Pub Date : 2026-02-02DOI: 10.1038/s41746-025-02225-6
Siqi Li, Yohei Okada, Wenjun Gu, Michael Hao Chen, Son Ngoc Do, Quyet Dinh Pham, Quoc Ta Hoang, Marcus Eng Hock Ong, Nan Liu
{"title":"Author Correction: Leveraging AI and transfer learning to enhance out-of-hospital cardiac arrest outcome prediction in diverse setting.","authors":"Siqi Li, Yohei Okada, Wenjun Gu, Michael Hao Chen, Son Ngoc Do, Quyet Dinh Pham, Quoc Ta Hoang, Marcus Eng Hock Ong, Nan Liu","doi":"10.1038/s41746-025-02225-6","DOIUrl":"10.1038/s41746-025-02225-6","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":"103"},"PeriodicalIF":15.1,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12864723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146106402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-02DOI: 10.1038/s41746-026-02414-x
Stephen Gilbert
{"title":"Digital medicine's international race for regulatory sandboxes and voluntary alternative pathways picks up tempo.","authors":"Stephen Gilbert","doi":"10.1038/s41746-026-02414-x","DOIUrl":"https://doi.org/10.1038/s41746-026-02414-x","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":" ","pages":""},"PeriodicalIF":15.1,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146106389","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}
Brain tumor MRI segmentation and classification are essential for preoperative boundary assessment, lesion burden quantification, postoperative response monitoring, and radiotherapy planning, yet edema overlap, sequence heterogeneity, and artifacts often blur lesion margins. Together with the high cost of pixel-level annotation, these factors limit robust, cross-institution deployment. We propose DARE-FUSE (Domain Aligned Representation with Evidence-guided FUSE), a unified framework for pixel-level segmentation and image-level classification under limited samples and labels. Dual encoders with a feature-interaction bridge learn a shared embedding, and a Domain Alignment Refiner maps it to task-aligned representations for the segmentation and classification branches. For segmentation, U-SEG decodes features and SEGU outputs pixel-wise uncertainty to regularize boundary over/under-segmentation. For classification, CPG produces predictions and multi-scale Grad-CAM++ evidence. A Generative Lesion Removal Prior reconstructs a tumor-free counterpart to yield a difference prior, and FUSE combines this prior with Grad-CAM++ under uncertainty attenuation to guide segmentation and suppress hallucinations. DARE-FUSE achieves stable, leading performance on BraTS segmentation benchmarks and several classification datasets; ablations and label-reduction experiments confirm complementary gains and smooth degradation as pixel annotations decrease. The resulting uncertainty maps and continuous priors support interpretable decision assistance in surgery, radiotherapy contouring, triage, and longitudinal follow-up.
脑肿瘤MRI分割和分类对于术前边界评估、病灶负担量化、术后反应监测和放疗计划至关重要，但水肿重叠、序列异质性和伪影常常模糊病灶边缘。再加上像素级注释的高成本，这些因素限制了健壮的跨机构部署。我们提出了DARE-FUSE (Domain Aligned Representation with Evidence-guided FUSE)，这是一个在有限样本和标签下进行像素级分割和图像级分类的统一框架。具有特征交互桥的双编码器学习共享嵌入，领域对齐细化器将其映射到用于分割和分类分支的任务对齐表示。对于分割，U-SEG对特征进行解码，SEGU输出逐像素的不确定性来正则化边界分割过/欠。对于分类，CPG产生预测和多尺度的Grad-CAM++证据。生成病变去除先验（Generative病变Removal Prior）重建无肿瘤对应物，产生差异先验，FUSE将该先验与不确定性衰减下的Grad-CAM++结合，指导分割，抑制幻觉。DARE-FUSE在brat分割基准和几个分类数据集上实现了稳定、领先的性能；消融和标签约简实验证实了互补增益和平滑退化，因为像素注释减少。由此产生的不确定性图和连续的先验支持手术、放疗轮廓、分诊和纵向随访中可解释的决策辅助。
{"title":"DARE-FUSE: domain aligned evidence guided learning for joint brain tumor MRI segmentation and classification","authors":"Yuqi Liu, Chen Sun, Yuning Niu, Xu Wang, Zehua Yue, Tieqiang Zhang, Jiang Li, Xiudong Guan, Dainan Zhang, Wang Jia","doi":"10.1038/s41746-026-02365-3","DOIUrl":"https://doi.org/10.1038/s41746-026-02365-3","url":null,"abstract":"Brain tumor MRI segmentation and classification are essential for preoperative boundary assessment, lesion burden quantification, postoperative response monitoring, and radiotherapy planning, yet edema overlap, sequence heterogeneity, and artifacts often blur lesion margins. Together with the high cost of pixel-level annotation, these factors limit robust, cross-institution deployment. We propose DARE-FUSE (Domain Aligned Representation with Evidence-guided FUSE), a unified framework for pixel-level segmentation and image-level classification under limited samples and labels. Dual encoders with a feature-interaction bridge learn a shared embedding, and a Domain Alignment Refiner maps it to task-aligned representations for the segmentation and classification branches. For segmentation, U-SEG decodes features and SEGU outputs pixel-wise uncertainty to regularize boundary over/under-segmentation. For classification, CPG produces predictions and multi-scale Grad-CAM++ evidence. A Generative Lesion Removal Prior reconstructs a tumor-free counterpart to yield a difference prior, and FUSE combines this prior with Grad-CAM++ under uncertainty attenuation to guide segmentation and suppress hallucinations. DARE-FUSE achieves stable, leading performance on BraTS segmentation benchmarks and several classification datasets; ablations and label-reduction experiments confirm complementary gains and smooth degradation as pixel annotations decrease. The resulting uncertainty maps and continuous priors support interpretable decision assistance in surgery, radiotherapy contouring, triage, and longitudinal follow-up.