Pub Date : 2025-12-01DOI: 10.1016/j.landig.2025.100917
Ghazal Azarfar PhD , Prof Sara Naimimohasses MD PhD , Prof Sirisha Rambhatla PhD , Prof Matthieu Komorowski MD PhD , Diana Ferro PhD , Prof Peter R Lewis PhD , Darren Gates PhD , Prof Nawar Shara PhD , Prof Gregg M Gascon PhD , Prof Anthony Chang MD , Prof Muhammad Mamdani PharmaD , Prof Mamatha Bhat MD PhD , Alliance of Centers of Artificial Intelligence in Medicine working group
Clinicians rely on various data modalities—such as patient history, clinical signs, imaging, and laboratory results—to improve decision making. Multimodal artificial intelligence (AI) systems are emerging as powerful tools to process these diverse data types; however, the clinical adoption of multimodal AI systems is challenging because of data heterogeneity and integration complexities. The 2024 Temerty Centre for AI Research and Education in Medicine symposium, held on June 17, 2024, in Toronto, Canada, explored the potential and challenges of implementing multimodal AI in health care. In this Review, we summarise insights from the symposium. We discuss current applications, such as those used in early diagnosis of sepsis and cardiology, and identify key barriers, including fusion techniques, model selection, generalisation, fairness, safety, security, and international considerations on the responsible deployment of multimodal AI in health care. We outline practical strategies to overcome these obstacles, emphasising technologies such as federated learning to reduce bias and promote equitable health care. By addressing these challenges, multimodal AI can transform clinical practice and improve patient outcomes worldwide.
{"title":"Responsible adoption of multimodal artificial intelligence in health care: promises and challenges","authors":"Ghazal Azarfar PhD , Prof Sara Naimimohasses MD PhD , Prof Sirisha Rambhatla PhD , Prof Matthieu Komorowski MD PhD , Diana Ferro PhD , Prof Peter R Lewis PhD , Darren Gates PhD , Prof Nawar Shara PhD , Prof Gregg M Gascon PhD , Prof Anthony Chang MD , Prof Muhammad Mamdani PharmaD , Prof Mamatha Bhat MD PhD , Alliance of Centers of Artificial Intelligence in Medicine working group","doi":"10.1016/j.landig.2025.100917","DOIUrl":"10.1016/j.landig.2025.100917","url":null,"abstract":"<div><div>Clinicians rely on various data modalities—such as patient history, clinical signs, imaging, and laboratory results—to improve decision making. Multimodal artificial intelligence (AI) systems are emerging as powerful tools to process these diverse data types; however, the clinical adoption of multimodal AI systems is challenging because of data heterogeneity and integration complexities. The 2024 Temerty Centre for AI Research and Education in Medicine symposium, held on June 17, 2024, in Toronto, Canada, explored the potential and challenges of implementing multimodal AI in health care. In this Review, we summarise insights from the symposium. We discuss current applications, such as those used in early diagnosis of sepsis and cardiology, and identify key barriers, including fusion techniques, model selection, generalisation, fairness, safety, security, and international considerations on the responsible deployment of multimodal AI in health care. We outline practical strategies to overcome these obstacles, emphasising technologies such as federated learning to reduce bias and promote equitable health care. By addressing these challenges, multimodal AI can transform clinical practice and improve patient outcomes worldwide.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 12","pages":"Article 100917"},"PeriodicalIF":24.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.landig.2025.100935
Prof Axel Haferkamp MD PhD , Lisa Frey MD , Gregor Duwe MD , Jan Hendrik Börner MD , Carola Hunfeld MD , Prof Kerstin A Brocker MD PhD , Stella Troilo MD , Prof Walter Lehmacher PhD , C Patrick Papp MD , Prof Kurt Miller MD PhD , Laura Wiemer MD
<div><h3>Background</h3><div>Urinary incontinence affects an estimated 25–45% of women aged 18 years and older. Despite guideline recommendations, conservative treatments are often underused. We hypothesised that an app-based digital therapeutic, when added to standard care, would significantly reduce incontinence episode frequency compared with standard care alone.</div></div><div><h3>Methods</h3><div>In this 12-week, single-blind, randomised, controlled trial across all regions of Germany, adult participants (aged 18 years or older) assigned female at birth with urinary incontinence (stress, urge, or mixed) as defined by their treating urologist or gynaecologist—with at least one urinary incontinence episode per day—were randomly assigned (1:1) to receive app-based therapy (Kranus Mictera) plus usual care (intervention group) or usual care alone (control group). The study centre was masked to treatment assignment. The primary endpoint was the relative change from baseline in incontinence episode frequency per day; a 50% reduction was considered clinically relevant. A 3-day incontinence diary using an electronic case report form measured the intervention effects. The primary endpoint was assessed by intention-to-treat (ITT), including all participants who were randomly assigned, regardless of study completion. App-based treatments consisted of pelvic floor training, behavioural therapy with methods of urinary urge control, and relaxation techniques. Safety was assessed in the safety population, defined as all participants who initiated the intervention. Because all randomly assigned participants activated the app, the safety population was the same as the ITT population. This trial is closed and registered with <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> (<span><span>NCT06389838</span><svg><path></path></svg></span>).