NPJ Digital Medicine最新文献_第10页

Towards evaluating and building versatile large language models for medicine

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-27 DOI: 10.1038/s41746-024-01390-4

Chaoyi Wu, Pengcheng Qiu, Jinxin Liu, Hongfei Gu, Na Li, Ya Zhang, Yanfeng Wang, Weidi Xie

{"title":"Towards evaluating and building versatile large language models for medicine","authors":"Chaoyi Wu, Pengcheng Qiu, Jinxin Liu, Hongfei Gu, Na Li, Ya Zhang, Yanfeng Wang, Weidi Xie","doi":"10.1038/s41746-024-01390-4","DOIUrl":"https://doi.org/10.1038/s41746-024-01390-4","url":null,"abstract":"In this study, we present MedS-Bench, a comprehensive benchmark to evaluate large language models (LLMs) in clinical contexts, MedS-Bench, spanning 11 high-level clinical tasks. We evaluate nine leading LLMs, e.g., MEDITRON, Llama 3, Mistral, GPT-4, Claude-3.5, etc. and found that most models struggle with these complex tasks. To address these limitations, we developed MedS-Ins, a large-scale instruction-tuning dataset for medicine. MedS-Ins comprises 58 medically oriented language corpora, totaling 5M instances with 19K instructions, across 122 tasks. To demonstrate the dataset’s utility, we conducted a proof-of-concept experiment by performing instruction tuning on a lightweight, open-source medical language model. The resulting model, MMedIns-Llama 3, significantly outperformed existing models on various clinical tasks. To promote further advancements, we have made MedS-Ins fully accessible and invite the research community to contribute to its expansion. Additionally, we have launched a dynamic leaderboard for MedS-Bench, to track the development progress of medical LLMs.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"45 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044104","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}

引用次数: 0

A systematic review of portable technologies for the early assessment of motor development in infants

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-27 DOI: 10.1038/s41746-025-01450-3

Weiyang Deng, Megan K. O’Brien, Rachel A. Andersen, Richa Rai, Erin Jones, Arun Jayaraman

{"title":"A systematic review of portable technologies for the early assessment of motor development in infants","authors":"Weiyang Deng, Megan K. O’Brien, Rachel A. Andersen, Richa Rai, Erin Jones, Arun Jayaraman","doi":"10.1038/s41746-025-01450-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01450-3","url":null,"abstract":"Early screening and evaluation of infant motor development are crucial for detecting motor deficits and enabling timely interventions. Traditional clinical assessments are often subjective, without fully capturing infants’ “real-world” behavior. This has sparked interest in portable, low-cost technologies to objectively and precisely measure infant motion at home, with a goal of enhancing ecological validity. In this systematic review, we explored the current landscape of portable, technology-based solutions to assess early motor development (within the first year), outlining the prevailing challenges and future directions. We reviewed 66 publications, which utilized video, sensors, or a combination of technologies. There were three key applications of these technologies: (1) automating clinical assessments, (2) illuminating new measures of motor development, and (3) predicting developmental outcomes. There was a promising trend toward earlier and more accurate detection using portable technologies. Additional research and demographic diversity are needed to develop fully automated, robust, and user-friendly tools. Registration & Protocol OSF Registries https://doi.org/10.17605/OSF.IO/R6JAE.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"54 41 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143044107","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}

引用次数: 0

A systematic review of passive data for remote monitoring in psychosis and schizophrenia

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-27 DOI: 10.1038/s41746-025-01451-2

Siân Bladon, Emily Eisner, Sandra Bucci, Anuoluwapo Oluwatayo, Glen P. Martin, Matthew Sperrin, John Ainsworth, Sophie Faulkner

引用次数: 0

A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-26 DOI: 10.1038/s41746-025-01454-z

Cheng-Jiang Wei, Yan Tang, Yang-Bai Sun, Tie-Long Yang, Cheng Yan, Hui Liu, Jun Liu, Jing-Ning Huang, Ming-Han Wang, Zhen-Wei Yao, Ji-Long Yang, Zhi-Chao Wang, Qing-Feng Li

{"title":"A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification","authors":"Cheng-Jiang Wei, Yan Tang, Yang-Bai Sun, Tie-Long Yang, Cheng Yan, Hui Liu, Jun Liu, Jing-Ning Huang, Ming-Han Wang, Zhen-Wei Yao, Ji-Long Yang, Zhi-Chao Wang, Qing-Feng Li","doi":"10.1038/s41746-025-01454-z","DOIUrl":"https://doi.org/10.1038/s41746-025-01454-z","url":null,"abstract":"Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background. In a Chinese seven-center cohort, data from 347 subjects were analyzed. Our one-step model incorporated normal tissue/organ labels to provide contextual information, offering a solution for tumors with complex backgrounds. To address privacy concerns, we utilized a lightweight deep neural network suitable for hospital deployment. The final model achieved an accuracy of 85.71% for MPNST diagnosis in the validation cohort and 84.75% accuracy in the independent test set, outperforming another classic two-step model. This success suggests potential for AI models in screening other whole-body primary/metastatic tumors.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"14 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143034970","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}

