Pub Date : 2025-09-01DOI: 10.1016/j.landig.2025.100890
Bardia Khosravi MD MPH , Saptarshi Purkayastha PhD , Prof Bradley J Erickson MD PhD , Hari M Trivedi MD , Judy W Gichoya MD MS
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.
{"title":"Exploring the potential of generative artificial intelligence in medical image synthesis: opportunities, challenges, and future directions","authors":"Bardia Khosravi MD MPH , Saptarshi Purkayastha PhD , Prof Bradley J Erickson MD PhD , Hari M Trivedi MD , Judy W Gichoya MD MS","doi":"10.1016/j.landig.2025.100890","DOIUrl":"10.1016/j.landig.2025.100890","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 9","pages":"Article 100890"},"PeriodicalIF":24.1,"publicationDate":"2025-09-01","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}
Pub Date : 2025-09-01DOI: 10.1016/j.landig.2025.100876
Sebastian Voigtlaender MSc , Thomas A Nelson MD , Philipp Karschnia MD , Eugene J Vaios MD , Prof Michelle M Kim MD , Philipp Lohmann PhD , Prof Norbert Galldiks MD , Prof Mariella G Filbin MD , Shekoofeh Azizi PhD , Vivek Natarajan MSc , Prof Michelle Monje MD , Prof Jorg Dietrich MD , Sebastian F Winter MD
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.
{"title":"Value of artificial intelligence in neuro-oncology","authors":"Sebastian Voigtlaender MSc , Thomas A Nelson MD , Philipp Karschnia MD , Eugene J Vaios MD , Prof Michelle M Kim MD , Philipp Lohmann PhD , Prof Norbert Galldiks MD , Prof Mariella G Filbin MD , Shekoofeh Azizi PhD , Vivek Natarajan MSc , Prof Michelle Monje MD , Prof Jorg Dietrich MD , Sebastian F Winter MD","doi":"10.1016/j.landig.2025.100876","DOIUrl":"10.1016/j.landig.2025.100876","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 9","pages":"Article 100876"},"PeriodicalIF":24.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.landig.2025.100910
Arun James Thirunavukarasu , Ernest Lim , Bright Huo
{"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":"10.1016/j.landig.2025.100910","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 9","pages":"Article 100910"},"PeriodicalIF":24.1,"publicationDate":"2025-09-01","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}
Pub Date : 2025-08-01DOI: 10.1016/j.landig.2025.100878
Cristina Crocamo PhD , Dario Palpella MD , Daniele Cavaleri MD , Christian Nasti MD , Susanna Piacenti MD , Pietro Morello MD , Giada Lauria MD , Oliviero Villa MD , Ilaria Riboldi PhD , Francesco Bartoli PhD , John Torous MD , Prof Giuseppe Carrà PhD
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.
{"title":"Digital health interventions for mental health disorders: an umbrella review of meta-analyses of randomised controlled trials","authors":"Cristina Crocamo PhD , Dario Palpella MD , Daniele Cavaleri MD , Christian Nasti MD , Susanna Piacenti MD , Pietro Morello MD , Giada Lauria MD , Oliviero Villa MD , Ilaria Riboldi PhD , Francesco Bartoli PhD , John Torous MD , 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}
Pub Date : 2025-08-01DOI: 10.1016/j.landig.2025.100888
Juliana C Taube AB , Zachary Susswein BS , Vittoria Colizza PhD , Prof Shweta Bansal PhD
<div><h3>Background</h3><div>Interpersonal contact has a crucial role in the transmission of infectious diseases. Characterising heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict differences in vulnerability by age, and inform physical distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and is potentially unrepresentative of behaviour in other geographical regions. We aimed to understand the variation in contact patterns in the USA across time, spatial scales, and demographic and social classifications during the COVID-19 pandemic, and to estimate what social behaviour looks like at baseline, in the absence of an ongoing pandemic.</div></div><div><h3>Methods</h3><div>For this study of contact patterns relevant to respiratory transmission during a pandemic, we examined 10·7 million responses to the US COVID-19 Trends and Impact Survey between June 1, 2020, and April 30, 2021 (ie, during the COVID-19 pandemic); the survey recruited participants aged 18 years and older in the USA through Facebook. Data were post-stratified by age and gender to correct for sample representation. We used generalised additive models to characterise spatiotemporal heterogeneity in respiratory contact patterns during the pandemic at the county-week scale; we established how contact patterns vary by urbanicity, age (18–54 years, 55–64 years, 65–74 years, or ≥75 years), gender (male or female), race or ethnicity (Asian, Black or African American, Hispanic, White, or other), and contact setting (work, shopping for essentials, social gatherings, or other). We used a regression approach to estimate baseline (non-pandemic) contact patterns.