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}
Pub Date : 2025-08-01DOI: 10.1016/j.landig.2025.100880
Daniel R Balcarcel MD , Sanjiv D Mehta MD , Celeste G Dixon MD , Charlotte Z Woods-Hill MD , Ewan C Goligher MD , Wouter A C van Amsterdam MD , Nadir Yehya MD
Prognostic models developed for use in the intensive care unit (ICU) can inform treatment decisions and improve patient care. However, despite extensive research, few models have contributed to improved patient-centred outcomes. A major limitation is that the influence of treatment interventions on patient outcomes during model development and validation is often overlooked. Upon implementation, prognostic models can affect clinical interventions, creating feedback loops that alter the relationship between predictors and observed patient outcomes. This alteration caused by model-mediated intervention is known as model drift. Positive feedback loops reinforce initial prognoses, leading to self-fulfilling prophecies, whereas negative feedback loops obscure the efficacy of successful interventions by rendering them as apparent model inaccuracies. To mitigate these issues, prognostic models for use in ICUs should account for treatment effects and the causal relationships among predictions, interventions, and outcomes. Thus, collaboration among data scientists, epidemiologists, clinical researchers, and implementation scientists is required to ensure that prognostic models enhance patient care without causing inadvertent harm.
{"title":"Feedback loops in intensive care unit prognostic models: an under-recognised threat to clinical validity","authors":"Daniel R Balcarcel MD , Sanjiv D Mehta MD , Celeste G Dixon MD , Charlotte Z Woods-Hill MD , Ewan C Goligher MD , Wouter A C van Amsterdam MD , Nadir Yehya MD","doi":"10.1016/j.landig.2025.100880","DOIUrl":"10.1016/j.landig.2025.100880","url":null,"abstract":"<div><div>Prognostic models developed for use in the intensive care unit (ICU) can inform treatment decisions and improve patient care. However, despite extensive research, few models have contributed to improved patient-centred outcomes. A major limitation is that the influence of treatment interventions on patient outcomes during model development and validation is often overlooked. Upon implementation, prognostic models can affect clinical interventions, creating feedback loops that alter the relationship between predictors and observed patient outcomes. This alteration caused by model-mediated intervention is known as model drift. Positive feedback loops reinforce initial prognoses, leading to self-fulfilling prophecies, whereas negative feedback loops obscure the efficacy of successful interventions by rendering them as apparent model inaccuracies. To mitigate these issues, prognostic models for use in ICUs should account for treatment effects and the causal relationships among predictions, interventions, and outcomes. Thus, collaboration among data scientists, epidemiologists, clinical researchers, and implementation scientists is required to ensure that prognostic models enhance patient care without causing inadvertent harm.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100880"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144691991","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.2025.100891
Marco Gustav MSc , Marko van Treeck MSc , Nic G Reitsam MD , Zunamys I Carrero PhD , Chiara M L Loeffler MD , Asier Rabasco Meneghetti PhD , Prof Bruno Märkl MD , Prof Lisa A Boardman MD , Amy J French MSc , Prof Ellen L Goode PhD , Andrea Gsur PhD , Stefanie Brezina PhD , Prof Marc J Gunter PhD , Neil Murphy PhD , Pia Hönscheid PhD , Christian Sperling MTLA , Sebastian Foersch MD , Robert Steinfelder PhD , Tabitha Harrison MPH , Prof Ulrike Peters PhD , Prof Jakob Nikolas Kather MD
Background
Deep learning-based models enable the prediction of molecular biomarkers from histopathology slides of colorectal cancer stained with haematoxylin and eosin; however, few studies have assessed prediction targets beyond microsatellite instability (MSI), BRAF, and KRAS systematically. We aimed to develop and validate a multi-target model based on deep learning for the simultaneous prediction of numerous genetic alterations and their associated phenotypes in colorectal cancer.
Methods
In this multicentre cohort study, tissue samples from patients with colorectal cancer were obtained by surgical resection and stained with haematoxylin and eosin. These samples were then digitised into whole-slide images and used to train and test a transformer-based deep learning algorithm for biomarker detection to simultaneously predict multiple genetic alterations and provide heatmap explanations. The primary dataset comprised 1376 patients from five cohorts who underwent comprehensive panel sequencing, with an additional 536 patients from two public datasets for validation. We compared the model's performance against conventional single-target models and examined the co-occurrence of alterations and shared morphology.
