Akshai Parakkal Sreenivasan, Aina Vaivade, Yassine Noui, Payam Emami Khoonsari, Joachim Burman, Ola Spjuth, Kim Kultima
{"title":"Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis","authors":"Akshai Parakkal Sreenivasan, Aina Vaivade, Yassine Noui, Payam Emami Khoonsari, Joachim Burman, Ola Spjuth, Kim Kultima","doi":"10.1038/s41746-025-01616-z","DOIUrl":"https://doi.org/10.1038/s41746-025-01616-z","url":null,"abstract":"<p>Accurate assessment of progression and disease course in multiple sclerosis (MS) is vital for timely and appropriate clinical intervention. The gradual transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS) is often diagnosed retrospectively with a typical delay of three years. To address this diagnostic delay, we developed a predictive model that uses electronic health records to distinguish between RRMS and SPMS at each individual visit. To enable reliable predictions, conformal prediction was implemented at the individual patient level with a confidence of 93%. Our model accurately predicted the change in diagnosis from RRMS to SPMS for patients who transitioned during the study period. Additionally, we identified new patients who, with high probability, are in the transition phase but have not yet received a clinical diagnosis. Our methodology aids in monitoring MS progression and proactively identifying transitioning patients. An anonymized model is available at https://msp-tracker.serve.scilifelab.se/.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"41 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143872794","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}
Vijaytha Muralidharan, Madelena Y. Ng, Shada AlSalamah, Sameer Pujari, Kanika Kalra, Rajeshwari Singh, Denise Schalet, Tobi Olantuji, Rohit Malpani, Rubeta N. Matin, Jesutofunmi A. Omiye, Yu Zhao, Anita Sands, Andreas Reis, Jose Eduardo Diaz Mendoza, Tina Hernandez-Boussard, Roxana Daneshjou, Alain B. Labrique
{"title":"Global Initiative on AI for Health (GI-AI4H): strategic priorities advancing governance across the United Nations","authors":"Vijaytha Muralidharan, Madelena Y. Ng, Shada AlSalamah, Sameer Pujari, Kanika Kalra, Rajeshwari Singh, Denise Schalet, Tobi Olantuji, Rohit Malpani, Rubeta N. Matin, Jesutofunmi A. Omiye, Yu Zhao, Anita Sands, Andreas Reis, Jose Eduardo Diaz Mendoza, Tina Hernandez-Boussard, Roxana Daneshjou, Alain B. Labrique","doi":"10.1038/s41746-025-01618-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01618-x","url":null,"abstract":"<p>The Global Initiative on Artificial Intelligence for Health (GI-AI4H), established by the World Health Organization, serves to harmonize governance standards for artificial intelligence (AI). The GI-AI4H spearheads novel on-the-ground efforts, especially in low- and middle-income countries, to advance ethical, regulatory, implementation, and operational dimensions of global governance for health AI. The GI-AI4H’s efforts across the United Nations drives safe, ethical, equitable, and sustainable health AI use for the global community.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"70 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866838","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}
Sushmita Adhikari, Iftikhar Ahmed, Deepak Bajracharya, Bishesh Khanal, Chandrasegarar Solomon, Kapila Jayaratne, Khondaker Abdullah Al Mamum, Muhammad Shamim Hayder Talukder, Sunila Shakya, Suresh Manandhar, Zahid Ali Memon, Moinul Haque Chowdhury, Ihtesham Ul Islam, Noor Sabah Rakhshani, M Imran Khan
{"title":"Author Correction: Transforming healthcare through just, equitable and quality driven artificial intelligence solutions in South Asia.","authors":"Sushmita Adhikari, Iftikhar Ahmed, Deepak Bajracharya, Bishesh Khanal, Chandrasegarar Solomon, Kapila Jayaratne, Khondaker Abdullah Al Mamum, Muhammad Shamim Hayder Talukder, Sunila Shakya, Suresh Manandhar, Zahid Ali Memon, Moinul Haque Chowdhury, Ihtesham Ul Islam, Noor Sabah Rakhshani, M Imran Khan","doi":"10.1038/s41746-025-01639-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01639-6","url":null,"abstract":"","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":"218"},"PeriodicalIF":12.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12019597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143974856","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}
Jiho Lee, Junseung Mun, Minhye Choo, Sung-Min Park
