Pub Date : 2026-01-22DOI: 10.1038/s41551-026-01611-z
It is an exciting time for biomedical engineering, with advances rapidly reshaping the forefront of translational research and medicine. Here we highlight some areas and technologies that we are particularly excited about for the coming years.
{"title":"On the horizon in biomedical engineering","authors":"","doi":"10.1038/s41551-026-01611-z","DOIUrl":"10.1038/s41551-026-01611-z","url":null,"abstract":"It is an exciting time for biomedical engineering, with advances rapidly reshaping the forefront of translational research and medicine. Here we highlight some areas and technologies that we are particularly excited about for the coming years.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"10 1","pages":"1-2"},"PeriodicalIF":26.8,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41551-026-01611-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016503","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 : 2026-01-22DOI: 10.1038/s41551-025-01587-2
Zifeng Wang,Benjamin Danek,Ziwei Yang,Zheng Chen,Jimeng Sun
Large language models (LLMs) can generate impressive data visualizations from simple requests, yet their accuracy remains underexplored. Here we present a benchmark of 293 coding tasks derived from 39 studies across 7 biomedical research areas, including biomarkers, integrative analysis, genomic profiling, molecular characterization, therapeutic response, translational research and pan-cancer analysis. Benchmarking eight proprietary and eight open-source LLMs under various prompting strategies reveals an overall accuracy below 40%. This low accuracy raises serious concerns about the risk of propagating incorrect scientific findings when blindly relying on AI-generated analyses. Therefore, we develop an AI agent that begins with and iteratively refines an analysis plan before generating code, achieving 74% accuracy. We embody this insight in a platform that enables users to codevelop analysis plans with LLMs and execute them within an integrated environment. In a user study with five medical researchers, the platform enabled users to complete over 80% of the analysis code for three studies.
{"title":"Making large language models reliable data science programming copilots for biomedical research.","authors":"Zifeng Wang,Benjamin Danek,Ziwei Yang,Zheng Chen,Jimeng Sun","doi":"10.1038/s41551-025-01587-2","DOIUrl":"https://doi.org/10.1038/s41551-025-01587-2","url":null,"abstract":"Large language models (LLMs) can generate impressive data visualizations from simple requests, yet their accuracy remains underexplored. Here we present a benchmark of 293 coding tasks derived from 39 studies across 7 biomedical research areas, including biomarkers, integrative analysis, genomic profiling, molecular characterization, therapeutic response, translational research and pan-cancer analysis. Benchmarking eight proprietary and eight open-source LLMs under various prompting strategies reveals an overall accuracy below 40%. This low accuracy raises serious concerns about the risk of propagating incorrect scientific findings when blindly relying on AI-generated analyses. Therefore, we develop an AI agent that begins with and iteratively refines an analysis plan before generating code, achieving 74% accuracy. We embody this insight in a platform that enables users to codevelop analysis plans with LLMs and execute them within an integrated environment. In a user study with five medical researchers, the platform enabled users to complete over 80% of the analysis code for three studies.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"58 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021521","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 : 2026-01-21DOI: 10.1038/s41551-025-01594-3
Lukas Ehlen, Martí Farrera-Sal, Martin Szyska, Janine Arndt, Simon Schallenberg, Cedric Scholz, Mingxing Yang, Claudia Vollbrecht, Anna Löwa, Rebecca Friedrich, Marco Mai, Lena Peter, Samira Picht, Sarah Schulenberg, Daniel Geray, Gabriela Korus, Anke Sommerfeld, Denise Treue, Julia Strauchmann, Aron Elsner, Jonas Kath, Valeria Fernandez Vallone, Maria Joosten, Franka Klatte-Schulz, Ansgar Petersen, Harald Stachelscheid, Dimitrios L. Wagner, Claudia Spies, Jens-Carsten Rückert, Andreas C. Hocke, Julia K. Polansky, Regina Stark, Oliver Klein, Michael Schmueck-Henneresse
Lung cancer, the leading cause of cancer-related mortality, presents major challenges for both standard therapies and chimeric antigen receptor (CAR) T cell therapy due to tumour heterogeneity and resistance. Preclinical models that capture patient-specific factors are essential for personalizing treatment decisions. Here we show that matched lung tumouroids and healthy lung organoids derived from patients provide a robust platform for studying therapy responses. The tumouroids faithfully retained the molecular and histological identity of the original tumours, as confirmed by genomic, epigenomic and proteomic analyses, and accurately replicated individual patient responses to standard-of-care therapies. Importantly, the platform also revealed patient-specific CAR T cell responses, uncovering a complex interplay between target antigen density and broader, tumour-intrinsic resistance programmes. By capturing these individualized factors, our model supports rational patient selection for CAR T cell therapy in lung cancer and provides a framework for designing CAR T cells tailored to overcome resistance mechanisms in solid tumours.