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"90 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102074","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 : 2026-01-31DOI: 10.1038/s41746-026-02360-8
Jonathan Heitz, Ines M. Engler, Nicolas Langer
With the growing prevalence of cognitive decline in ageing populations, accessible and scalable screening tools are essential for early intervention. This study investigated the potential of automated speech analysis as a proxy for cognitive assessment in 1003 older adults. Employing machine learning regression models, we demonstrated that linguistic and acoustic features extracted from spontaneous speech quadrupled performance compared to models using demographic information alone, when predicting cognitive domain scores. We then trained a binary classifier to identify individuals performing below normative thresholds (ROC-AUC up to 0.81), illustrating possible applications such as large-scale screening for cognitive impairment and improved participant selection for clinical trials. Finally, we evaluated our approach on an independent clinical dataset of Alzheimer’s disease (AD) patients and controls, demonstrating its generalizability. These findings highlight the clinical feasibility of speech analysis as a low-cost, non-intrusive digital biomarker for cognitive monitoring and screening.
{"title":"Towards a speech-based digital biomarker for cognitive impairment: speech as a proxy for cognitive assessment","authors":"Jonathan Heitz, Ines M. Engler, Nicolas Langer","doi":"10.1038/s41746-026-02360-8","DOIUrl":"https://doi.org/10.1038/s41746-026-02360-8","url":null,"abstract":"With the growing prevalence of cognitive decline in ageing populations, accessible and scalable screening tools are essential for early intervention. This study investigated the potential of automated speech analysis as a proxy for cognitive assessment in 1003 older adults. Employing machine learning regression models, we demonstrated that linguistic and acoustic features extracted from spontaneous speech quadrupled performance compared to models using demographic information alone, when predicting cognitive domain scores. We then trained a binary classifier to identify individuals performing below normative thresholds (ROC-AUC up to 0.81), illustrating possible applications such as large-scale screening for cognitive impairment and improved participant selection for clinical trials. Finally, we evaluated our approach on an independent clinical dataset of Alzheimer’s disease (AD) patients and controls, demonstrating its generalizability. These findings highlight the clinical feasibility of speech analysis as a low-cost, non-intrusive digital biomarker for cognitive monitoring and screening.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"286 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089863","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 : 2026-01-31DOI: 10.1038/s41746-025-02263-0
Janos Meszaros, Isabelle Huys, John P. A. Ioannidis
The EU Artificial Intelligence Act (AI Act) is the world’s first comprehensive, cross-sectoral legal framework dedicated specifically to AI. It introduces a structured regulatory approach to ensure that AI systems are safe, transparent, and trustworthy. To foster innovation, it includes research exemptions that place certain AI systems - those under development or used solely for scientific research - outside of its scope and obligations. However, this paper argues that these exemptions rely on distinctions that may not fully capture the realities of contemporary AI research. These include the unclear divide between research and commercial activities, and between lab-based development and real-world testing. Through legal analysis and practical scenarios, we demonstrate how the blurred boundaries between academic and commercial interests, as well as between controlled research and real-world use, create regulatory uncertainty and open the door to potential misuse. The paper highlights the risks stemming from vague definitions and the lack of harmonized guidance. It ultimately calls for clearer guidance, stronger safeguards, and more realistic frameworks that reflect the complexities of modern AI research.