</div></div><div><h3>Findings</h3><div>Between April 30 and Sep 2, 2024, after obtaining ethical committee approval, 194 patients were randomly assigned to the intervention group (96 [49%] participants) or the control group (98 [51%] participants), with eight participants subsequently withdrawing (four in the intervention group and four in the control group). Median age of the participants was 50 years (IQR 40–57). 121 (62%) participants had stress, 43 (22%) had urge, and 30 (15%) had mixed urinary incontinence. At week 12, mean relative reduction in incontinence episode frequency was 60·95% (SD 44·32) in the intervention group and 1·69% (43·75) in the control group (between-group difference −59·2%, 95% CI −71·7 to −46·7; p<0·0001). No relevant treatment-related adverse events occurred.</div></div><div><h3>Interpretation</h3><div>This study showed that app-based therapy improved standard care for urinary incontinence through a clinically meaningful reduction of urinary incontinence frequency, highlighting its potential to bridge treatment gaps in clinical practice.</div></div><div><h3>Funding</h3><div>Kra
{"title":"App-based therapy for female patients with urinary incontinence in Germany (DINKS): a single-blind, randomised, controlled trial","authors":"Prof Axel Haferkamp MD PhD , Lisa Frey MD , Gregor Duwe MD , Jan Hendrik Börner MD , Carola Hunfeld MD , Prof Kerstin A Brocker MD PhD , Stella Troilo MD , Prof Walter Lehmacher PhD , C Patrick Papp MD , Prof Kurt Miller MD PhD , Laura Wiemer MD","doi":"10.1016/j.landig.2025.100935","DOIUrl":"10.1016/j.landig.2025.100935","url":null,"abstract":"<div><h3>Background</h3><div>Urinary incontinence affects an estimated 25–45% of women aged 18 years and older. Despite guideline recommendations, conservative treatments are often underused. We hypothesised that an app-based digital therapeutic, when added to standard care, would significantly reduce incontinence episode frequency compared with standard care alone.</div></div><div><h3>Methods</h3><div>In this 12-week, single-blind, randomised, controlled trial across all regions of Germany, adult participants (aged 18 years or older) assigned female at birth with urinary incontinence (stress, urge, or mixed) as defined by their treating urologist or gynaecologist—with at least one urinary incontinence episode per day—were randomly assigned (1:1) to receive app-based therapy (Kranus Mictera) plus usual care (intervention group) or usual care alone (control group). The study centre was masked to treatment assignment. The primary endpoint was the relative change from baseline in incontinence episode frequency per day; a 50% reduction was considered clinically relevant. A 3-day incontinence diary using an electronic case report form measured the intervention effects. The primary endpoint was assessed by intention-to-treat (ITT), including all participants who were randomly assigned, regardless of study completion. App-based treatments consisted of pelvic floor training, behavioural therapy with methods of urinary urge control, and relaxation techniques. Safety was assessed in the safety population, defined as all participants who initiated the intervention. Because all randomly assigned participants activated the app, the safety population was the same as the ITT population. This trial is closed and registered with <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span> (<span><span>NCT06389838</span><svg><path></path></svg></span>).</div></div><div><h3>Findings</h3><div>Between April 30 and Sep 2, 2024, after obtaining ethical committee approval, 194 patients were randomly assigned to the intervention group (96 [49%] participants) or the control group (98 [51%] participants), with eight participants subsequently withdrawing (four in the intervention group and four in the control group). Median age of the participants was 50 years (IQR 40–57). 121 (62%) participants had stress, 43 (22%) had urge, and 30 (15%) had mixed urinary incontinence. At week 12, mean relative reduction in incontinence episode frequency was 60·95% (SD 44·32) in the intervention group and 1·69% (43·75) in the control group (between-group difference −59·2%, 95% CI −71·7 to −46·7; p<0·0001). No relevant treatment-related adverse events occurred.</div></div><div><h3>Interpretation</h3><div>This study showed that app-based therapy improved standard care for urinary incontinence through a clinically meaningful reduction of urinary incontinence frequency, highlighting its potential to bridge treatment gaps in clinical practice.</div></div><div><h3>Funding</h3><div>Kra","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 12","pages":"Article 100935"},"PeriodicalIF":24.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145776044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.landig.2025.100927
Kiruba Nagaratnam MD , Ain A Neuhaus DPhil , Lauren Fensome , Matthew Epton DPhil , Tracey Marriott MBA , Zoe Woodhead PhD , Claire Fernandez DPhil , Michalis Papadakis PhD , Stephen Gerry DPhil , Deb Lowe FRCP , David Hargroves FRCP , Dermot H Mallon PhD , Rob Simister PhD , Pervinder Bhogal PhD , Oliver Spooner MBBS , Ingrid Kane MD , Phil Mathieson MBChB , William Mukonoweshuro MBChB , Martin James MD , Prof Gary A Ford FRCP , George Harston DPhil
Background
Endovascular thrombectomy is a standard of care for patients with large vessel occlusion stroke. Artificial intelligence (AI) imaging software is increasingly used to support identification and selection of patients with stroke for this treatment. We aimed to evaluate the effect of AI stroke imaging software on endovascular treatment in England.
