NPJ Digital Medicine最新文献

{"title":"Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index","authors":"Benjamin S. C. Wade, Ryan Pindale, James Luccarelli, Shuang Li, Robert C. Meisner, Stephen J. Seiner, Joan A. Camprodon, Michael E. Henry","doi":"10.1038/s41746-025-01523-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01523-3","url":null,"abstract":"<p>Electroconvulsive therapy (ECT) and ketamine are effective treatments for depression; however, evidence-based guidelines are needed to inform individual treatment selection. We adapted the Personalized Advantage Index (PAI) using machine learning to predict optimal treatment assignment to ECT or ketamine using EHR data on 2506 ECT and 196 ketamine patients. Depressive symptoms were evaluated using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Propensity score matching across treatments was used to address confounding by indication, yielding a sample of 392 patients (<i>n</i> = 196 per treatment). Models predicted differential minimum QIDS scores (min-QIDS) over acute treatment using pretreatment EHR measures and SHAP values identified prescriptive predictors. Patients with large PAI scores who received a predicted optimal had significantly lower min-QIDS compared to the non-optimal treatment group (mean difference = 1.19 [95% CI: 0.32, ∞], <i>t</i> = 2.25, <i>q</i> &lt; 0.05, <i>d</i> = 0.26). Our model identified candidate pretreatment factors to provide actionable, effective antidepressant treatment selection guidelines.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"29 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalist medical AI reimbursement challenges and opportunities","authors":"Arjun Mahajan, Dylan Powell","doi":"10.1038/s41746-025-01521-5","DOIUrl":"https://doi.org/10.1038/s41746-025-01521-5","url":null,"abstract":"Generalist AI systems in healthcare can handle multiple complex clinical tasks, unlike narrow AI tools that perform isolated functions. However, current payment systems struggle to capture the value of these integrated capabilities. We examine potential solutions, including value-based and tiered structures, balancing innovation, equitable access, continuous performance evaluation, and cost-effectiveness to realize generalist AI’s transformative potential.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"2 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143507493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Could transparent model cards with layered accessible information drive trust and safety in health AI?","authors":"Stephen Gilbert, Rasmus Adler, Taras Holoyad, Eva Weicken","doi":"10.1038/s41746-025-01482-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01482-9","url":null,"abstract":"We place ‘Model Cards’ and graphical ‘nutrition labels’ for health AI in context with the information needs of patients, health care providers and deployers. We discuss the applicability of Model Cards for General Purpose AI (GPAI) models. If these approaches are to be useful and safe they need to be integrated with regulatory approaches and linked to deeper layers of open and detailed model information and optimized through user testing.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"22 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143485816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new metric to understand the association between heart rate variability and menstrual regularity","authors":"Kimia Heydari, Elizabeth J. Enichen, Ben Li, Joseph C. Kvedar","doi":"10.1038/s41746-025-01517-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01517-1","url":null,"abstract":"Cardiovascular disease is underdiagnosed and undertreated in women compared to men. Wearable technologies (wearables) help shed light on women’s cardiovascular by collecting continuous cardiovascular data and correlating it with hormonal fluctuations across the menstrual cycle. In this context, Jasinski et al. propose that the new metric, cardiovascular amplitude, enables non-invasive monitoring of female physiology and health across the menstrual cycle.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"50 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-world feasibility, accuracy and acceptability of automated retinal photography and AI-based cardiovascular disease risk assessment in Australian primary care settings: a pragmatic trial","authors":"Wenyi Hu, Zhihong Lin, Malcolm Clark, Jacqueline Henwood, Xianwen Shang, Ruiye Chen, Katerina Kiburg, Lei Zhang, Zongyuan Ge, Peter van Wijngaarden, Zhuoting Zhu, Mingguang He","doi":"10.1038/s41746-025-01436-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01436-1","url":null,"abstract":"<p>We aim to assess the real-world accuracy (primary outcome), feasibility and acceptability (secondary outcomes) of an automated retinal photography and artificial intelligence (AI)-based cardiovascular disease (CVD) risk assessment system (rpCVD) in Australian primary care settings. Participants aged 45–70 years who had recently undergone all or part of a CVD risk assessment were recruited from two general practice clinics in Victoria, Australia. After consenting, participants underwent retinal imaging using an automated fundus camera, and an rpCVD risk score was generated by a deep learning algorithm. This score was compared against the World Health Organisation (WHO) CVD risk score, which incorporates age, sex, and other clinical risk factors. The predictive accuracy of the rpCVD and WHO CVD risk scores for 10-year incident CVD events was evaluated using data from the UK Biobank, with the accuracy of each system assessed through the area under the receiver operating characteristic curve (AUC). Participant satisfaction was assessed through a survey, and the imaging success rate was determined by the percentage of individuals with images of sufficient quality to produce an rpCVD risk score. Of the 361 participants, 339 received an rpCVD risk score, resulting in a 93.9% imaging success rate. The rpCVD risk scores showed a moderate correlation with the WHO CVD risk scores (Pearson correlation coefficient [PCC] = 0.526, 95% CI: 0.444–0.599). Despite this, the rpCVD system, which relies solely on retinal images, demonstrated a similar level of accuracy in predicting 10-year incident CVD (AUC = 0.672, 95% CI: 0.658-0.686) compared to the WHO CVD risk score (AUC = 0.693, 95% CI: 0.680-0.707). High satisfaction rates were reported, with 92.5% of participants and 87.5% of general practitioners (GPs) expressing satisfaction with the system. The automated rpCVD system, using only retinal photographs, demonstrated predictive accuracy comparable to the WHO CVD risk score, which incorporates multiple clinical factors including age, the most heavily weighted factor for CVD prediction. This underscores the potential of the rpCVD approach as a faster, easier, and non-invasive alternative for CVD risk assessment in primary care settings, avoiding the need for more complex clinical procedures.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal machine learning to predict surgical site infection with healthcare workload impact assessment","authors":"Kenneth A. McLean, Alessandro Sgrò, Leo R. Brown, Louis F. Buijs, Katie E. Mountain, Catherine A. Shaw, Thomas M. Drake, Riinu Pius, Stephen R. Knight, Cameron J. Fairfield, Richard J. E. Skipworth, Sotirios A. Tsaftaris, Stephen J. Wigmore, Mark A. Potter, Matt-Mouley Bouamrane, Ewen M. Harrison","doi":"10.1038/s41746-024-01419-8","DOIUrl":"https://doi.org/10.1038/s41746-024-01419-8","url":null,"abstract":"<p>Remote monitoring is essential for healthcare digital transformation, however, this poses greater burdens on healthcare providers to review and respond as the data collected expands. This study developed a multimodal neural network to automate assessments of patient-generated data from remote postoperative wound monitoring. Two interventional studies including adult gastrointestinal surgery patients collected wound images and patient-reported outcome measures (PROMs) for 30-days postoperatively. Neural networks for PROMs and images were combined to predict surgical site infection (SSI) diagnosis within 48 h. The multimodal neural network model to predict confirmed SSI within 48 h remained comparable to clinician triage (0.762 [0.690–0.835] vs 0.777 [0.721–0.832]), with an excellent performance on external validation. Simulated usage indicated an 80% reduction in staff time (51.5 to 9.1 h) without compromising diagnostic accuracy. This multimodal approach can effectively support remote monitoring, alleviating provider burden while ensuring high-quality postoperative care.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"50 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An active inference strategy for prompting reliable responses from large language models in medical practice","authors":"Roma Shusterman, Allison C. Waters, Shannon O’Neill, Marshall Bangs, Phan Luu, Don M. Tucker","doi":"10.1038/s41746-025-01516-2","DOIUrl":"https://doi.org/10.1038/s41746-025-01516-2","url":null,"abstract":"<p>Continuing advances in Large Language Models (LLMs) are transforming medical knowledge access across education, training, and treatment. Early literature cautions their non-determinism, potential for harmful responses, and lack of quality control. To address these issues, we propose a domain-specific, validated dataset for LLM training and an actor–critic prompting protocol grounded in active inference. A Therapist agent generates initial responses to patient queries, while a Supervisor agent refines them. In a blind validation study, experienced cognitive behavior therapy for insomnia (CBT-I) therapists evaluated 100 patient queries. For each query, they were given either the LLM’s response or one of two therapist-crafted responses—one appropriate and one deliberately inappropriate—and asked to rate the quality and accuracy of each reply. The LLM often received higher ratings than the appropriate responses, indicating effective alignment with expert standards. This structured approach lays the foundation for safely integrating advanced LLM technology into medical applications.