Findings
The multi-target model was able to predict numerous biomarkers from pathology slides, matching and partly exceeding single-target transformers. In the primary external validation cohorts, mean area under the receiver operating characteristic curve (AUROC) for the multi-target transformer was 0·78 (SD 0·01) for BRAF, 0·88 (0·01) for hypermutation, 0·93 (0·01) for MSI, and 0·86 (0·01) for RNF43; predictive performance was consistent across metrics and supported by co-occurrence analyses. However, biomarkers with high AUROCs largely correlated with MSI, with model predictions depending considerably on morphology associated with MSI at pathological examination.
Interpretation
By use of morphology associated with MSI and more subtle biomarker-specific patterns within a shared phenotype, the multi-target transformers efficiently predicted biomarker status for diverse genetic alterations in colorectal cancer from slides stained with haematoxylin and eosin. These results highlight the importance of considering mutational co-occurrence and common morphology in biomarker research based on deep learning. Our validated and scalable model could support extension to other cancers and large, diverse cohorts, potentially facilitating cost-effective pre-screening and streamlined diagnostics in precision oncology.
Funding
German Federal Ministry of Health, Max-Eder-Programme of German Cancer Aid, German Federal Ministry of Education and Research, German Academic Exchange Service, and the EU.
{"title":"Assessing genotype−phenotype correlations in colorectal cancer with deep learning: a multicentre cohort study","authors":"Marco Gustav MSc , Marko van Treeck MSc , Nic G Reitsam MD , Zunamys I Carrero PhD , Chiara M L Loeffler MD , Asier Rabasco Meneghetti PhD , Prof Bruno Märkl MD , Prof Lisa A Boardman MD , Amy J French MSc , Prof Ellen L Goode PhD , Andrea Gsur PhD , Stefanie Brezina PhD , Prof Marc J Gunter PhD , Neil Murphy PhD , Pia Hönscheid PhD , Christian Sperling MTLA , Sebastian Foersch MD , Robert Steinfelder PhD , Tabitha Harrison MPH , Prof Ulrike Peters PhD , Prof Jakob Nikolas Kather MD","doi":"10.1016/j.landig.2025.100891","DOIUrl":"10.1016/j.landig.2025.100891","url":null,"abstract":"<div><h3>Background</h3><div>Deep learning-based models enable the prediction of molecular biomarkers from histopathology slides of colorectal cancer stained with haematoxylin and eosin; however, few studies have assessed prediction targets beyond microsatellite instability (MSI), <em>BRAF</em>, and <em>KRAS</em> systematically. We aimed to develop and validate a multi-target model based on deep learning for the simultaneous prediction of numerous genetic alterations and their associated phenotypes in colorectal cancer.</div></div><div><h3>Methods</h3><div>In this multicentre cohort study, tissue samples from patients with colorectal cancer were obtained by surgical resection and stained with haematoxylin and eosin. These samples were then digitised into whole-slide images and used to train and test a transformer-based deep learning algorithm for biomarker detection to simultaneously predict multiple genetic alterations and provide heatmap explanations. The primary dataset comprised 1376 patients from five cohorts who underwent comprehensive panel sequencing, with an additional 536 patients from two public datasets for validation. We compared the model's performance against conventional single-target models and examined the co-occurrence of alterations and shared morphology.</div></div><div><h3>Findings</h3><div>The multi-target model was able to predict numerous biomarkers from pathology slides, matching and partly exceeding single-target transformers. In the primary external validation cohorts, mean area under the receiver operating characteristic curve (AUROC) for the multi-target transformer was 0·78 (SD 0·01) for <em>BRAF</em>, 0·88 (0·01) for hypermutation, 0·93 (0·01) for MSI, and 0·86 (0·01) for <em>RNF43</em>; predictive performance was consistent across metrics and supported by co-occurrence analyses. However, biomarkers with high AUROCs largely correlated with MSI, with model predictions depending considerably on morphology associated with MSI at pathological examination.