引用次数: 0

Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms 利用脉动颅骨扩张波形估测无创颅内压的机器学习方法

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-26 DOI: 10.1038/s41746-025-01463-y

Gustavo Frigieri, Sérgio Brasil, Danilo Cardim, Marek Czosnyka, Matheus Ferreira, Wellingson S. Paiva, Xiao Hu

引用次数: 0

Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-25 DOI: 10.1038/s41746-024-01378-0

Hyojin Lee, You Rim Choi, Hyun Kyung Lee, Jaemin Jeong, Joopyo Hong, Hyun-Woo Shin, Hyung-Sin Kim

{"title":"Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals","authors":"Hyojin Lee, You Rim Choi, Hyun Kyung Lee, Jaemin Jeong, Joopyo Hong, Hyun-Woo Shin, Hyung-Sin Kim","doi":"10.1038/s41746-024-01378-0","DOIUrl":"https://doi.org/10.1038/s41746-024-01378-0","url":null,"abstract":"Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the ‘black-box’ nature. In this study, we present SleepXViT, an automatic sleep staging system using Vision Transformer (ViT) that provides intuitive, consistent explanations by mimicking human ‘visual scoring’. Tested on KISS–a PSG image dataset from 7745 patients across four hospitals–SleepXViT achieved a Macro F1 score of 81.94%, outperforming baseline models and showing robust performances on public datasets SHHS1 and SHHS2. Furthermore, SleepXViT offers well-calibrated confidence scores, enabling expert review for low-confidence predictions, alongside high-resolution heatmaps highlighting essential features and relevance scores for adjacent epochs’ influence on sleep stage predictions. Together, these explanations reinforce the scoring consistency of SleepXViT, making it both reliable and interpretable, thereby facilitating the synergy between the AI model and human scorers in clinical settings.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"47 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030921","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}

引用次数: 0

A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-24 DOI: 10.1038/s41746-024-01393-1

Zheyi Dong, Xiaofei Wang, Sai Pan, Taohan Weng, Xiaoniao Chen, Shuangshuang Jiang, Ying Li, Zonghua Wang, Xueying Cao, Qian Wang, Pu Chen, Lai Jiang, Guangyan Cai, Li Zhang, Yong Wang, Jinkui Yang, Yani He, Hongli Lin, Jie Wu, Li Tang, Jianhui Zhou, Shengxi Li, Zhaohui Li, Yibing Fu, Xinyue Yu, Yanqiu Geng, Yingjie Zhang, Liqiang Wang, Mai Xu, Xiangmei Chen

{"title":"A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging","authors":"Zheyi Dong, Xiaofei Wang, Sai Pan, Taohan Weng, Xiaoniao Chen, Shuangshuang Jiang, Ying Li, Zonghua Wang, Xueying Cao, Qian Wang, Pu Chen, Lai Jiang, Guangyan Cai, Li Zhang, Yong Wang, Jinkui Yang, Yani He, Hongli Lin, Jie Wu, Li Tang, Jianhui Zhou, Shengxi Li, Zhaohui Li, Yibing Fu, Xinyue Yu, Yanqiu Geng, Yingjie Zhang, Liqiang Wang, Mai Xu, Xiangmei Chen","doi":"10.1038/s41746-024-01393-1","DOIUrl":"https://doi.org/10.1038/s41746-024-01393-1","url":null,"abstract":"Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) without a kidney biopsy remains a major challenge, often leading to missed opportunities for targeted treatments that could greatly improve NDRD outcomes. To reform the traditional biopsy-all diagnostic paradigm and avoid unnecessary biopsy, we developed a transformer-based deep learning (DL) system for detecting DN and NDRD upon non-invasive multi-modal data of fundus images and clinical characteristics. Our Trans-MUF achieved an AUC of 0.980 (95% CI: 0.979 to 0.980) over the internal retrospective set and also had superior generalizability over a prospective dataset (AUC: 0.989, 95% CI: 0.987 to 0.990) and a multicenter, cross-machine and multi-operator dataset (AUC: 0.932, 95% CI: 0.931 to 0.939). Moreover, the nephrologists‘ diagnosis accuracy can be improved by 21%, through visualization assistance of the DL system. This paper lays a foundation for automatically differentiating DN and NDRD without biopsy. (Registry name: Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy. ID: NCT03865914. Date: 2017-11-30).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"6 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027195","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}