Methods
This prospective observational study was undertaken with the use of data from stroke units in England’s National Health Service (NHS). Data on all patients aged 16 years and older admitted to an NHS hospital with a primary diagnosis of stroke were collected through the national stroke audit registry (Sentinel Stroke National Audit Programme; SSNAP). Endovascular thrombectomy rates and interhospital transfer times were measured through SSNAP for all 107 NHS hospitals admitting patients with acute stroke in England from Jan 1, 2019, to Dec 31, 2023, before and after the systematic implementation of stroke AI software (Brainomix 360 Stroke) in 26 hospitals (six comprehensive stroke centres and 20 primary stroke centres; evaluation sites). Hospital-level data were collected for all hospitals, and patient-level data were collected at evaluation sites. The primary outcome was the proportion of patients with stroke receiving endovascular thrombectomy. Changes in endovascular treatment rates were compared for patients who were reviewed with the use of AI software for image interpretation versus those who were reviewed without AI software.
Findings
452 952 patients with stroke were admitted to 107 hospitals in England between Jan 1, 2019, and Dec 31, 2023. Patient-level data were available for 71 017 patients with ischaemic stroke who were admitted to one of the 26 evaluation sites. For evaluation sites, the pre-implementation endovascular thrombectomy rate was 2·3% (376 of 15 969 patients) and the post-implementation rate was 4·6% (751 of 15 428 patients), a relative increase of 100%. For non-evaluation sites, the pre-implementation rate was 1·6% (1431 of 88 712 patients) and the post-implementation rate was 2·6% (2410 of 89 900 patients), a relative increase of 62·5% (odds ratio [OR] for the interaction between site and time period 1·24 [95% CI 1·08–1·43]; p=0·0026). At the patient level, use of AI stroke software was associated with an increased likelihood of endovascular thrombectomy (OR 1·57 [95% CI 1·33–1·86]; p<0·0001) compared with patients for whom AI software was not used.
Interpretation
Stroke AI imaging software was associated with increased endovascular thrombectomy rates across the English NHS. These results support the routine use of AI imaging software in the management of patients with stroke.
Funding
AI in Health and Care Award from the Accelerated Access Collaborative within NHS England.
{"title":"Artificial intelligence imaging decision support for acute stroke treatment in England: a prospective observational study","authors":"Kiruba Nagaratnam MD , Ain A Neuhaus DPhil , Lauren Fensome , Matthew Epton DPhil , Tracey Marriott MBA , Zoe Woodhead PhD , Claire Fernandez DPhil , Michalis Papadakis PhD , Stephen Gerry DPhil , Deb Lowe FRCP , David Hargroves FRCP , Dermot H Mallon PhD , Rob Simister PhD , Pervinder Bhogal PhD , Oliver Spooner MBBS , Ingrid Kane MD , Phil Mathieson MBChB , William Mukonoweshuro MBChB , Martin James MD , Prof Gary A Ford FRCP , George Harston DPhil","doi":"10.1016/j.landig.2025.100927","DOIUrl":"10.1016/j.landig.2025.100927","url":null,"abstract":"<div><h3>Background</h3><div>Endovascular thrombectomy is a standard of care for patients with large vessel occlusion stroke. Artificial intelligence (AI) imaging software is increasingly used to support identification and selection of patients with stroke for this treatment. We aimed to evaluate the effect of AI stroke imaging software on endovascular treatment in England.</div></div><div><h3>Methods</h3><div>This prospective observational study was undertaken with the use of data from stroke units in England’s National Health Service (NHS). Data on all patients aged 16 years and older admitted to an NHS hospital with a primary diagnosis of stroke were collected through the national stroke audit registry (Sentinel Stroke National Audit Programme; SSNAP). Endovascular thrombectomy rates and interhospital transfer times were measured through SSNAP for all 107 NHS hospitals admitting patients with acute stroke in England from Jan 1, 2019, to Dec 31, 2023, before and after the systematic implementation of stroke AI software (Brainomix 360 Stroke) in 26 hospitals (six comprehensive stroke centres and 20 primary stroke centres; evaluation sites). Hospital-level data were collected for all hospitals, and patient-level data were collected at evaluation sites. The primary outcome was the proportion of patients with stroke receiving endovascular thrombectomy. Changes in endovascular treatment rates were compared for patients who were reviewed with the use of AI software for image interpretation versus those who were reviewed without AI software.