</div></div><div><h3>Interpretation</h3><div>By use of morphology associated with MSI and more subtle biomarker-specific patterns within a shared phenotype, the multi-target transformers efficiently predicted biomarker status for diverse genetic alterations in colorectal cancer from slides stained with haematoxylin and eosin. These results highlight the importance of considering mutational co-occurrence and common morphology in biomarker research based on deep learning. Our validated and scalable model could support extension to other cancers and large, diverse cohorts, potentially facilitating cost-effective pre-screening and streamlined diagnostics in precision oncology.</div></div><div><h3>Funding</h3><div>German Federal Ministry of Health, Max-Eder-Programme of German Cancer Aid, German Federal Ministry of Education and Research, German Academic Exchange Service, and the EU.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 8","pages":"Article 100891"},"PeriodicalIF":24.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884123","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 : 2025-07-01DOI: 10.1016/j.landig.2025.100879
Kunal Rajput , Ara Darzi , Saira Ghafur
{"title":"Overlooked and under-reported: the impact of cyberattacks on primary care in the UK National Health Service","authors":"Kunal Rajput , Ara Darzi , Saira Ghafur","doi":"10.1016/j.landig.2025.100879","DOIUrl":"10.1016/j.landig.2025.100879","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 7","pages":"Article 100879"},"PeriodicalIF":24.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144069","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 : 2025-07-01DOI: 10.1016/j.landig.2025.100885
Charles T A Parker MB BChir , Larissa Mendes PhD , Vinnie Y T Liu MSc , Emily Grist PhD , Songwan Joun MSc , Rikiya Yamashita MD PhD , Akinori Mitani MD PhD , Emmalyn Chen PhD , Marina A Parry PhD , Ashwin Sachdeva PhD , Laura Murphy PhD , Huei-Chung Huang MA , Jacqueline Griffin PhD , Douwe van der Wal MSc , Tamara Todorovic MPH , Sharanpreet Lall BSc , Sara Santos Vidal MSc , Miriam Goncalves BSc , Suparna Thakali BSc , Anna Wingate MSc , Prof Gerhardt Attard PhD
Background
Effective prognostication improves selection of patients with prostate cancer for treatment combinations. We aimed to evaluate whether a previously developed multimodal artificial intelligence (MMAI) algorithm was prognostic in very advanced prostate cancer using data from four phase 3 trials of the STAMPEDE platform protocol.
Methods
We included patients starting androgen-deprivation therapy in the docetaxel, docetaxel plus zoledronic acid, abiraterone, or abiraterone plus enzalutamide trials. Patients were recruited at 112 sites. We combined all standard-of-care control patients (including those allocated to standard of care [SOC-ADT] consisting of testosterone suppression with luteinising hormone-releasing hormone agonists or antagonists, and radiotherapy when indicated), and we combined the rest of the patients into docetaxel-treated or abiraterone-treated groups. Patients had either metastatic disease or were at very high-risk of metastatic disease, determined by node-positivity or, if node-negative, by T stage, serum prostate-specific antigen (PSA) level, and Gleason score. We used the locked ArteraAI Prostate MMAI algorithm that combined these clinical variables, age, and digitised prostate biopsy pathology images. We performed Fine–Gray and Cox regression adjusted for treatment allocation and cumulative incidence analyses at 5 years to evaluate associations with prostate cancer-specific mortality (PCSM) for continuous (per SD increase) and categorical (quartile-Q) scores. The STAMPEDE platform protocol is registered with ClinicalTrials.gov, NCT00268476.