{"title":"Lung tumouroids as a testing platform for precision CAR T cell therapy","authors":"Lukas Ehlen, Martí Farrera-Sal, Martin Szyska, Janine Arndt, Simon Schallenberg, Cedric Scholz, Mingxing Yang, Claudia Vollbrecht, Anna Löwa, Rebecca Friedrich, Marco Mai, Lena Peter, Samira Picht, Sarah Schulenberg, Daniel Geray, Gabriela Korus, Anke Sommerfeld, Denise Treue, Julia Strauchmann, Aron Elsner, Jonas Kath, Valeria Fernandez Vallone, Maria Joosten, Franka Klatte-Schulz, Ansgar Petersen, Harald Stachelscheid, Dimitrios L. Wagner, Claudia Spies, Jens-Carsten Rückert, Andreas C. Hocke, Julia K. Polansky, Regina Stark, Oliver Klein, Michael Schmueck-Henneresse","doi":"10.1038/s41551-025-01594-3","DOIUrl":"https://doi.org/10.1038/s41551-025-01594-3","url":null,"abstract":"Lung cancer, the leading cause of cancer-related mortality, presents major challenges for both standard therapies and chimeric antigen receptor (CAR) T cell therapy due to tumour heterogeneity and resistance. Preclinical models that capture patient-specific factors are essential for personalizing treatment decisions. Here we show that matched lung tumouroids and healthy lung organoids derived from patients provide a robust platform for studying therapy responses. The tumouroids faithfully retained the molecular and histological identity of the original tumours, as confirmed by genomic, epigenomic and proteomic analyses, and accurately replicated individual patient responses to standard-of-care therapies. Importantly, the platform also revealed patient-specific CAR T cell responses, uncovering a complex interplay between target antigen density and broader, tumour-intrinsic resistance programmes. By capturing these individualized factors, our model supports rational patient selection for CAR T cell therapy in lung cancer and provides a framework for designing CAR T cells tailored to overcome resistance mechanisms in solid tumours.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"48 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006079","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}
Successful surgical resection of solid tumours requires highly reliable real-time intraoperative tools to accurately delineate tumour boundaries, which remains challenging in routine clinical standards. Here, we identify endogenous substances with intense autofluorescence in the second near-infrared window (NIR-II, 1,000-1,700 nm) that are abundant in human liver tissues but negligible in cancerous tissues. Inspired by this discovery, we develop a label-free and wide-field imaging approach, named tissue autofluorescence NIR-II imaging (TANI) for visualizing human liver malignancies. TANI demonstrates exceptional contrast (7.69 ± 0.52), sensitivity (97.8%) and specificity (98.4%) in delineating various liver malignancies, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma and liver metastasis from cirrhotic or non-cirrhotic livers, outperforming routine fluorescence-guided surgery and conventional autofluorescence imaging in the visible (400-650 nm) or first near-infrared (700-900 nm) window. The excellent performance of TANI remains unaffected by cancer grade/stage, benign lesions or blood/bile contamination. These findings represent a promising advance in intraoperative decision-making and suggest a strong correlation between near-infrared autofluorescence and diseases. We believe that clarifying the molecular insights underlying these autofluorescent substances may provide new diagnostic directions.
