NPJ Digital Medicine最新文献

{"title":"AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology","authors":"Yingrui Ma, Shivprasad Jamdade, Lakshmi Konduri, Heba Sailem","doi":"10.1038/s41746-025-01524-2","DOIUrl":"https://doi.org/10.1038/s41746-025-01524-2","url":null,"abstract":"<p>Digital pathology and artificial intelligence (AI) hold immense transformative potential to revolutionize cancer diagnostics, treatment outcomes, and biomarker discovery. Gaining a deeper understanding of deep learning algorithm methods applied to histopathological data and evaluating their performance on different tasks is crucial for developing the next generation of AI technologies. To this end, we developed AI in Histopathology Explorer (HistoPathExplorer); an interactive dashboard with intelligent tools available at www.histopathexpo.ai. This real-time online resource enables users, including researchers, decision-makers, and various stakeholders, to assess the current landscape of AI applications for specific clinical tasks, analyze their performance, and explore the factors influencing their translation into practice. Moreover, a quality index was defined for evaluating the comprehensiveness of methodological details in published AI methods. HistoPathExplorer highlights opportunities and challenges for AI in histopathology, and offers a valuable resource for creating more effective methods and shaping strategies and guidelines for translating digital pathology applications into clinical practice.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"67 4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599487","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":"Bias recognition and mitigation strategies in artificial intelligence healthcare applications","authors":"Fereshteh Hasanzadeh, Colin B. Josephson, Gabriella Waters, Demilade Adedinsewo, Zahra Azizi, James A. White","doi":"10.1038/s41746-025-01503-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01503-7","url":null,"abstract":"<p>Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"2 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143589626","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 evidence for altered communication patterns in individuals with autism spectrum disorder","authors":"Mehran Turna, Johannes Eckert, Kristina Meier-Böke, Mamaka Narava, Irini Chaliani, Simon B. Eickhoff, Leonhard Schilbach, Juergen Dukart","doi":"10.1038/s41746-025-01545-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01545-x","url":null,"abstract":"<p>Adults with autism spectrum disorder (ASD) may compensate for their social difficulties by resorting to more sequential forms of communication. Here, we study communication preferences in individuals with ASD and neurotypical controls by monitoring smartphone-based communication for verbal, written, and mixed app categories over a period of four months. We find ASD participants to prefer written over verbal communication, underscoring the importance of considering these preferences to facilitate social integration.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"54 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143589624","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":"Ethical considerations in AI for child health and recommendations for child-centered medical AI","authors":"Seo Yi Chng, Mark Jun Wen Tern, Yung Seng Lee, Lionel Tim-Ee Cheng, Jeevesh Kapur, Johan Gunnar Eriksson, Yap Seng Chong, Julian Savulescu","doi":"10.1038/s41746-025-01541-1","DOIUrl":"https://doi.org/10.1038/s41746-025-01541-1","url":null,"abstract":"<p>There does not exist any previous comprehensive review on AI ethics in child health or any guidelines for management, unlike in adult medicine. This review describes ethical principles in AI for child health and provides recommendations for child-centered medical AI. We also introduce the Pediatrics EthicAl Recommendations List for AI (PEARL-AI) framework for clinicians and AI developers to ensure ethical AI enabled systems in healthcare for children.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"40 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143589762","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":"Smartphone conjunctiva photography for malaria risk stratification in asymptomatic school age children","authors":"Shaun G. Hong, Sang Mok Park, Semin Kwon, Haripriya Sakthivel, Sreeram P. Nagappa, Jung Woo Leem, Steven R. Steinhubl, Pascal Ngiruwonsanga, Jean-Louis N. Mangara, Célestin Twizere, Young L. Kim","doi":"10.1038/s41746-025-01548-8","DOIUrl":"https://doi.org/10.1038/s41746-025-01548-8","url":null,"abstract":"<p>Malaria remains a major global health challenge. Although effective control relies on testing all suspected cases, asymptomatic infections in school-age children are frequently overlooked. Advances in retinal imaging and computer vision have enhanced malaria detection. However, noninvasive, point-of-care malaria detection remains unrealized, partly because of the need for specialized equipment. Here we report radiomic analyses of 4302 photographs of the palpebral conjunctiva captured using unmodified smartphone cameras from asymptomatic 405 participants aged 5 to 15 years to predict malaria risk. Our neural network classification model of radiomic features achieves an area under the receiver operating characteristic curve of 0.76 with 95% confidence intervals from 0.68 to 0.84 in distinguishing between malaria-infected and non-infected cases in endemic regions. Photographing the inner eyelid provides the advantages of easy accessibility and direct exposure to the microvasculature. This mobile health approach has the potential for malaria prescreening and managing febrile illness in resource-limited settings.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"40 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143589627","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":"Evaluating search engines and large language models for answering health questions","authors":"Marcos Fernández-Pichel, Juan C. Pichel, David E. Losada","doi":"10.1038/s41746-025-01546-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01546-w","url":null,"abstract":"<p>Search engines (SEs) have traditionally been primary tools for information seeking, but the new large language models (LLMs) are emerging as powerful alternatives, particularly for question-answering tasks. This study compares the performance of four popular SEs, seven LLMs, and retrieval-augmented (RAG) variants in answering 150 health-related questions from the TREC Health Misinformation (HM) Track. Results reveal SEs correctly answer 50–70% of questions, often hindered by many retrieval results not responding to the health question. LLMs deliver higher accuracy, correctly answering about 80% of questions, though their performance is sensitive to input prompts. RAG methods significantly enhance smaller LLMs’ effectiveness, improving accuracy by up to 30% by integrating retrieval evidence.