Findings
Of 5213 eligible patients recruited from Oct 5, 2005, to March 31, 2016, 3167 were included in this analysis (1575 [49·7%] with non-metastatic disease, 1592 [50·3%] with metastatic disease; median follow-up 6·9 years [IQR 5·9–8·0]) with all datapoints available for score generation. The MMAI algorithm (per SD increase) was strongly associated with PCSM (hazard ratio [HR] 1·40, 95% CI 1·30–1·51, p<0·0001). On ad-hoc inspection, the highest scoring quartile of patients in each disease and treatment allocation group (MMAI Q4; vs the bottom three quartiles, Q1–3) had the highest PCSM risk in both patients with non-metastatic disease (HR 2·12, 1·61–2·81, p<0·0001) and those with metastatic disease (HR 1·62, 1·39–1·88, p<0·0001). MMAI quartile stratification split patients categorised by disease burden into groups with notably different risks of 5-year PCSM: patients with non-metastatic disease that were node-negative could be further stratified by MMAI score quartile Q1–3 (3%, 2–4) versus Q4 (11%, 7–15), those with non-metastatic disease that were node-positive could be stratified by Q1–3 (11%, 8–14) versus Q4 (20%, 13–26), those with metastatic disease with low-volume could be strati
背景：有效的预后改善了前列腺癌患者联合治疗的选择。我们的目的是评估先前开发的多模态人工智能（MMAI）算法是否可以使用STAMPEDE平台协议的四个3期试验数据来预测晚期前列腺癌的预后。方法：我们纳入了多西他赛、多西他赛加唑来膦酸、阿比特龙或阿比特龙加恩杂鲁胺试验中开始雄激素剥夺治疗的患者。在112个地点招募患者。我们将所有标准护理对照患者（包括那些分配到标准护理[SOC-ADT]的患者，包括睾酮抑制与黄体生成素释放激素激动剂或拮抗剂，并在有指示时进行放疗），并将其余患者合并为多西他赛治疗组或阿比特龙治疗组。患者要么患有转移性疾病，要么处于转移性疾病的高危状态，通过淋巴结阳性或淋巴结阴性，通过T分期、血清前列腺特异性抗原（PSA）水平和Gleason评分来确定。我们使用了锁定的ArteraAI前列腺MMAI算法，该算法结合了这些临床变量、年龄和数字化的前列腺活检病理图像。我们对5年的治疗分配和累积发病率分析进行了微调的Fine-Gray和Cox回归，以评估前列腺癌特异性死亡率（PCSM）与连续（每SD增加）和分类（四分位q）评分的关系。STAMPEDE平台方案已在ClinicalTrials.gov注册，编号NCT00268476。结果：在2005年10月5日至2016年3月31日招募的5213名符合条件的患者中，有3167名患者被纳入该分析，其中1575名（49.7%）为非转移性疾病，1592名（50.3%）为转移性疾病；中位随访时间为6.9年（IQR为5.9 - 8.0），所有数据点均可用于评分生成。MMAI算法（每SD增加）与PCSM密切相关（风险比[HR] 1.40, 95% CI 1.30 - 1.51）。解释：诊断性前列腺活检样本包含放射学上明显转移性前列腺癌患者或高危患者的预后信息。MMAI算法结合疾病负担提高晚期前列腺癌的预后。资助：英国前列腺癌协会、英国医学研究委员会、英国癌症研究中心、约翰·布莱克慈善基金会、前列腺癌基金会、赛诺菲·安万特、杨森、安斯泰来、诺华、Artera。
{"title":"External validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol","authors":"Charles T A Parker MB BChir , Larissa Mendes PhD , Vinnie Y T Liu MSc , Emily Grist PhD , Songwan Joun MSc , Rikiya Yamashita MD PhD , Akinori Mitani MD PhD , Emmalyn Chen PhD , Marina A Parry PhD , Ashwin Sachdeva PhD , Laura Murphy PhD , Huei-Chung Huang MA , Jacqueline Griffin PhD , Douwe van der Wal MSc , Tamara Todorovic MPH , Sharanpreet Lall BSc , Sara Santos Vidal MSc , Miriam Goncalves BSc , Suparna Thakali BSc , Anna Wingate MSc , Prof Gerhardt Attard PhD","doi":"10.1016/j.landig.2025.100885","DOIUrl":"10.1016/j.landig.2025.100885","url":null,"abstract":"<div><h3>Background</h3><div>Effective prognostication improves selection of patients with prostate cancer for treatment combinations. We aimed to evaluate whether a previously developed multimodal artificial intelligence (MMAI) algorithm was prognostic in very advanced prostate cancer using data from four phase 3 trials of the STAMPEDE platform protocol.</div></div><div><h3>Methods</h3><div>We included patients starting androgen-deprivation therapy in the docetaxel, docetaxel plus zoledronic acid, abiraterone, or abiraterone plus enzalutamide trials. Patients were recruited at 112 sites. We combined all standard-of-care control patients (including those allocated to standard of care [SOC-ADT] consisting of testosterone suppression with luteinising hormone-releasing hormone agonists or antagonists, and radiotherapy when indicated), and we combined the rest of the patients into docetaxel-treated or abiraterone-treated groups. Patients had either metastatic disease or were at very high-risk of metastatic disease, determined by node-positivity or, if node-negative, by T stage, serum prostate-specific antigen (PSA) level, and Gleason score. We used the locked ArteraAI Prostate MMAI algorithm that combined these clinical variables, age, and digitised prostate biopsy pathology images. We performed Fine–Gray and Cox regression adjusted for treatment allocation and cumulative incidence analyses at 5 years to evaluate associations with prostate cancer-specific mortality (PCSM) for continuous (per SD increase) and categorical (quartile-Q) scores. The STAMPEDE platform protocol is registered with <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span>, <span><span>NCT00268476</span><svg><path></path></svg></span>.