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"27 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram","authors":"Mostafa A. Al-Alusi, Samuel F. Friedman, Shinwan Kany, Joel T. Rämö, Daniel Pipilas, Pulkit Singh, Christopher Reeder, Shaan Khurshid, James P. Pirruccello, Mahnaz Maddah, Jennifer E. Ho, Patrick T. Ellinor","doi":"10.1038/s41746-025-01491-8","DOIUrl":"https://doi.org/10.1038/s41746-025-01491-8","url":null,"abstract":"<p>Hypertension is a major risk factor for cardiovascular disease (CVD), yet blood pressure is measured intermittently and under suboptimal conditions. We developed a deep learning model to identify hypertension and stratify risk of CVD using 12-lead electrocardiogram waveforms. HTN-AI was trained to detect hypertension using 752,415 electrocardiograms from 103,405 adults at Massachusetts General Hospital. We externally validated HTN-AI and demonstrated associations between HTN-AI risk and incident CVD in 56,760 adults at Brigham and Women’s Hospital. HTN-AI accurately discriminated hypertension (internal and external validation AUROC 0.803 and 0.771, respectively). In Fine-Gray regression analyses model-predicted probability of hypertension was associated with mortality (hazard ratio per standard deviation: 1.47 [1.36-1.60], <i>p</i> &lt; 0.001), HF (2.26 [1.90-2.69], <i>p</i> &lt; 0.001), MI (1.87 [1.69-2.07], <i>p</i> &lt; 0.001), stroke (1.30 [1.18-1.44], <i>p</i> &lt; 0.001), and aortic dissection or rupture (1.69 [1.22-2.35], <i>p</i> &lt; 0.001) after adjustment for demographics and risk factors. HTN-AI may facilitate diagnosis of hypertension and serve as a digital biomarker of hypertension-associated CVD.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"18 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143473446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement","authors":"Gabrielle Hoyer, Kenneth T. Gao, Felix G. Gassert, Johanna Luitjens, Fei Jiang, Sharmila Majumdar, Valentina Pedoia","doi":"10.1038/s41746-025-01507-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01507-3","url":null,"abstract":"<p>This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. Through cross-sectional cohort analysis and statistical modeling, we identified specific biomarkers, including variations in cartilage thickness and medial meniscus shape, that are significantly associated with OA incidence and KR outcomes. Integrating these findings into a comprehensive framework represents a considerable step toward personalized knee-joint digital twins, which could enhance therapeutic strategies and inform clinical decision-making in rheumatological care. This versatile and reliable infrastructure has the potential to be extended to broader clinical applications in precision health.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"15 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of consumers’ and healthcare professionals’ trust in digital healthcare","authors":"Soraia de Camargo Catapan, Hannah Sazon, Sophie Zheng, Victor Gallegos-Rejas, Roshni Mendis, Pedro H. R. Santiago, Jaimon T. Kelly","doi":"10.1038/s41746-025-01510-8","DOIUrl":"https://doi.org/10.1038/s41746-025-01510-8","url":null,"abstract":"<p>Despite the well-documented importance of trust in digital healthcare, its domains are not well-understood, preventing theoretically robust instruments for standardised measurements. We identified instruments measuring trust in digital healthcare, explored definitions, associated factors, and outcomes. We systematically reviewed the literature using tailored searches and 49 studies measuring trust in digital healthcare from either consumers’, healthcare professionals’, or both perspectives were included. Trust in digital healthcare is complex and, from a consumers’ perspective, can influence digital healthcare use, adoption, acceptance, and usefulness. Consumers’ trust can be affected by the degree of human interaction in automated interventions, perceived risks, privacy concerns, data accuracy, digital literacy, quality of the digital healthcare intervention, satisfaction, education, and income. Healthcare professionals’ trust is enhanced by education and observing good digital health performance. While studies can benefit from rigorous trust measurements, future efforts should address the need for a theoretical framework for trust in digital healthcare.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"29 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462422","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}