</div></div><div><h3>Findings</h3><div>Although contact patterns varied over time during the COVID-19 pandemic, the average number of daily contacts was relatively stable after controlling for the effect of incidence-mediated risk perception and disease-related policy. The mean number of non-household contacts was spatially heterogeneous, varying across urban versus rural settings, regardless of the presence of disease. Additional heterogeneity was observed across age, gender, race or ethnicity, and contact setting. Mean number of contacts decreased with age for individuals older than 55 years and was lower in women than in men. During periods of increased national incidence of disease, the contacts of White individuals and contacts at work or social gatherings showed the greatest change.</div></div><div><h3>Interpretation</h3><div>Our findings indicate that US adult baseline contact patterns show little variability over time after controlling for disease, but high spatial variability regardless of disease, with implications for understanding the seasonality of respiratory infectious diseases. The highly structured spat
{"title":"Characterising non-household contact patterns relevant to respiratory transmission in the USA: analysis of a cross-sectional survey","authors":"Juliana C Taube AB , Zachary Susswein BS , Vittoria Colizza PhD , Prof Shweta Bansal PhD","doi":"10.1016/j.landig.2025.100888","DOIUrl":"10.1016/j.landig.2025.100888","url":null,"abstract":"<div><h3>Background</h3><div>Interpersonal contact has a crucial role in the transmission of infectious diseases. Characterising heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict differences in vulnerability by age, and inform physical distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and is potentially unrepresentative of behaviour in other geographical regions. We aimed to understand the variation in contact patterns in the USA across time, spatial scales, and demographic and social classifications during the COVID-19 pandemic, and to estimate what social behaviour looks like at baseline, in the absence of an ongoing pandemic.</div></div><div><h3>Methods</h3><div>For this study of contact patterns relevant to respiratory transmission during a pandemic, we examined 10·7 million responses to the US COVID-19 Trends and Impact Survey between June 1, 2020, and April 30, 2021 (ie, during the COVID-19 pandemic); the survey recruited participants aged 18 years and older in the USA through Facebook. Data were post-stratified by age and gender to correct for sample representation. We used generalised additive models to characterise spatiotemporal heterogeneity in respiratory contact patterns during the pandemic at the county-week scale; we established how contact patterns vary by urbanicity, age (18–54 years, 55–64 years, 65–74 years, or ≥75 years), gender (male or female), race or ethnicity (Asian, Black or African American, Hispanic, White, or other), and contact setting (work, shopping for essentials, social gatherings, or other). We used a regression approach to estimate baseline (non-pandemic) contact patterns.</div></div><div><h3>Findings</h3><div>Although contact patterns varied over time during the COVID-19 pandemic, the average number of daily contacts was relatively stable after controlling for the effect of incidence-mediated risk perception and disease-related policy. The mean number of non-household contacts was spatially heterogeneous, varying across urban versus rural settings, regardless of the presence of disease. Additional heterogeneity was observed across age, gender, race or ethnicity, and contact setting. Mean number of contacts decreased with age for individuals older than 55 years and was lower in women than in men. During periods of increased national incidence of disease, the contacts of White individuals and contacts at work or social gatherings showed the greatest change.</div></div><div><h3>Interpretation</h3><div>Our findings indicate that US adult baseline contact patterns show little variability over time after controlling for disease, but high spatial variability regardless of disease, with implications for understanding the seasonality of respiratory infectious diseases. The highly structured spat","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100888"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974453","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 : 2025-08-01DOI: 10.1016/j.landig.2025.100882
Suzanne L van Winkel MSc , Jim Peters MSc , Natasja Janssen PhD , Jaap Kroes PhD , Elizabeth A Loehrer PhD , Jessie Gommers MSc , Prof Ioannis Sechopoulos PhD , Linda de Munck PhD , Jonas Teuwen PhD , Prof Mireille Broeders PhD , Prof Nico Karssemeijer PhD , Ritse M Mann MD
Background
Breast cancer screening programmes have shown to reduce mortality, but current methods face challenges such as limited mammographic sensitivity, limited resources, and variability in radiologist expertise. Artificial intelligence (AI) offers potential to improve screening accuracy and efficiency. This study simulated different screening scenarios, evaluating the performance of population-based breast cancer screening when using an AI system as a stand-alone reader or second reader.