{"title":"Challenges in applying the EU AI act research exemptions to contemporary AI research","authors":"Janos Meszaros, Isabelle Huys, John P. A. Ioannidis","doi":"10.1038/s41746-025-02263-0","DOIUrl":"https://doi.org/10.1038/s41746-025-02263-0","url":null,"abstract":"The EU Artificial Intelligence Act (AI Act) is the world’s first comprehensive, cross-sectoral legal framework dedicated specifically to AI. It introduces a structured regulatory approach to ensure that AI systems are safe, transparent, and trustworthy. To foster innovation, it includes research exemptions that place certain AI systems - those under development or used solely for scientific research - outside of its scope and obligations. However, this paper argues that these exemptions rely on distinctions that may not fully capture the realities of contemporary AI research. These include the unclear divide between research and commercial activities, and between lab-based development and real-world testing. Through legal analysis and practical scenarios, we demonstrate how the blurred boundaries between academic and commercial interests, as well as between controlled research and real-world use, create regulatory uncertainty and open the door to potential misuse. The paper highlights the risks stemming from vague definitions and the lack of harmonized guidance. It ultimately calls for clearer guidance, stronger safeguards, and more realistic frameworks that reflect the complexities of modern AI research.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"9 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089872","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 : 2026-01-31DOI: 10.1038/s41746-026-02408-9
Ryan A Heumann,Steven R Steinhubl
{"title":"Diverging trajectories of trust in healthcare and on-line information seeking: what's next with LLMs.","authors":"Ryan A Heumann,Steven R Steinhubl","doi":"10.1038/s41746-026-02408-9","DOIUrl":"https://doi.org/10.1038/s41746-026-02408-9","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"78 1","pages":"102"},"PeriodicalIF":15.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095478","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 : 2026-01-31DOI: 10.1038/s41746-026-02410-1
Ariel Yuhan Ong,David A Merle,Andreas Pollreisz,Siegfried K Wagner,Mertcan Sevgi,Pearse A Keane,Roman Huemer,Julian Oehling,Markus Jäger,Josef Huemer
The parallels between medicine and aviation are well-recognised. The aviation industry's early experience with automation improved safety and efficiency, but simultaneously introduced new vulnerabilities and occasionally created misplaced trust in complex systems. Aviation has developed a robust safety framework in response to these costly lessons. In this Perspective, which draws from the experiences of clinicians and aviation experts, we argue that it is now time for the medical community to consider how we can learn from these lessons as artificial intelligence (AI) becomes increasingly integrated into clinical care. We propose that this requires a shift in perspective from AI as "autopilot" to collaboration with a "digital copilot", as well as considerations of practicalities such as scenario-based training, clinician benchmarking, and minimum unaided practice, with the ultimate aim of optimising human-AI collaboration to improve patient care.
{"title":"Flight rules for clinical AI: lessons from aviation for human-AI collaboration in medicine.","authors":"Ariel Yuhan Ong,David A Merle,Andreas Pollreisz,Siegfried K Wagner,Mertcan Sevgi,Pearse A Keane,Roman Huemer,Julian Oehling,Markus Jäger,Josef Huemer","doi":"10.1038/s41746-026-02410-1","DOIUrl":"https://doi.org/10.1038/s41746-026-02410-1","url":null,"abstract":"The parallels between medicine and aviation are well-recognised. The aviation industry's early experience with automation improved safety and efficiency, but simultaneously introduced new vulnerabilities and occasionally created misplaced trust in complex systems. Aviation has developed a robust safety framework in response to these costly lessons. In this Perspective, which draws from the experiences of clinicians and aviation experts, we argue that it is now time for the medical community to consider how we can learn from these lessons as artificial intelligence (AI) becomes increasingly integrated into clinical care. We propose that this requires a shift in perspective from AI as \"autopilot\" to collaboration with a \"digital copilot\", as well as considerations of practicalities such as scenario-based training, clinician benchmarking, and minimum unaided practice, with the ultimate aim of optimising human-AI collaboration to improve patient care.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"93 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146095477","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 : 2026-01-31DOI: 10.1038/s41746-026-02393-z
Guoqiang Ren, Zhen Chen, Pengxiang Su, Da Li, Xiaoping Yang, Di Gai, Xin Wei, Weifeng Xu, Hongping Chen, Xiaoguang Zhao, Xiaofei Wang, Pengfei Liu, Honghua Ye, Yanfeng Ma
Accurate medical image segmentation continues to pose significant challenges, as existing methods often struggle to concurrently achieve efficient global context modeling, precise boundary delineation, and robust generalization. To address these issues, a novel framework named Contextual and Frequency-Guided Mamba Network (CFG-MambaNet) is presented. Specifically, a variable-scale state space block based on Mamba is employed so that long-range dependencies can be captured with linear complexity, efficiently addressing the inefficiency of Transformer-based models in high-resolution medical imaging. Moreover, a frequency-guided representation module is incorporated to explicitly separate global low-frequency structures from high-frequency boundary details, which significantly alleviates the difficulty of segmenting lesions with blurred contours or weak textures. Furthermore, an adaptive context aggregation mechanism is introduced to integrate heterogeneous semantic cues and to consistently highlight clinically critical regions, substantially improving robustness across diverse anatomical scales and morphologies. To further stabilize training and improve boundary adherence, a composite loss combined with deep supervision is employed. Extensive experiments were conducted on four publicly available datasets, including ACDC, Kvasir-SEG, ISIC, and SEED, covering cardiac MRI, endoscopy, dermoscopy, and pathology images.