</div></div><div><h3>Findings</h3><div>452 952 patients with stroke were admitted to 107 hospitals in England between Jan 1, 2019, and Dec 31, 2023. Patient-level data were available for 71 017 patients with ischaemic stroke who were admitted to one of the 26 evaluation sites. For evaluation sites, the pre-implementation endovascular thrombectomy rate was 2·3% (376 of 15 969 patients) and the post-implementation rate was 4·6% (751 of 15 428 patients), a relative increase of 100%. For non-evaluation sites, the pre-implementation rate was 1·6% (1431 of 88 712 patients) and the post-implementation rate was 2·6% (2410 of 89 900 patients), a relative increase of 62·5% (odds ratio [OR] for the interaction between site and time period 1·24 [95% CI 1·08–1·43]; p=0·0026). At the patient level, use of AI stroke software was associated with an increased likelihood of endovascular thrombectomy (OR 1·57 [95% CI 1·33–1·86]; p<0·0001) compared with patients for whom AI software was not used.</div></div><div><h3>Interpretation</h3><div>Stroke AI imaging software was associated with increased endovascular thrombectomy rates across the English NHS. These results support the routine use of AI imaging software in the management of patients with stroke.</div></div><div><h3>Funding</h3><div>AI in Health and Care Award from the Accelerated Access Collaborative within NHS England.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 12","pages":"Article 100927"},"PeriodicalIF":24.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145670297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100923
Jennifer K Kulke MSc , Lukas M Fuhrmann MSc , Prof Matthias Berking PhD , Prof David D Ebert PhD , Prof Harald Baumeister PhD , Ariqa Derfiora MSc , Avery Veldhouse MSc , Kiona K Weisel PhD
<div><h3>Background</h3><div>To map out the potential benefits of widely available smartphone apps for mental health, especially in contexts where face-to-face services are limited or unavailable, it is crucial to examine their efficacy compared with inactive controls. Standalone smartphone apps might offer an accessible option for individuals waiting for treatment or living in under-resourced settings. Given the currently inconclusive evidence regarding these apps, this systematic review and meta-analysis aimed to assess the efficacy and study quality of randomised controlled trials (RCTs) evaluating standalone smartphone apps for mental health.</div></div><div><h3>Methods</h3><div>In this systematic review and meta-analysis, based on a previously published study, we conducted an updated systematic search of PubMed, PsycINFO, Web of Science, Cochrane Clinical Trial, and Scopus for RCTs published from database inception to Nov 10, 2023. We included RCTs that examined the efficacy of standalone smartphone apps for mental health in adults (age ≥18 years) with heightened symptom severity compared with an inactive control group (eg, waitlist, informational material, and control apps). We excluded control groups that received active treatment. Two independent researchers (AV and AD) extracted summary data, which were verified by a third researcher (JKK). The effect size Hedges’ <em>g</em>, 95% CI, and p value were calculated for each target outcome. We applied a random-effects model to all analyses due to the expected heterogeneity between RCTs. We assessed quality using the Risk of Bias 2 tool (dated Aug 22, 2019) and assessed publication bias via the Egger's test, and the Duval and Tweedie trim-and-fill analysis. The study was registered with PROSPERO, CRD42022310762.</div></div><div><h3>Findings</h3><div>We retrieved 12 705 records from electronic databases and 74 records from other sources (ie, reviews and meta-analyses on digital interventions for mental health identified through database searches and their reference lists, reference lists of other studies, trial registrations in PROSPERO, and websites of researchers in the field). Of these, we included 72 RCTs (70 reports) with 21 702 participants (of the 21 048 participants with sex or gender data, 14 208 [67%] were female, 6744 [32%] were male, and 96 [<1%] were other). At post assessment (assessment after completion of intervention), we found significant effects of apps targeting depression (33 comparisons; Hedges’ <em>g</em> 0·45 [95% CI 0·30 to 0·60], p≤0·0001, <em>I</em><sup>2</sup>=81·30%), anxiety (23 comparisons; 0·35 [0·22 to 0·48], p≤0·0001, <em>I</em><sup>2</sup>=74·91%), sleep problems (14 comparisons; 0·71 [0·51 to 0·92], p≤0·0001, <em>I</em><sup>2</sup>=76·17%), post-traumatic stress disorder (nine comparisons; 0·15 [0·02 to 0·28], p=0·029, <em>I</em><sup>2</sup>=28·65%), eating disorders (four comparisons; 0·50 [0·29 to 0·71], p≤0·0001, <em>I</em><sup>2</sup>=50·49%), and body