Methods
In this retrospective cohort study, 42 236 consecutive 2D mammograms from 42 100 women attending the Dutch population-based breast cancer screening between Sept 1, 2016, and Aug 31, 2018 were processed by an AI-based cancer detection system (Transpara version 1.7.0, ScreenPoint Medical). Verified outcomes from the Netherlands Cancer Registry on screen-detected cancers, interval cancers, and later-in-future-detected breast cancers were available with 4-year follow-up. We compared sensitivity, specificity, and recall-rate between single human reading, double human reading, stand-alone AI reading, and combined single human reading with AI. Furthermore, we assessed potential differences in performance regarding breast density, tumour size, lymph-node positivity, and invasiveness between cancers identified by single human readers and AI alone.
Findings
After follow-up, 580 mammograms (579 woman) were labelled positive: 291 screen-detected cancers, 102 interval cancers, and 187 future breast cancers. Double human reading recalled 1244 mammograms (2·9%, 291 screen-detected cancers) and combined single human reading with AI recalled 2112 mammograms (5·0%, 282 screen-detected cancers, 29 interval cancers, 38 future breast cancers), improving the sensitivity by 8·4% (95% CI 5·7–11·2, p<0·0001). No significant difference in performance between combined single human reading with AI across density categories was found. AI-detected future breast cancers and interval cancers missed by human readers were more often invasive cancers (26·7%) or tumours larger than 20 mm in diameter (16·6%) by the time of eventual detection compared with the average screen-detected cancers.
Interpretation
Evaluating screening mammograms with one human reader and AI leads to increased breast cancer detection compared with double human reading, independent of breast density. However, an effective arbitration process is needed as the recall rate increases. AI-identified breast cancers that are missed by human readers seem larger and more often invasive by the time they are eventually detected, confirming the clinical relevance of these cases, recognisable by AI at an earlier stage.
Funding
MARBLE.
背景：乳腺癌筛查项目已显示出降低死亡率的效果，但目前的方法面临着诸如乳腺x线摄影灵敏度有限、资源有限以及放射科医生专业知识差异等挑战。人工智能（AI）提供了提高筛查准确性和效率的潜力。本研究模拟了不同的筛查场景，评估了使用人工智能系统作为独立阅读器或第二阅读器时基于人群的乳腺癌筛查的性能。方法：在这项回顾性队列研究中，使用基于人工智能的癌症检测系统（Transpara version 1.7.0, ScreenPoint Medical）处理2016年9月1日至2018年8月31日期间参加荷兰人群乳腺癌筛查的42 100名妇女的42 236张连续2D乳房x光片。荷兰癌症登记处关于筛查检测到的癌症、间隔期癌症和以后检测到的乳腺癌的验证结果可通过4年随访获得。我们比较了单人阅读、双人阅读、独立人工智能阅读和单人阅读与人工智能结合的敏感性、特异性和召回率。此外，我们评估了单个人类读者和单独人工智能识别的癌症在乳腺密度、肿瘤大小、淋巴结阳性和侵袭性方面的潜在差异。结果：随访后，580张乳房x光片（579名女性）被标记为阳性：291例筛查出的癌症，102例间隔期癌症，187例未来的乳腺癌。双人阅读召回1244张乳房x光片（2.9%，筛查出291例癌症），单人阅读联合人工智能召回2112张乳房x光片（5.0%，筛查出282例癌症，29例间隔期癌症，38例未来乳腺癌），灵敏度提高了8.4% （95% CI 5.7 - 11.2）。解释：与双人阅读相比，单人阅读和人工智能筛查乳房x光片的乳腺癌检出率增加，与乳腺密度无关。但是，随着召回率的增加，需要有效的仲裁程序。人工智能识别出的乳腺癌在最终被检测到时似乎更大，更具侵袭性，这证实了这些病例的临床相关性，人工智能可以在早期阶段识别出来。资金:大理石。
{"title":"AI as an independent second reader in detection of clinically relevant breast cancers within a population-based screening programme in the Netherlands: a retrospective cohort study","authors":"Suzanne L van Winkel MSc , Jim Peters MSc , Natasja Janssen PhD , Jaap Kroes PhD , Elizabeth A Loehrer PhD , Jessie Gommers MSc , Prof Ioannis Sechopoulos PhD , Linda de Munck PhD , Jonas Teuwen PhD , Prof Mireille Broeders PhD , Prof Nico Karssemeijer PhD , Ritse M Mann MD","doi":"10.1016/j.landig.2025.100882","DOIUrl":"10.1016/j.landig.2025.100882","url":null,"abstract":"<div><h3>Background</h3><div>Breast cancer screening programmes have shown to reduce mortality, but current methods face challenges such as limited mammographic sensitivity, limited resources, and variability in radiologist expertise. Artificial intelligence (AI) offers potential to improve screening accuracy and efficiency. This study simulated different screening scenarios, evaluating the performance of population-based breast cancer screening when using an AI system as a stand-alone reader or second reader.