</div></div><div><h3>Findings</h3><div>Of 5213 eligible patients recruited from Oct 5, 2005, to March 31, 2016, 3167 were included in this analysis (1575 [49·7%] with non-metastatic disease, 1592 [50·3%] with metastatic disease; median follow-up 6·9 years [IQR 5·9–8·0]) with all datapoints available for score generation. The MMAI algorithm (per SD increase) was strongly associated with PCSM (hazard ratio [HR] 1·40, 95% CI 1·30–1·51, p<0·0001). On ad-hoc inspection, the highest scoring quartile of patients in each disease and treatment allocation group (MMAI Q4; <em>vs</em> the bottom three quartiles, Q1–3) had the highest PCSM risk in both patients with non-metastatic disease (HR 2·12, 1·61–2·81, p<0·0001) and those with metastatic disease (HR 1·62, 1·39–1·88, p<0·0001). MMAI quartile stratification split patients categorised by disease burden into groups with notably different risks of 5-year PCSM: patients with non-metastatic disease that were node-negative could be further stratified by MMAI score quartile Q1–3 (3%, 2–4) versus Q4 (11%, 7–15), those with non-metastatic disease that were node-positive could be stratified by Q1–3 (11%, 8–14) versus Q4 (20%, 13–26), those with metastatic disease with low-volume could be strati","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 7","pages":"Article 100885"},"PeriodicalIF":24.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227290","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 : 2025-07-01DOI: 10.1016/j.landig.2025.100886
Amir Hadid PhD , Emily G McDonald MD , Qianggang Ding MEng , Christopher Phillipp BSc , Audrey Trottier BSc , Philippe C Dixon PhD , Oussama Jlassi MSc , Matthew P Cheng MD , Jesse Papenburg MD , Prof Michael Libman MD , Dennis Jensen PhD
<div><h3>Background</h3><div>Presymptomatic or asymptomatic immune system signals and subclinical physiological changes might provide a more objective measure of early viral upper respiratory tract infections (VRTIs) compared with symptom-based detection. We aimed to use multimodal wearable sensors, host-response biomarkers, and machine learning to predict systemic inflammation following controlled exposure to a live attenuated influenza vaccine, without relying on symptoms.</div></div><div><h3>Methods</h3><div>WE SENSE study is a single-centre (McGill University Health Center, Montreal, QC, Canada), prospective controlled trial that recruited healthy adults aged 18–59 years who had not received or were not planning to receive the seasonal influenza vaccine or any other vaccine during the study period. We excluded participants with any infectious symptoms within 7 days before screening. We collected physiological and activity data (eg, heart rate, breathing rate, and acceleration) through continuous monitoring with a smart ring (Oura ring Gen 2, Oura Oy, Finland), smart watch (Biobeat watch, Biobeat Technologies, Israel), and smart shirt (Astroskin–Hexoskin shirt, Hexoskin, Canada) along with high temporal resolution systemic inflammatory biomarker mapping over 12 days (7 days before inoculation and 5 days after). We frequently tested participants both before and after inoculation via PCR for respiratory pathogens, and monitored them via apps for symptoms and free-text annotations. Machine learning algorithms predicting systemic inflammatory surges were trained (35 participants), validated (ten participants), and tested (ten participants) using gradient-boosting techniques.