{"title":"Efficacy of standalone smartphone apps for mental health: an updated systematic review and meta-analysis","authors":"Jennifer K Kulke MSc , Lukas M Fuhrmann MSc , Prof Matthias Berking PhD , Prof David D Ebert PhD , Prof Harald Baumeister PhD , Ariqa Derfiora MSc , Avery Veldhouse MSc , Kiona K Weisel PhD","doi":"10.1016/j.landig.2025.100923","DOIUrl":"10.1016/j.landig.2025.100923","url":null,"abstract":"<div><h3>Background</h3><div>To map out the potential benefits of widely available smartphone apps for mental health, especially in contexts where face-to-face services are limited or unavailable, it is crucial to examine their efficacy compared with inactive controls. Standalone smartphone apps might offer an accessible option for individuals waiting for treatment or living in under-resourced settings. Given the currently inconclusive evidence regarding these apps, this systematic review and meta-analysis aimed to assess the efficacy and study quality of randomised controlled trials (RCTs) evaluating standalone smartphone apps for mental health.</div></div><div><h3>Methods</h3><div>In this systematic review and meta-analysis, based on a previously published study, we conducted an updated systematic search of PubMed, PsycINFO, Web of Science, Cochrane Clinical Trial, and Scopus for RCTs published from database inception to Nov 10, 2023. We included RCTs that examined the efficacy of standalone smartphone apps for mental health in adults (age ≥18 years) with heightened symptom severity compared with an inactive control group (eg, waitlist, informational material, and control apps). We excluded control groups that received active treatment. Two independent researchers (AV and AD) extracted summary data, which were verified by a third researcher (JKK). The effect size Hedges’ <em>g</em>, 95% CI, and p value were calculated for each target outcome. We applied a random-effects model to all analyses due to the expected heterogeneity between RCTs. We assessed quality using the Risk of Bias 2 tool (dated Aug 22, 2019) and assessed publication bias via the Egger's test, and the Duval and Tweedie trim-and-fill analysis. The study was registered with PROSPERO, CRD42022310762.</div></div><div><h3>Findings</h3><div>We retrieved 12 705 records from electronic databases and 74 records from other sources (ie, reviews and meta-analyses on digital interventions for mental health identified through database searches and their reference lists, reference lists of other studies, trial registrations in PROSPERO, and websites of researchers in the field). Of these, we included 72 RCTs (70 reports) with 21 702 participants (of the 21 048 participants with sex or gender data, 14 208 [67%] were female, 6744 [32%] were male, and 96 [<1%] were other). At post assessment (assessment after completion of intervention), we found significant effects of apps targeting depression (33 comparisons; Hedges’ <em>g</em> 0·45 [95% CI 0·30 to 0·60], p≤0·0001, <em>I</em><sup>2</sup>=81·30%), anxiety (23 comparisons; 0·35 [0·22 to 0·48], p≤0·0001, <em>I</em><sup>2</sup>=74·91%), sleep problems (14 comparisons; 0·71 [0·51 to 0·92], p≤0·0001, <em>I</em><sup>2</sup>=76·17%), post-traumatic stress disorder (nine comparisons; 0·15 [0·02 to 0·28], p=0·029, <em>I</em><sup>2</sup>=28·65%), eating disorders (four comparisons; 0·50 [0·29 to 0·71], p≤0·0001, <em>I</em><sup>2</sup>=50·49%), and body","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100923"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145606895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100902
Marcella Montagnese PhD , Bojidar Rangelov PhD , Tom Doel PhD , Prof David Llewellyn PhD , Prof Zuzana Walker MD PhD , Timothy Rittman MD PhD , Neil P Oxtoby PhD
Dementia poses an increasing global health challenge, and the introduction of new drugs with diverse activity profiles underscores the need for the rapid development and deployment of tailored predictive models. Machine learning has shown promise in dementia research, but it remains largely untested in routine dementia health care—particularly for image-based decision support—owing to data unavailability. Thus, data drift remains a key barrier for equitable real-world translation. We propose and pilot a scalable, cloud-based infrastructure as code solution incorporating privacy-preserving federated learning. This architecture preserves patient privacy by keeping data localised and secure, while enabling the development of robust, adaptable artificial intelligence models. Although technology giants have successfully implemented such technologies in consumer applications, their potential in health-care applications remains largely underutilised. This Viewpoint outlines the key challenges and solutions in implementing cloud-based federated learning for dementia medicine and provides a well-documented codebase to support further research.