</div></div><div><h3>Methods</h3><div>In this retrospective cohort study, 42 236 consecutive 2D mammograms from 42 100 women attending the Dutch population-based breast cancer screening between Sept 1, 2016, and Aug 31, 2018 were processed by an AI-based cancer detection system (Transpara version 1.7.0, ScreenPoint Medical). Verified outcomes from the Netherlands Cancer Registry on screen-detected cancers, interval cancers, and later-in-future-detected breast cancers were available with 4-year follow-up. We compared sensitivity, specificity, and recall-rate between single human reading, double human reading, stand-alone AI reading, and combined single human reading with AI. Furthermore, we assessed potential differences in performance regarding breast density, tumour size, lymph-node positivity, and invasiveness between cancers identified by single human readers and AI alone.</div></div><div><h3>Findings</h3><div>After follow-up, 580 mammograms (579 woman) were labelled positive: 291 screen-detected cancers, 102 interval cancers, and 187 future breast cancers. Double human reading recalled 1244 mammograms (2·9%, 291 screen-detected cancers) and combined single human reading with AI recalled 2112 mammograms (5·0%, 282 screen-detected cancers, 29 interval cancers, 38 future breast cancers), improving the sensitivity by 8·4% (95% CI 5·7–11·2, p<0·0001). No significant difference in performance between combined single human reading with AI across density categories was found. AI-detected future breast cancers and interval cancers missed by human readers were more often invasive cancers (26·7%) or tumours larger than 20 mm in diameter (16·6%) by the time of eventual detection compared with the average screen-detected cancers.</div></div><div><h3>Interpretation</h3><div>Evaluating screening mammograms with one human reader and AI leads to increased breast cancer detection compared with double human reading, independent of breast density. However, an effective arbitration process is needed as the recall rate increases. AI-identified breast cancers that are missed by human readers seem larger and more often invasive by the time they are eventually detected, confirming the clinical relevance of these cases, recognisable by AI at an earlier stage.</div></div><div><h3>Funding</h3><div>MARBLE.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100882"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144859802","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 : 2025-08-01DOI: 10.1016/j.landig.2025.100887
Miles Crosskey PhD , Tomas McIntee PhD , Sandy Preiss MS , Daniel Brannock MS , John M Baratta MD , Yun Jae Yoo BS , Emily Hadley MS , Frank Blanceró BA , Robert Chew MS , Johanna Loomba MS , Abhishek Bhatia MS , Prof Christopher G Chute MD , Prof Melissa Haendel PhD , Richard Moffitt PhD , Emily R Pfaff PhD , N3C Consortium and the RECOVER EHR cohort
Background
In 2021, we used the National COVID Cohort Collaborative (N3C) as part of the National Institutes of Health RECOVER Initiative to develop a machine learning pipeline to identify patients with a high probability of having post-acute sequelae of SARS-CoV-2 infection or long COVID. However, the increased home testing, missing documentation, and reinfections that characterise the pandemic beyond 2022 necessitated the re-engineering of our original model to account for these changes in the COVID-19 research landscape.
Methods
Trained on 72 745 patient records (36 238 with long COVID and 36 507 with no evidence of long COVID), our updated XGBoost model gathered data for each patient in overlapping 100-day periods that progressed through time and issued a probability of long COVID for each 100-day period. We ran the model on patients in N3C (n=5 875 065) who met at least one of the following criteria from Jan 1, 2020, to June 22, 2023: a U07·1 (COVID-19) diagnosis code; a positive SARS-CoV-2 test; a U09·9 (post-acute sequelae of SARS-CoV-2 infection) diagnosis code; a prescription for nirmatrelvir–ritonavir or remdesivir; or an M35·81 (multisystem inflammatory syndrome in children [MIS-C]) diagnosis code. Each patient was given a model score that predicted long COVID status for each 100-day window in which they were aged ≥18 years. If a patient had known acute COVID-19 during any 100-day window (including reinfections), we censored the data from 7 days before the diagnosis or positive test date to 28 days after. We ran the model on controls selected from pre-2020 data to assess the likelihood of false positives.