{"title":"CFG-MambaNet: Contextual and Frequency-Guided Mamba Network for medical image segmentation","authors":"Guoqiang Ren, Zhen Chen, Pengxiang Su, Da Li, Xiaoping Yang, Di Gai, Xin Wei, Weifeng Xu, Hongping Chen, Xiaoguang Zhao, Xiaofei Wang, Pengfei Liu, Honghua Ye, Yanfeng Ma","doi":"10.1038/s41746-026-02393-z","DOIUrl":"https://doi.org/10.1038/s41746-026-02393-z","url":null,"abstract":"Accurate medical image segmentation continues to pose significant challenges, as existing methods often struggle to concurrently achieve efficient global context modeling, precise boundary delineation, and robust generalization. To address these issues, a novel framework named Contextual and Frequency-Guided Mamba Network (CFG-MambaNet) is presented. Specifically, a variable-scale state space block based on Mamba is employed so that long-range dependencies can be captured with linear complexity, efficiently addressing the inefficiency of Transformer-based models in high-resolution medical imaging. Moreover, a frequency-guided representation module is incorporated to explicitly separate global low-frequency structures from high-frequency boundary details, which significantly alleviates the difficulty of segmenting lesions with blurred contours or weak textures. Furthermore, an adaptive context aggregation mechanism is introduced to integrate heterogeneous semantic cues and to consistently highlight clinically critical regions, substantially improving robustness across diverse anatomical scales and morphologies. To further stabilize training and improve boundary adherence, a composite loss combined with deep supervision is employed. Extensive experiments were conducted on four publicly available datasets, including ACDC, Kvasir-SEG, ISIC, and SEED, covering cardiac MRI, endoscopy, dermoscopy, and pathology images.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"114 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089871","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 : 2026-01-31DOI: 10.1038/s41746-026-02395-x
Damià Valero-Bover, David Monterde, Gerard Carot-Sans, Emili Vela, Rubèn González-Colom, Josep Roca, Caridad Pontes, Xabier Michelena, Maria Mercedes Nogueras, Pilar Aparicio, Inmaculada Corrales, Teresa Biec, Isaac Cano, Jordi Piera-Jiménez
Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently understood in its population-level dynamics. Using data from a 10-year population-based cohort of 5.5 million adults in Catalonia, Spain, we quantified multimorbidity-associated clinical complexity using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate ( < P80) to high/very high ( ≥ P80) complexity. Machine learning models identified predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of the individuals who remained alive throughout the analysis period transitioned to high/very high complexity. Baseline AMG score was the strongest predictor of progression, surpassing models relying solely on individual diagnoses. The most prevalent conditions were nutritional and endocrine disorders, anxiety, and hypertension, with notable sequential links between mental and physical disorders. Findings emphasize the need for integrated, patient-centred care strategies and population-based prevention approaches to mitigate multimorbidity progression.
{"title":"Ten-year population-based assessment of multimorbidity burden progression in a regional cohort of 5.5 million adults","authors":"Damià Valero-Bover, David Monterde, Gerard Carot-Sans, Emili Vela, Rubèn González-Colom, Josep Roca, Caridad Pontes, Xabier Michelena, Maria Mercedes Nogueras, Pilar Aparicio, Inmaculada Corrales, Teresa Biec, Isaac Cano, Jordi Piera-Jiménez","doi":"10.1038/s41746-026-02395-x","DOIUrl":"https://doi.org/10.1038/s41746-026-02395-x","url":null,"abstract":"Multimorbidity, a major driver of healthcare demand and clinical complexity, is often addressed in a disease-centric manner and remains insufficiently understood in its population-level dynamics. Using data from a 10-year population-based cohort of 5.5 million adults in Catalonia, Spain, we quantified multimorbidity-associated clinical complexity using the Adjusted Morbidity Groups (AMG) index to predict progression from low/moderate ( < P80) to high/very high ( ≥ P80) complexity. Machine learning models identified predictive factors, while network analyses explored co-occurrence patterns among chronic conditions. During follow-up, 39.2% of the individuals who remained alive throughout the analysis period transitioned to high/very high complexity. Baseline AMG score was the strongest predictor of progression, surpassing models relying solely on individual diagnoses. The most prevalent conditions were nutritional and endocrine disorders, anxiety, and hypertension, with notable sequential links between mental and physical disorders. Findings emphasize the need for integrated, patient-centred care strategies and population-based prevention approaches to mitigate multimorbidity progression.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"16 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146089870","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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