{"title":"Predictive modeling of hemodynamics during viscerosensory neurostimulation via neural computation mechanism in the brainstem","authors":"Jiho Lee, Junseung Mun, Minhye Choo, Sung-Min Park","doi":"10.1038/s41746-025-01635-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01635-w","url":null,"abstract":"<p>Neurostimulation for cardiovascular control faces challenges due to the lack of predictive modeling for stimulus-driven dynamic responses, which is crucial for precise neuromodulation via quality feedback. We address this by employing a digital twin approach that leverages computational mechanisms underlying neuro-hemodynamic responses during neurostimulation. Our results emphasize the computational role of the nucleus tractus solitarius (NTS) in the brainstem in determining these responses. The intrinsic neural circuit within the NTS harbors collective dynamics residing in a low-dimensional latent space, which effectively captures stimulus-driven hemodynamic perturbations. Building on this, we developed a digital twin framework for individually optimized predictive modeling of neuromodulatory outcomes. This framework potentially enables the design of closed-loop neurostimulation systems for precise hemodynamic control. Consequently, our digital twin based on neural computation mechanisms marks an advancement in the artificial regulation of internal organs, paving the way for precise translational medicine to treat chronic diseases.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"32 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866764","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}
Brenda Y. Miao, Christopher Y. K. Williams, Ebenezer Chinedu-Eneh, Travis Zack, Emily Alsentzer, Atul J. Butte, Irene Y. Chen
{"title":"Understanding contraceptive switching rationales from real world clinical notes using large language models","authors":"Brenda Y. Miao, Christopher Y. K. Williams, Ebenezer Chinedu-Eneh, Travis Zack, Emily Alsentzer, Atul J. Butte, Irene Y. Chen","doi":"10.1038/s41746-025-01615-0","DOIUrl":"https://doi.org/10.1038/s41746-025-01615-0","url":null,"abstract":"<p>Understanding reasons for treatment switching is of significant medical interest, but these factors are often only found in unstructured clinical notes and can be difficult to extract. We evaluated the zero-shot abilities of GPT-4 and eight other open-source large language models (LLMs) to extract contraceptive switching information from 1964 clinical notes derived from the UCSF Information Commons dataset. GPT-4 extracted the contraceptives started and stopped at each switch with microF1 scores of 0.85 and 0.88, respectively, compared to 0.81 and 0.88 for the best open-source model. When evaluated by clinical experts, GPT-4 extracted reasons for switching with an accuracy of 91.4% (2.2% hallucination rate). Transformer-based topic modeling identified patient preference, adverse events, and insurance coverage as key reasons. These findings demonstrate the value of LLMs in identifying complex treatment factors and provide insights into reasons for contraceptive switching in real-world settings.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"138 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866622","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}
Yushen Dai, Jiaying Li, Yan Li, Frances Kam Yuet Wong, Mengqi Li, Chen Li, Ye Jia, Yueying Wang, Janelle Yorke
{"title":"A scoping review on the role of virtual walking intervention in enhancing wellness","authors":"Yushen Dai, Jiaying Li, Yan Li, Frances Kam Yuet Wong, Mengqi Li, Chen Li, Ye Jia, Yueying Wang, Janelle Yorke","doi":"10.1038/s41746-025-01609-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01609-y","url":null,"abstract":"<p>Virtual walking has the potential to be an adjunct to traditional physical therapy. This scoping review aims to synthesize evidence on the characteristics, effectiveness, feasibility, and neurological mechanism of virtual walking interventions on health-related outcomes. Articles in English were retrieved from twelve databases (January 2014–October 2024). Thirteen interventional studies were included, focusing on three types of virtual walking: passive observing moving (71.4%), arm swing locomotion (21.5%), and foot tracking locomotion (7.1%). Most studies (84.6%) involved individuals with spinal cord injuries, while the remaining studies focused on lower back pain (7.7%) and lower limb pain (7.7%). Over 70% of studies lasted 11–20 min, 1–5 weekly sessions for 10–14 days. Statistically significant findings included pain reduction (84.6%), improved physical function (mobility and muscle strength), and reduced depression. Mild adverse effects (fatigue and dizziness) were transient. Neurological evidence indicates somatosensory cortex activation during virtual walking, possibly linked to neuropathic pain.