Findings
The updated model had an area under the receiver operating characteristic curve of 0·90. Precision and recall could be adjusted according to a given use case, depending on whether greater sensitivity or specificity was warranted. Using our model, we estimate the overall prevalence of long COVID among the COVID-19 positive cohort within N3C repository to be 10.4%.
Interpretation
By eschewing the COVID-19 index date as an anchor point for analysis, we can assess the probability of long COVID among patients who might have tested at home, or with suspected (but untested) cases of COVID-19, or multiple SARS-CoV-2 reinfections. We view this exercise as a model for maintaining and updating any machine learning pipeline used for clinical research and operations.
{"title":"Re-engineering a machine learning phenotype to adapt to the changing COVID-19 landscape: a machine learning modelling study from the N3C and RECOVER consortia","authors":"Miles Crosskey PhD , Tomas McIntee PhD , Sandy Preiss MS , Daniel Brannock MS , John M Baratta MD , Yun Jae Yoo BS , Emily Hadley MS , Frank Blanceró BA , Robert Chew MS , Johanna Loomba MS , Abhishek Bhatia MS , Prof Christopher G Chute MD , Prof Melissa Haendel PhD , Richard Moffitt PhD , Emily R Pfaff PhD , N3C Consortium and the RECOVER EHR cohort","doi":"10.1016/j.landig.2025.100887","DOIUrl":"10.1016/j.landig.2025.100887","url":null,"abstract":"<div><h3>Background</h3><div>In 2021, we used the National COVID Cohort Collaborative (N3C) as part of the National Institutes of Health RECOVER Initiative to develop a machine learning pipeline to identify patients with a high probability of having post-acute sequelae of SARS-CoV-2 infection or long COVID. However, the increased home testing, missing documentation, and reinfections that characterise the pandemic beyond 2022 necessitated the re-engineering of our original model to account for these changes in the COVID-19 research landscape.</div></div><div><h3>Methods</h3><div>Trained on 72 745 patient records (36 238 with long COVID and 36 507 with no evidence of long COVID), our updated XGBoost model gathered data for each patient in overlapping 100-day periods that progressed through time and issued a probability of long COVID for each 100-day period. We ran the model on patients in N3C (n=5 875 065) who met at least one of the following criteria from Jan 1, 2020, to June 22, 2023: a U07·1 (COVID-19) diagnosis code; a positive SARS-CoV-2 test; a U09·9 (post-acute sequelae of SARS-CoV-2 infection) diagnosis code; a prescription for nirmatrelvir–ritonavir or remdesivir; or an M35·81 (multisystem inflammatory syndrome in children [MIS-C]) diagnosis code. Each patient was given a model score that predicted long COVID status for each 100-day window in which they were aged ≥18 years. If a patient had known acute COVID-19 during any 100-day window (including reinfections), we censored the data from 7 days before the diagnosis or positive test date to 28 days after. We ran the model on controls selected from pre-2020 data to assess the likelihood of false positives.</div></div><div><h3>Findings</h3><div>The updated model had an area under the receiver operating characteristic curve of 0·90. Precision and recall could be adjusted according to a given use case, depending on whether greater sensitivity or specificity was warranted. Using our model, we estimate the overall prevalence of long COVID among the COVID-19 positive cohort within N3C repository to be 10.4%.</div></div><div><h3>Interpretation</h3><div>By eschewing the COVID-19 index date as an anchor point for analysis, we can assess the probability of long COVID among patients who might have tested at home, or with suspected (but untested) cases of COVID-19, or multiple SARS-CoV-2 reinfections. We view this exercise as a model for maintaining and updating any machine learning pipeline used for clinical research and operations.</div></div><div><h3>Funding</h3><div>National Institutes of Health RECOVER Initiative.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100887"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144974537","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 : 2025-08-01DOI: 10.1016/j.landig.2025.100909
The Lancet Digital Health
{"title":"Rapid generative AI rollout in health care","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100909","DOIUrl":"10.1016/j.landig.2025.100909","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100909"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849412","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 : 2025-08-01DOI: 10.1016/j.landig.2025.100898
Bilal A Mateen , Vasee Moorthy , Alain Labrique , Jeremy Farrar
{"title":"Artificial intelligence and clinical trials: a framework for effective adoption☆","authors":"Bilal A Mateen , Vasee Moorthy , Alain Labrique , Jeremy Farrar","doi":"10.1016/j.landig.2025.100898","DOIUrl":"10.1016/j.landig.2025.100898","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100898"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144709489","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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