</div></div><div><h3>Findings</h3><div>Between Dec 10, 2021, and Feb, 28, 2022, we enrolled 56 participants, of whom 55 had available data; all 55 participants continuously wore the Oura ring, 54 participants wore the Astroskin–Hexoskin shirt, and 50 wore the Biobeat watch. 27 (49%) participants were female and 28 (51%) were male; 31 (56%) participants were White, eight (15%) were Asian, four (7%) were Black, two (4%) were Latino or Hispanic, and ten (18%) did not disclose. We used model 2, which included handpicked features from the Oura ring night-time data, as the candidate model because it was built on the lowest number of features (more practical). This model predicted inflammatory surges with receiver operating characteristic area under the curve (ROC-AUC) of 0·73 (95% CI 0·71–0·74) for real-time prediction and 0·89 (0·87–0·90) for a 24-h tolerance prediction window (24h-tol) using night-time data from the Oura ring. Incorporating both night-time and daytime data from the Astroskin–Hexoskin shirt yielded ROC-AUC values of 0·73 (0·71–0·75) for real-time and 0·91 (0·90–0·92) for 24h-tol along with improved precision (ie, specificity [0·83, 0·79–0·87] and F1 score [0·65, 0·58–0·71]). The model based on symptoms alone had lower performance, with ROC-AUC values of 0·66 (0·63–0
背景：与基于症状的检测相比，症状前或无症状的免疫系统信号和亚临床生理变化可能为早期病毒性上呼吸道感染（VRTIs）提供更客观的衡量标准。我们的目标是使用多模态可穿戴传感器、宿主反应生物标志物和机器学习来预测控制暴露于减毒流感活疫苗后的全身炎症，而不依赖于症状。方法：WE SENSE研究是一项单中心前瞻性对照试验（McGill University Health Center, Montreal， QC, Canada），招募了18-59岁的健康成年人，他们在研究期间没有接种或不打算接种季节性流感疫苗或任何其他疫苗。我们在筛查前7天内排除了有任何感染症状的参与者。我们通过连续监测收集生理和活动数据（例如，心率，呼吸频率和加速度），使用智能环（Oura环Gen 2， Oura Oy，芬兰），智能手表（Biobeat手表，Biobeat Technologies，以色列）和智能衬衫（Astroskin-Hexoskin衬衫，Hexoskin，加拿大），以及高时间分辨率的全身炎症生物标志物制图超过12天（接种前7天和接种后5天）。我们经常通过PCR对接种前后的参与者进行呼吸道病原体检测，并通过应用程序监测他们的症状和免费文本注释。使用梯度增强技术对预测全身性炎症激增的机器学习算法进行了训练（35名参与者）、验证（10名参与者）和测试（10名参与者）。研究结果：在2021年12月10日至2022年2月28日期间，我们招募了56名参与者，其中55名有可用数据；所有55名参与者都一直戴着Oura戒指，54名参与者穿着Astroskin-Hexoskin衬衫，50名参与者戴着Biobeat手表。女性27人（49%），男性28人（51%）；31名（56%）参与者是白人，8名（15%）是亚洲人，4名（7%）是黑人，2名（4%）是拉丁裔或西班牙裔，10名（18%）没有透露。我们使用模型2，其中包括从Oura环夜间数据中精心挑选的特征，作为候选模型，因为它建立在最少数量的特征上（更实用）。该模型使用来自Oura环的夜间数据预测炎症激增，实时预测受试者工作特征曲线下面积（ROC-AUC）为0.73 (95% CI 0.71 - 0.74)， 24小时耐受性预测窗口（24h-tol）为0.89（0.87 - 0.90）。结合astrosskin - hexoskin衬衫的夜间和日间数据，实时的ROC-AUC值为0.73(0.71 - 0.75)，24小时的ROC-AUC值为0.91(0.90 - 0.92)，并提高了精度（即特异性[0.83,0.79 - 0.87]和F1评分[0.65,0.58 - 0.71]）。仅基于症状的模型性能较低，实时ROC-AUC值为0.66 (0.63 - 0.68)，24h-tol的ROC-AUC值为0.79（0.77 - 0.82）。解释：全身炎症生物标志物与可穿戴生物传感器的生理数据相结合，为训练机器学习算法提供了丰富而客观的数据，以预测低级别流感挑战的全身炎症。这种方法优于基于症状的检测，并有可能改善流感等虚拟呼吸道感染的检测，并缩短检测时间，即使在无症状人群中也是如此。资助：加拿大卫生研究所。
{"title":"Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial","authors":"Amir Hadid PhD , Emily G McDonald MD , Qianggang Ding MEng , Christopher Phillipp BSc , Audrey Trottier BSc , Philippe C Dixon PhD , Oussama Jlassi MSc , Matthew P Cheng MD , Jesse Papenburg MD , Prof Michael Libman MD , Dennis Jensen PhD","doi":"10.1016/j.landig.2025.100886","DOIUrl":"10.1016/j.landig.2025.100886","url":null,"abstract":"<div><h3>Background</h3><div>Presymptomatic or asymptomatic immune system signals and subclinical physiological changes might provide a more objective measure of early viral upper respiratory tract infections (VRTIs) compared with symptom-based detection. We aimed to use multimodal wearable sensors, host-response biomarkers, and machine learning to predict systemic inflammation following controlled exposure to a live attenuated influenza vaccine, without relying on symptoms.