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"31 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143590275","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 mobile health application that supports a patient centered approach to cardiovascular risk management","authors":"Ben Li, Kimia Heydari, Elizabeth J. Enichen, Joseph C. Kvedar","doi":"10.1038/s41746-025-01549-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01549-7","url":null,"abstract":"Digital health tools have the potential to support patients in managing their chronic diseases. Recently, Ullrich and colleagues (2025) introduced PreventiPlaque, a mobile health application that provides patients with up-to-date ultrasound images of their carotid plaques and tracks their lifestyle habits. Through a randomized controlled trial, the authors provide evidence of PreventiPlaque’s efficacy in improving patients’ cardiovascular risk profiles. This study highlights the potential for digital health interventions to provide personalized health information to patients and empower them to take actionable steps to improve their cardiovascular health.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"53 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575421","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":"Red teaming ChatGPT in medicine to yield real-world insights on model behavior","authors":"Crystal T. Chang, Hodan Farah, Haiwen Gui, Shawheen Justin Rezaei, Charbel Bou-Khalil, Ye-Jean Park, Akshay Swaminathan, Jesutofunmi A. Omiye, Akaash Kolluri, Akash Chaurasia, Alejandro Lozano, Alice Heiman, Allison Sihan Jia, Amit Kaushal, Angela Jia, Angelica Iacovelli, Archer Yang, Arghavan Salles, Arpita Singhal, Balasubramanian Narasimhan, Benjamin Belai, Benjamin H. Jacobson, Binglan Li, Celeste H. Poe, Chandan Sanghera, Chenming Zheng, Conor Messer, Damien Varid Kettud, Deven Pandya, Dhamanpreet Kaur, Diana Hla, Diba Dindoust, Dominik Moehrle, Duncan Ross, Ellaine Chou, Eric Lin, Fateme Nateghi Haredasht, Ge Cheng, Irena Gao, Jacob Chang, Jake Silberg, Jason A. Fries, Jiapeng Xu, Joe Jamison, John S. Tamaresis, Jonathan H. Chen, Joshua Lazaro, Juan M. Banda, Julie J. Lee, Karen Ebert Matthys, Kirsten R. Steffner, Lu Tian, Luca Pegolotti, Malathi Srinivasan, Maniragav Manimaran, Matthew Schwede, Minghe Zhang, Minh Nguyen, Mohsen Fathzadeh, Qian Zhao, Rika Bajra, Rohit Khurana, Ruhana Azam, Rush Bartlett, Sang T. Truong, Scott L. Fleming, Shriti Raj, Solveig Behr, Sonia Onyeka, Sri Muppidi, Tarek Bandali, Tiffany Y. Eulalio, Wenyuan Chen, Xuanyu Zhou, Yanan Ding, Ying Cui, Yuqi Tan, Yutong Liu, Nigam Shah, Roxana Daneshjou","doi":"10.1038/s41746-025-01542-0","DOIUrl":"https://doi.org/10.1038/s41746-025-01542-0","url":null,"abstract":"<p>Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy of large language models, but non-model creator-affiliated red teaming is scant in healthcare. We convened teams of clinicians, medical and engineering students, and technical professionals (80 participants total) to stress-test models with real-world clinical cases and categorize inappropriate responses along axes of safety, privacy, hallucinations/accuracy, and bias. Six medically-trained reviewers re-analyzed prompt-response pairs and added qualitative annotations. Of 376 unique prompts (1504 responses), 20.1% were inappropriate (GPT-3.5: 25.8%; GPT-4.0: 16%; GPT-4.0 with Internet: 17.8%). Subsequently, we show the utility of our benchmark by testing GPT-4o, a model released after our event (20.4% inappropriate). 21.5% of responses appropriate with GPT-3.5 were inappropriate in updated models. We share insights for constructing red teaming prompts, and present our benchmark for iterative model assessments.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"39 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575422","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":"Identifying and forecasting importation and asymptomatic spreaders of multi-drug resistant organisms in hospital settings","authors":"Jiaming Cui, Jack Heavey, Eili Klein, Gregory R. Madden, Costi D. Sifri, Anil Vullikanti, B. Aditya Prakash","doi":"10.1038/s41746-025-01529-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01529-x","url":null,"abstract":"<p>Healthcare-associated infections (HAIs) from multi-drug resistant organisms (MDROs) pose a significant challenge for healthcare systems. Patients can arrive at hospitals already infected (\"importation”) or acquire infections during their stay (\"nosocomial infection”). Many cases, often asymptomatic, complicate rapid identification due to testing limitations and delays. Although recent advancements in mathematical modeling and machine learning have aimed to identify at-risk patients, these methods face challenges: transmission models often overlook valuable electronic health record (EHR) data, while machine learning approaches typically lack mechanistic insights into underlying processes. To address these issues, we propose NeurABM, a novel framework that integrates neural networks and agent-based models (ABM) to leverage the strengths of both methods. NeurABM simultaneously learns a neural network for patient-level importation predictions and an ABM for infection identification. Our findings show that NeurABM significantly outperforms existing methods, marking a breakthrough in accurately identifying importation cases and forecasting future nosocomial infections in clinical practice.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"8 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575423","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":"Unregulated large language models produce medical device-like output","authors":"Gary E. Weissman, Toni Mankowitz, Genevieve P. Kanter","doi":"10.1038/s41746-025-01544-y","DOIUrl":"https://doi.org/10.1038/s41746-025-01544-y","url":null,"abstract":"<p>Large language models (LLMs) show considerable promise for clinical decision support (CDS) but none is currently authorized by the Food and Drug Administration (FDA) as a CDS device. We evaluated whether two popular LLMs could be induced to provide device-like CDS output. We found that LLM output readily produced device-like decision support across a range of scenarios, suggesting a need for regulation if LLMs are formally deployed for clinical use.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"53 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575134","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}