{"title":"Label-free tissue NIR-II autofluorescence imaging for visualization of human liver malignancy.","authors":"Haisheng He,Wenwei Zhu,Han Miao,Shangfeng Wang,Zunguo Du,Hongxin Zhang,Jiang Ming,Ben Shi,Hao Wang,Jianping Qi,Yong Fan,Wei Wu,Dongyuan Zhao,Lun-Xiu Qin,Fan Zhang","doi":"10.1038/s41551-025-01593-4","DOIUrl":"https://doi.org/10.1038/s41551-025-01593-4","url":null,"abstract":"Successful surgical resection of solid tumours requires highly reliable real-time intraoperative tools to accurately delineate tumour boundaries, which remains challenging in routine clinical standards. Here, we identify endogenous substances with intense autofluorescence in the second near-infrared window (NIR-II, 1,000-1,700 nm) that are abundant in human liver tissues but negligible in cancerous tissues. Inspired by this discovery, we develop a label-free and wide-field imaging approach, named tissue autofluorescence NIR-II imaging (TANI) for visualizing human liver malignancies. TANI demonstrates exceptional contrast (7.69 ± 0.52), sensitivity (97.8%) and specificity (98.4%) in delineating various liver malignancies, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma and liver metastasis from cirrhotic or non-cirrhotic livers, outperforming routine fluorescence-guided surgery and conventional autofluorescence imaging in the visible (400-650 nm) or first near-infrared (700-900 nm) window. The excellent performance of TANI remains unaffected by cancer grade/stage, benign lesions or blood/bile contamination. These findings represent a promising advance in intraoperative decision-making and suggest a strong correlation between near-infrared autofluorescence and diseases. We believe that clarifying the molecular insights underlying these autofluorescent substances may provide new diagnostic directions.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"63 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005415","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}
Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. We present BioPathNet, a graph neural network framework based on the neural Bellman-Ford network (NBFNet), addressing limitations of traditional representation-based learning methods through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability, and allowing visualization of influential paths and biological validation. BioPathNet leverages a background regulatory graph for enhanced message passing and uses stringent negative sampling to improve precision and scalability. BioPathNet outperforms or matches existing methods across diverse tasks including gene function annotation, drug-disease indication, synthetic lethality and lncRNA-target interaction prediction. Our study identifies promising additional drug indications for diseases such as acute lymphoblastic leukaemia and Alzheimer's disease, validated by medical experts and clinical trials. In addition, we prioritize putative synthetic lethal gene pairs and regulatory lncRNA-target interactions. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights.
{"title":"Enhancing link prediction in biomedical knowledge graphs with BioPathNet.","authors":"Emy Yue Hu,Svitlana Oleshko,Samuele Firmani,Hui Cheng,Zhaocheng Zhu,Maria Ulmer,Matthias Arnold,Maria Colomé-Tatché,Jian Tang,Sophie Xhonneux,Annalisa Marsico","doi":"10.1038/s41551-025-01598-z","DOIUrl":"https://doi.org/10.1038/s41551-025-01598-z","url":null,"abstract":"Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. We present BioPathNet, a graph neural network framework based on the neural Bellman-Ford network (NBFNet), addressing limitations of traditional representation-based learning methods through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability, and allowing visualization of influential paths and biological validation. BioPathNet leverages a background regulatory graph for enhanced message passing and uses stringent negative sampling to improve precision and scalability. BioPathNet outperforms or matches existing methods across diverse tasks including gene function annotation, drug-disease indication, synthetic lethality and lncRNA-target interaction prediction. Our study identifies promising additional drug indications for diseases such as acute lymphoblastic leukaemia and Alzheimer's disease, validated by medical experts and clinical trials. In addition, we prioritize putative synthetic lethal gene pairs and regulatory lncRNA-target interactions. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights.","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":"276 1","pages":""},"PeriodicalIF":28.1,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005413","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 : 2026-01-19DOI: 10.1038/s41551-025-01604-4
Han Ouyang, Dongjie Jiang, Yiran Hu, Sijing Cheng, Zhengmin Zhang, Bojing Shi, Engui Wang, Jiangtao Xue, Yizhu Shan, Lingling Xu, Yang Zou, Sixian Weng, Hui Li, Hongxia Niu, Min Gu, Lin Luo, Shengyu Chao, Puchuan Tan, Yan Yao, Ningning Wang, Yubo Fan, Zhong Lin Wang, Wei Hua, Zhou Li
Lifelong pacing is one of the ultimate goals of cardiac pacemakers. However, meeting the critical energy condition for lifelong service is a tremendous challenge. Here we report a symbiotic transcatheter pacemaker that regenerates electric energy from heart motion via electromagnetic induction and surpasses the critical energy condition for lifelong service. The pacemaker can be closely integrated with the body owing to favourable biocompatibility and hemocompatibility, and its small size enables interventional delivery. To minimize energy loss and eliminate mechanical collision and friction, we propose a straightforward magnetic levitation energy cache structure. The energy regeneration module has a near-zero boot threshold, high kinetic energy conversion efficiency and intracardiac root mean square output power. We show the energy regeneration and therapeutic function of the symbiotic transcatheter pacemaker over a month-long autonomous operation in a porcine model of brady-arrhythmia. These advances may provide a potential path to extend the service life of pacemakers to the level of the natural heart.