{"title":"Cloud computing for equitable, data-driven dementia medicine","authors":"Marcella Montagnese PhD , Bojidar Rangelov PhD , Tom Doel PhD , Prof David Llewellyn PhD , Prof Zuzana Walker MD PhD , Timothy Rittman MD PhD , Neil P Oxtoby PhD","doi":"10.1016/j.landig.2025.100902","DOIUrl":"10.1016/j.landig.2025.100902","url":null,"abstract":"<div><div>Dementia poses an increasing global health challenge, and the introduction of new drugs with diverse activity profiles underscores the need for the rapid development and deployment of tailored predictive models. Machine learning has shown promise in dementia research, but it remains largely untested in routine dementia health care—particularly for image-based decision support—owing to data unavailability. Thus, data drift remains a key barrier for equitable real-world translation. We propose and pilot a scalable, cloud-based infrastructure as code solution incorporating privacy-preserving federated learning. This architecture preserves patient privacy by keeping data localised and secure, while enabling the development of robust, adaptable artificial intelligence models. Although technology giants have successfully implemented such technologies in consumer applications, their potential in health-care applications remains largely underutilised. This Viewpoint outlines the key challenges and solutions in implementing cloud-based federated learning for dementia medicine and provides a well-documented codebase to support further research.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100902"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145483530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100914
Prof Alicja R Rudnicka PhD , Royce Shakespeare MSc , Ryan Chambers BEng , Louis Bolter MSc , John Anderson MD , Jiri Fajtl PhD , Roshan A Welikala PhD , Prof Sarah A Barman PhD , Abraham Olvera-Barrios MD , Laura Webster , Samantha Mann MD , Aaron Lee MD , Prof Paolo Remagnino PhD , Catherine Egan MD , Prof Christopher G Owen PhD , Prof Adnan Tufail MD
Background
The global prevalence of diabetes is rising, alongside costs and workload associated with screening for diabetic eye disease (diabetic retinopathy). Automated retinal image analysis systems (ARIAS) could replace primary human grading of images for diabetic retinopathy. We evaluated multiple ARIAS in a real-life screening programme.
Methods
Eight of 25 invited and potentially eligible CE-marked systems for diabetic retinopathy detection from retinal images agreed to participate. From 202 886 screening encounters at the North East London Diabetic Eye Screening Programme (between Jan 1, 2021, and Dec 31, 2022) we curated a database of 1·2 million images and sociodemographic and grading data. Images were manually graded by up to three graders according to a standard national protocol. ARIAS performance overall and by subgroups of age, sex, ethnicity, and index of multiple deprivation (IMD) were assessed against the reference standard, defined as the final human grade in the worst eye for referable diabetic retinopathy (primary outcome). Vendor algorithms did not have access to human grading data.
Findings
Sensitivity across vendors ranged from 83·7% to 98·7% for referable diabetic retinopathy, from 96·7% to 99·8% for moderate-to-severe non-proliferative diabetic retinopathy, and from 95·8% to 99·5% for proliferative diabetic retinopathy. Sensitivity was largely consistent for moderate-to-severe non-proliferative and proliferative diabetic retinopathy by subgroups of age, sex, ethnicity, and IMD for all ARIAS. For mild-to-moderate non-proliferative diabetic retinopathy with referable maculopathy, sensitivity across vendors ranged from 79·5% to 98·3%, with greater variability across population subgroups. False positive rates for no observable diabetic retinopathy ranged from 4·3% to 61·4% and within vendors varied by 0·5 to 44 percentage points across population subgroups.
Interpretation
ARIAS showed high sensitivity for medium-risk and high-risk diabetic retinopathy in a real-world screening service, with equitable performance across population subgroups. ARIAS could provide a cost-effective solution to deal with the rising burden of screening for diabetic retinopathy by safely triaging for human grading, substantially increasing grading capacity and rapid diabetic retinopathy detection.
Funding
NHS Transformation Directorate, The Health Foundation, and The Wellcome Trust.