引用次数: 0

Association between exposure to particulate matter and heart rate variability in vulnerable and susceptible individuals

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-24 DOI: 10.1038/s41746-024-01373-5

Yong Whi Jeong, Hayon Michelle Choi, Youhyun Park, Yongjin Lee, Ji Ye Jung, Dae Ryong Kang

{"title":"Association between exposure to particulate matter and heart rate variability in vulnerable and susceptible individuals","authors":"Yong Whi Jeong, Hayon Michelle Choi, Youhyun Park, Yongjin Lee, Ji Ye Jung, Dae Ryong Kang","doi":"10.1038/s41746-024-01373-5","DOIUrl":"https://doi.org/10.1038/s41746-024-01373-5","url":null,"abstract":"Particulate matter (PM) exposure can reduce heart rate variability (HRV), a cardiovascular health marker. This study examines PM1.0 (aerodynamic diameters <1 μm), PM2.5 (≥1 μm and <2.5 μm), and PM10 (≥2.5 μm and <10 μm) effects on HRV in patients with environmental diseases as chronic disease groups and vulnerable populations as control groups. PM levels were measured indoors and outdoors for five days in 97 participants, with 24-h HRV monitoring via wearable devices. PM exposure was assessed by categorizing daily cumulative PM concentrations into higher and lower exposure days, while daily average PM concentrations were used for analysis. Results showed significant negative associations between exposure to single and mixtures of different PM metrics and HRV across all groups, particularly in chronic airway disease and higher air pollution exposed groups. These findings highlight that even lower PM levels may reduce HRV, suggesting a need for stricter standards to protect sensitive individuals.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"1 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027190","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}

引用次数: 0

V3+ extends the V3 framework to ensure user-centricity and scalability of sensor-based digital health technologies

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-24 DOI: 10.1038/s41746-024-01322-2

Jessie P. Bakker, Roland Barge, Jacob Centra, Bryan Cobb, Chas Cota, Christine C. Guo, Bert Hartog, Nathalie Horowicz-Mehler, Elena S. Izmailova, Nikolay V. Manyakov, Samantha McClenahan, Stéphane Motola, Smit Patel, Oana Paun, Marian Schoone, Emre Sezgin, Thomas Switzer, Animesh Tandon, Willem van den Brink, Srinivasan Vairavan, Benjamin Vandendriessche, Bernard Vrijens, Jennifer C. Goldsack

引用次数: 0

Machine learning based quantitative pain assessment for the perioperative period

IF 15.2 1区医学

NPJ Digital Medicine Pub Date : 2025-01-24 DOI: 10.1038/s41746-024-01362-8

Gayeon Ryu, Jae Moon Choi, Hyeon Seok Seok, Jaehyung Lee, Eun-Kyung Lee, Hangsik Shin, Byung-Moon Choi

{"title":"Machine learning based quantitative pain assessment for the perioperative period","authors":"Gayeon Ryu, Jae Moon Choi, Hyeon Seok Seok, Jaehyung Lee, Eun-Kyung Lee, Hangsik Shin, Byung-Moon Choi","doi":"10.1038/s41746-024-01362-8","DOIUrl":"https://doi.org/10.1038/s41746-024-01362-8","url":null,"abstract":"This study developed and evaluated a model for assessing pain during the surgical period using photoplethysmogram data from 242 patients. Pain levels were measured at 2 min intervals using a numerical rating scale or clinical criteria: preoperative, before and after intubation, before and after skin incision, and postoperative. Key features from the photoplethysmography waveform were extracted to build XGBoost-based models for intraoperative and postoperative pain assessment. The combined perioperative model was compared with a commercial surgical pain index, yielding area under the receiver operating characteristics curve scores of 0.819 and 0.927 for intraoperative and postoperative periods, respectively, compared to the commercial index’s scores of 0.829 and 0.577. These results highlight the models’ effectiveness in pain assessment throughout the surgical process, identifying waveform skewness and diastolic phase rate decrease as critical for intraoperative pain assessment and systolic phase area or baseline fluctuation as significant for postoperative pain assessment.Clinical trial registration: Registration name: Clinical Research Information Service (CRIS). Registration site: http://cris.nih.go.kr. Number: KCT0005840. Principal Investigator: Dr. Byung-Moon Choi. Date of registration: January 28, 2021","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"19 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027193","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}

引用次数: 0