2025.100932","DOIUrl":"10.1016/j.landig.2025.100932","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100932"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524601","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":"The Jevons Paradox in global health: efficiency, demand, and the AI dilemma","authors":"Michael JA Reid ,&nbsp;Bilal Mateen","doi":"10.1016/j.landig.2025.100928","DOIUrl":"10.1016/j.landig.2025.100928","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 10","pages":"Article 100928"},"PeriodicalIF":24.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145524536","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}