{"title":"Automated retinal image analysis systems to triage for grading of diabetic retinopathy: a large-scale, open-label, national screening programme in England","authors":"Prof Alicja R Rudnicka PhD , Royce Shakespeare MSc , Ryan Chambers BEng , Louis Bolter MSc , John Anderson MD , Jiri Fajtl PhD , Roshan A Welikala PhD , Prof Sarah A Barman PhD , Abraham Olvera-Barrios MD , Laura Webster , Samantha Mann MD , Aaron Lee MD , Prof Paolo Remagnino PhD , Catherine Egan MD , Prof Christopher G Owen PhD , Prof Adnan Tufail MD","doi":"10.1016/j.landig.2025.100914","DOIUrl":"10.1016/j.landig.2025.100914","url":null,"abstract":"<div><h3>Background</h3><div>The global prevalence of diabetes is rising, alongside costs and workload associated with screening for diabetic eye disease (diabetic retinopathy). Automated retinal image analysis systems (ARIAS) could replace primary human grading of images for diabetic retinopathy. We evaluated multiple ARIAS in a real-life screening programme.</div></div><div><h3>Methods</h3><div>Eight of 25 invited and potentially eligible CE-marked systems for diabetic retinopathy detection from retinal images agreed to participate. From 202 886 screening encounters at the North East London Diabetic Eye Screening Programme (between Jan 1, 2021, and Dec 31, 2022) we curated a database of 1·2 million images and sociodemographic and grading data. Images were manually graded by up to three graders according to a standard national protocol. ARIAS performance overall and by subgroups of age, sex, ethnicity, and index of multiple deprivation (IMD) were assessed against the reference standard, defined as the final human grade in the worst eye for referable diabetic retinopathy (primary outcome). Vendor algorithms did not have access to human grading data.</div></div><div><h3>Findings</h3><div>Sensitivity across vendors ranged from 83·7% to 98·7% for referable diabetic retinopathy, from 96·7% to 99·8% for moderate-to-severe non-proliferative diabetic retinopathy, and from 95·8% to 99·5% for proliferative diabetic retinopathy. Sensitivity was largely consistent for moderate-to-severe non-proliferative and proliferative diabetic retinopathy by subgroups of age, sex, ethnicity, and IMD for all ARIAS. For mild-to-moderate non-proliferative diabetic retinopathy with referable maculopathy, sensitivity across vendors ranged from 79·5% to 98·3%, with greater variability across population subgroups. False positive rates for no observable diabetic retinopathy ranged from 4·3% to 61·4% and within vendors varied by 0·5 to 44 percentage points across population subgroups.</div></div><div><h3>Interpretation</h3><div>ARIAS showed high sensitivity for medium-risk and high-risk diabetic retinopathy in a real-world screening service, with equitable performance across population subgroups. ARIAS could provide a cost-effective solution to deal with the rising burden of screening for diabetic retinopathy by safely triaging for human grading, substantially increasing grading capacity and rapid diabetic retinopathy detection.</div></div><div><h3>Funding</h3><div>NHS Transformation Directorate, The Health Foundation, and The Wellcome Trust.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100914"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145606945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100959
The Lancet Digital Health
{"title":"Evidence and responsibility of artificial intelligence use in mental health care","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100959","DOIUrl":"10.1016/j.landig.2025.100959","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100959"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745126","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}
Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient certainty for some patients but not for others, raising concerns about fairness. To address this limitation, the effective sample size has been proposed as a measure of sampling uncertainty. We developed a computational method to estimate effective sample sizes for a wide range of prediction models, including machine learning approaches. In this Viewpoint, we illustrated the approach using a clinical dataset (N=23 034) across five model types: logistic regression, elastic net, XGBoost, neural network, and random forest. During simulations, our approach generated accurate estimates of effective sample sizes for logistic regression and elastic net models, with minor deviations noted for the other three models. Although model performance metrics were similar across models, substantial differences in effective sample sizes and risk predictions were observed among patients in the clinical dataset. In conclusion, prediction uncertainty at the individual prediction level can be substantial even when models are developed using large samples. Effective sample size is thus a promising measure to communicate the uncertainty of predicted risk to individual users of machine learning-based prediction models.