{"title":"Symbiotic transcatheter pacemaker for lifelong energy regeneration and therapeutic function in porcine disease model.","authors":"Han Ouyang, Dongjie Jiang, Yiran Hu, Sijing Cheng, Zhengmin Zhang, Bojing Shi, Engui Wang, Jiangtao Xue, Yizhu Shan, Lingling Xu, Yang Zou, Sixian Weng, Hui Li, Hongxia Niu, Min Gu, Lin Luo, Shengyu Chao, Puchuan Tan, Yan Yao, Ningning Wang, Yubo Fan, Zhong Lin Wang, Wei Hua, Zhou Li","doi":"10.1038/s41551-025-01604-4","DOIUrl":"https://doi.org/10.1038/s41551-025-01604-4","url":null,"abstract":"<p><p>Lifelong pacing is one of the ultimate goals of cardiac pacemakers. However, meeting the critical energy condition for lifelong service is a tremendous challenge. Here we report a symbiotic transcatheter pacemaker that regenerates electric energy from heart motion via electromagnetic induction and surpasses the critical energy condition for lifelong service. The pacemaker can be closely integrated with the body owing to favourable biocompatibility and hemocompatibility, and its small size enables interventional delivery. To minimize energy loss and eliminate mechanical collision and friction, we propose a straightforward magnetic levitation energy cache structure. The energy regeneration module has a near-zero boot threshold, high kinetic energy conversion efficiency and intracardiac root mean square output power. We show the energy regeneration and therapeutic function of the symbiotic transcatheter pacemaker over a month-long autonomous operation in a porcine model of brady-arrhythmia. These advances may provide a potential path to extend the service life of pacemakers to the level of the natural heart.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":26.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146003922","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 : 2026-01-16DOI: 10.1038/s41551-025-01603-5
Yang Zhang, Shuai Na, Jonathan J Russin, Karteekeya Sastry, Li Lin, Junfu Zheng, Yilin Luo, Xin Tong, Yujin An, Peng Hu, Konstantin Maslov, Tze-Woei Tan, Charles Y Liu, Lihong V Wang
Imaging the human body's morphological and angiographic information is essential for diagnosing, monitoring and treating medical conditions. Here we combine the power of ultrasonography for morphological assessment of soft tissue with photoacoustic tomography (PAT) for visualizing blood vessels to enable three-dimensional (3D) panoramic imaging. Specifically, fast panoramic rotational ultrasound tomography and PAT are integrated for hybrid rotational ultrasound and photoacoustic tomography (RUS-PAT), which obtains 3D ultrasound structural and PAT angiographic images of the human body quasi-simultaneously. The rotational ultrasound tomography functionality is achieved using a single-element ultrasonic transducer for ultrasound transmission and rotating arc-shaped arrays for 3D panoramic detection. By switching the acoustic source to a light source, the system is conveniently converted to PAT mode to acquire angiographic images in the same region. Using RUS-PAT, we successfully imaged the human head, breast, hand and foot with a 10-cm-diameter field of view, submillimetre isotropic resolution and 10 s imaging time for each modality. 3D RUS-PAT is a powerful tool for high-speed, dual-contrast imaging of the human body with potential for rapid clinical translation.
{"title":"Rotational ultrasound and photoacoustic tomography of the human body.","authors":"Yang Zhang, Shuai Na, Jonathan J Russin, Karteekeya Sastry, Li Lin, Junfu Zheng, Yilin Luo, Xin Tong, Yujin An, Peng Hu, Konstantin Maslov, Tze-Woei Tan, Charles Y Liu, Lihong V Wang","doi":"10.1038/s41551-025-01603-5","DOIUrl":"10.1038/s41551-025-01603-5","url":null,"abstract":"<p><p>Imaging the human body's morphological and angiographic information is essential for diagnosing, monitoring and treating medical conditions. Here we combine the power of ultrasonography for morphological assessment of soft tissue with photoacoustic tomography (PAT) for visualizing blood vessels to enable three-dimensional (3D) panoramic imaging. Specifically, fast panoramic rotational ultrasound tomography and PAT are integrated for hybrid rotational ultrasound and photoacoustic tomography (RUS-PAT), which obtains 3D ultrasound structural and PAT angiographic images of the human body quasi-simultaneously. The rotational ultrasound tomography functionality is achieved using a single-element ultrasonic transducer for ultrasound transmission and rotating arc-shaped arrays for 3D panoramic detection. By switching the acoustic source to a light source, the system is conveniently converted to PAT mode to acquire angiographic images in the same region. Using RUS-PAT, we successfully imaged the human head, breast, hand and foot with a 10-cm-diameter field of view, submillimetre isotropic resolution and 10 s imaging time for each modality. 3D RUS-PAT is a powerful tool for high-speed, dual-contrast imaging of the human body with potential for rapid clinical translation.</p>","PeriodicalId":19063,"journal":{"name":"Nature Biomedical Engineering","volume":" ","pages":""},"PeriodicalIF":26.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145990053","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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