</div></div><div><h3>Methods</h3><div>WE SENSE study is a single-centre (McGill University Health Center, Montreal, QC, Canada), prospective controlled trial that recruited healthy adults aged 18–59 years who had not received or were not planning to receive the seasonal influenza vaccine or any other vaccine during the study period. We excluded participants with any infectious symptoms within 7 days before screening. We collected physiological and activity data (eg, heart rate, breathing rate, and acceleration) through continuous monitoring with a smart ring (Oura ring Gen 2, Oura Oy, Finland), smart watch (Biobeat watch, Biobeat Technologies, Israel), and smart shirt (Astroskin–Hexoskin shirt, Hexoskin, Canada) along with high temporal resolution systemic inflammatory biomarker mapping over 12 days (7 days before inoculation and 5 days after). We frequently tested participants both before and after inoculation via PCR for respiratory pathogens, and monitored them via apps for symptoms and free-text annotations. Machine learning algorithms predicting systemic inflammatory surges were trained (35 participants), validated (ten participants), and tested (ten participants) using gradient-boosting techniques.</div></div><div><h3>Findings</h3><div>Between Dec 10, 2021, and Feb, 28, 2022, we enrolled 56 participants, of whom 55 had available data; all 55 participants continuously wore the Oura ring, 54 participants wore the Astroskin–Hexoskin shirt, and 50 wore the Biobeat watch. 27 (49%) participants were female and 28 (51%) were male; 31 (56%) participants were White, eight (15%) were Asian, four (7%) were Black, two (4%) were Latino or Hispanic, and ten (18%) did not disclose. We used model 2, which included handpicked features from the Oura ring night-time data, as the candidate model because it was built on the lowest number of features (more practical). This model predicted inflammatory surges with receiver operating characteristic area under the curve (ROC-AUC) of 0·73 (95% CI 0·71–0·74) for real-time prediction and 0·89 (0·87–0·90) for a 24-h tolerance prediction window (24h-tol) using night-time data from the Oura ring. Incorporating both night-time and daytime data from the Astroskin–Hexoskin shirt yielded ROC-AUC values of 0·73 (0·71–0·75) for real-time and 0·91 (0·90–0·92) for 24h-tol along with improved precision (ie, specificity [0·83, 0·79–0·87] and F1 score [0·65, 0·58–0·71]). The model based on symptoms alone had lower performance, with ROC-AUC values of 0·66 (0·63–0","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 7","pages":"Article 100886"},"PeriodicalIF":24.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561612","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 : 2025-07-01DOI: 10.1016/j.landig.2025.100897
The Lancet Digital Health
{"title":"Fixing cracks in the artificial intelligence drug development pipeline","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100897","DOIUrl":"10.1016/j.landig.2025.100897","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 7","pages":"Article 100897"},"PeriodicalIF":24.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668785","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 : 2025-07-01DOI: 10.1016/j.landig.2025.02.004
Christoph Sadée MRes , Stefano Testa MD , Thomas Barba MD PhD , Katherine Hartmann MD PhD , Maximilian Schuessler MS MPP , Alexander Thieme Dr med , Prof George M Church PhD , Prof Ifeoma Okoye MBBS FWACS , Prof Tina Hernandez-Boussard PhD , Prof Leroy Hood MD PhD , Prof Ilya Shmulevich PhD , Prof Ellen Kuhl PhD , Prof Olivier Gevaert PhD
The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.
{"title":"Medical digital twins: enabling precision medicine and medical artificial intelligence","authors":"Christoph Sadée MRes , Stefano Testa MD , Thomas Barba MD PhD , Katherine Hartmann MD PhD , Maximilian Schuessler MS MPP , Alexander Thieme Dr med , Prof George M Church PhD , Prof Ifeoma Okoye MBBS FWACS , Prof Tina Hernandez-Boussard PhD , Prof Leroy Hood MD PhD , Prof Ilya Shmulevich PhD , Prof Ellen Kuhl PhD , Prof Olivier Gevaert PhD","doi":"10.1016/j.landig.2025.02.004","DOIUrl":"10.1016/j.landig.2025.02.004","url":null,"abstract":"<div><div>The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 7","pages":"Article 100864"},"PeriodicalIF":24.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303357","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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