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"108 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862922","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":"Consumer insights from a feasibility study on remote and extended use of a novel non-invasive wearable fetal electrocardiogram monitor","authors":"Debjyoti Karmakar, Tarini Paul, Emerson Keenan, Marimuthu Palaniswami, Kaitlin Constable, Erica Spessot, Fiona Brownfoot","doi":"10.1038/s41746-025-01628-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01628-9","url":null,"abstract":"<p>The COVID-19 pandemic accelerated the adoption of telehealth and remote monitoring in obstetric care. This study assessed pregnant patients’ perceptions before and after using a novel non-invasive fetal electrocardiogram (NI-FECG) device. The trial is prospectively registered on the Australia New Zealand Clinical Trials Registry (ANZCTRN12621001260819; submitted June 9th, 2021; approved September 17th, 2021). Seventy participants from 36 weeks’ gestation completed pre- and post-use surveys. Interest in continuous and home fetal monitoring was high (79% and 90%, respectively). Post-use, 89% reported satisfaction; over 90% comfortable wearing and removing the sensor. Extended use was acceptable to 76%, and only 3% reported high skin irritation. Sentiment analysis highlighted themes of reassurance, convenience, and reduced anxiety. Some suggested smaller, wireless design. Analysis by natural language processing and clustering provided deeper insights. Findings support strong interest in at-home fetal monitoring; further refinement and education are needed to enhance acceptability. Future research should assess long-term effects on anxiety and clinical outcomes.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"30 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853398","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":"Germany and Europe lead digital innovation and AI with collaborative health data use at continental level","authors":"Daniel C. Baumgart, Joseph C. Kvedar","doi":"10.1038/s41746-025-01631-0","DOIUrl":"https://doi.org/10.1038/s41746-025-01631-0","url":null,"abstract":"Collaborative use of population-level health data and artificial intelligence is essential for achieving precision health through a learning health system. Two groundbreaking initiatives—the European Health Data Space (EHDS), covering 449 million EU citizens, and Germany’s forthcoming Health Data Lab, providing access to data from 75 million insured individuals (90% of the country’s population)—offer unprecedented opportunities to advance digital health innovation and research with global impact.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"33 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853399","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":"Advancing perioperative care with digital applications and wearables","authors":"Ben Li, Arjun Mahajan, Dylan Powell","doi":"10.1038/s41746-025-01620-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01620-3","url":null,"abstract":"The rapid increase in real-time health information collected from wearable devices has allowed digital biomarkers to emerge as a promising tool to support perioperative care, including surgical prehabilitation, intra-operative guidance, and post-operative monitoring. Important challenges include the accuracy of generated information, data security risks, and slow adoption of new technologies. Active stakeholder engagement and following existing digital biomarker development/implementation frameworks may support using this technology to improve surgical outcomes.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"91 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849602","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}
Roberto Vega, Masood Dehghan, Arun Nagdev, Brian Buchanan, Jeevesh Kapur, Jacob L. Jaremko, Dornoosh Zonoobi
{"title":"Overcoming barriers in the use of artificial intelligence in point of care ultrasound","authors":"Roberto Vega, Masood Dehghan, Arun Nagdev, Brian Buchanan, Jeevesh Kapur, Jacob L. Jaremko, Dornoosh Zonoobi","doi":"10.1038/s41746-025-01633-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01633-y","url":null,"abstract":"<p>Point-of-care ultrasound is a portable, low-cost imaging technology focused on answering specific clinical questions in real time. Artificial intelligence amplifies its capabilities by aiding clinicians in the acquisition and interpretation of the images; however, there are growing concerns on its effectiveness and trustworthiness. Here, we address key issues such as population bias, explainability and training of artificial intelligence in this field and propose approaches to ensure clinical effectiveness.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"10 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849642","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}