{"title":"Effective sample size for individual risk predictions: quantifying uncertainty in machine learning models","authors":"Doranne Thomassen PhD , Toby Hackmann MSc , Prof Jelle Goeman PhD , Prof Ewout Steyerberg PhD , Prof Saskia le Cessie PhD","doi":"10.1016/j.landig.2025.100911","DOIUrl":"10.1016/j.landig.2025.100911","url":null,"abstract":"<div><div>Individual prediction uncertainty is a key aspect of clinical prediction model performance; however, standard performance metrics do not capture it. Consequently, a model might offer sufficient certainty for some patients but not for others, raising concerns about fairness. To address this limitation, the effective sample size has been proposed as a measure of sampling uncertainty. We developed a computational method to estimate effective sample sizes for a wide range of prediction models, including machine learning approaches. In this Viewpoint, we illustrated the approach using a clinical dataset (N=23 034) across five model types: logistic regression, elastic net, XGBoost, neural network, and random forest. During simulations, our approach generated accurate estimates of effective sample sizes for logistic regression and elastic net models, with minor deviations noted for the other three models. Although model performance metrics were similar across models, substantial differences in effective sample sizes and risk predictions were observed among patients in the clinical dataset. In conclusion, prediction uncertainty at the individual prediction level can be substantial even when models are developed using large samples. Effective sample size is thus a promising measure to communicate the uncertainty of predicted risk to individual users of machine learning-based prediction models.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100911"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145641240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100944
Federica Miglietta , Maria Vittoria Dieci
{"title":"Artificial intelligence and tumour-infiltrating lymphocytes in breast cancer: bridging innovation and feasibility towards clinical utility","authors":"Federica Miglietta , Maria Vittoria Dieci","doi":"10.1016/j.landig.2025.100944","DOIUrl":"10.1016/j.landig.2025.100944","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100944"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-01DOI: 10.1016/j.landig.2025.100908
Alexandra J Zimmer PhD , Rishav Das MSc , Patricia Espinoza Lopez MD , Vaidehi Nafade MSc , Genevieve Gore MLIS , César Ugarte-Gil PhD , Prof Kian Fan Chung MD , Woo-Jung Song PhD , Prof Madhukar Pai PhD , Simon Grandjean Lapierre MD
Quantifying cough can offer value for respiratory disease assessment and monitoring. Traditionally, patient-reported outcomes have provided subjective insights into symptoms. Novel digital cough counting tools now enable objective assessments; however, their integration into clinical practice is limited. The aim of this scoping review was to address this gap in the literature by examining the use of automated and semiautomated cough counting tools in patient care and public health. A systematic search of six databases and preprint servers identified studies published up to Feb 12, 2025. From 6968 records found, 618 full-text articles were assessed for eligibility, and 77 were included. Five clinical use cases were identified—disease diagnosis, severity assessment, treatment monitoring, health outcome prediction, and syndromic surveillance—with scarce available evidence supporting each use case. Moderate correlations were found between objective cough frequency and patient-reported cough severity (median correlation coefficient of 0.42, IQR 0·38 to 0·59) and quality of life (median correlation coefficient of −0·49, −0·63 to −0·44), indicating a complex relationship between quantifiable measures and perceived symptoms. Feasibility challenges include device obtrusiveness, monitoring adherence, and addressing patient privacy concerns. Comprehensive studies are needed to validate these technologies in real-world settings and show their clinical value. Early feasibility and acceptability assessments are essential for successful integration.
{"title":"Objective cough counting in clinical practice and public health: a scoping review","authors":"Alexandra J Zimmer PhD , Rishav Das MSc , Patricia Espinoza Lopez MD , Vaidehi Nafade MSc , Genevieve Gore MLIS , César Ugarte-Gil PhD , Prof Kian Fan Chung MD , Woo-Jung Song PhD , Prof Madhukar Pai PhD , Simon Grandjean Lapierre MD","doi":"10.1016/j.landig.2025.100908","DOIUrl":"10.1016/j.landig.2025.100908","url":null,"abstract":"<div><div>Quantifying cough can offer value for respiratory disease assessment and monitoring. Traditionally, patient-reported outcomes have provided subjective insights into symptoms. Novel digital cough counting tools now enable objective assessments; however, their integration into clinical practice is limited. The aim of this scoping review was to address this gap in the literature by examining the use of automated and semiautomated cough counting tools in patient care and public health. A systematic search of six databases and preprint servers identified studies published up to Feb 12, 2025. From 6968 records found, 618 full-text articles were assessed for eligibility, and 77 were included. Five clinical use cases were identified—disease diagnosis, severity assessment, treatment monitoring, health outcome prediction, and syndromic surveillance—with scarce available evidence supporting each use case. Moderate correlations were found between objective cough frequency and patient-reported cough severity (median correlation coefficient of 0.42, IQR 0·38 to 0·59) and quality of life (median correlation coefficient of −0·49, −0·63 to −0·44), indicating a complex relationship between quantifiable measures and perceived symptoms. Feasibility challenges include device obtrusiveness, monitoring adherence, and addressing patient privacy concerns. Comprehensive studies are needed to validate these technologies in real-world settings and show their clinical value. Early feasibility and acceptability assessments are essential for successful integration.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 11","pages":"Article 100908"},"PeriodicalIF":24.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145582684","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}
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