Health and Technology最新文献

Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy. 统一奥德赛：罕见疾病诊断和治疗的人工智能。

IF 2.8

Health and Technology Pub Date : 2026-03-10 DOI: 10.1007/s12553-026-01057-y

Mai-Lan Ho, Marinka Zitnik, Ronen Azachi, Sanjay Basu, Pranav Rajpurkar, Richard Sidlow

{"title":"Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy.","authors":"Mai-Lan Ho, Marinka Zitnik, Ronen Azachi, Sanjay Basu, Pranav Rajpurkar, Richard Sidlow","doi":"10.1007/s12553-026-01057-y","DOIUrl":"10.1007/s12553-026-01057-y","url":null,"abstract":"Purpose: To summarize current challenges in rare disease (RD) diagnosis and therapy, highlight recent advances in artificial intelligence (AI) for RDs, and propose a model future state for RD patient care.Methods: Multidisciplinary expert-led narrative review summarizing modern practical challenges and rate-limiting steps in RD patient care, citing key clinical and research considerations with respect to regulatory and economic constraints.Results: Over 10,000 known RDs collectively affect 1 in 10 Americans, a total of over 30 million people. Annually, RDs account for over $1 trillion of annual US healthcare expenditures. Despite advances in genomic medicine, it takes 5-8 years on average to obtain an accurate diagnosis, and less than 5% of RDs currently have FDA-approved therapies. In this article, we review the history of RD diagnosis and current healthcare gaps underlying the major failures in patient care. Next, we will highlight emerging advances in genomic medicine and AI that are rapidly changing the RD landscape. Finally, we propose a target future state that integrates agentic AI for diagnosis and therapy with human-in-the-loop feedback.Conclusions: The rare disease diagnostic and therapeutic odyssey represents healthcare's most persistent failure mode. Ongoing challenges for clinical implementation involve biological modeling, manufacturing bottlenecks, and clinical trial design. We propose strategies for artificial intelligence to restructure the traditional sequence of diagnosis-then-therapy into a proactive orchestrated system delivering personalized cures at scale.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147622811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and physicochemical characterisation of a biodegradable Ho-166-polycaprolactone rod formulation for targeted theragnostic radiation therapy of liver tumours 用于肝肿瘤靶向放射治疗的可生物降解ho -166-聚己内酯棒制剂的开发和理化特性

Health and Technology Pub Date : 2025-05-30 DOI: 10.1007/s12553-025-00982-8

Asseel Hisham Alregib, Yin How Wong, Azahari Kasbollah, Basri Johan Jeet Abdullah, Chai Hong Yeong

引用次数: 0

Task effectiveness, usability, and acceptability of mHealth technologies among older adults at risk for cardiovascular disease: a feasibility study. 在有心血管疾病风险的老年人中，移动健康技术的任务有效性、可用性和可接受性：可行性研究

IF 2.8

Health and Technology Pub Date : 2025-05-01 Epub Date: 2025-04-23 DOI: 10.1007/s12553-025-00969-5

Andrew Thomas Reyes, Dion Candelaria, Reimund Serafica, Janett A Hildebrand, Marysol Cacciata, Axel Santa Maria, Jung-Ah Lee, Anna Strömberg, Lorraine S Evangelista

{"title":"Task effectiveness, usability, and acceptability of mHealth technologies among older adults at risk for cardiovascular disease: a feasibility study.","authors":"Andrew Thomas Reyes, Dion Candelaria, Reimund Serafica, Janett A Hildebrand, Marysol Cacciata, Axel Santa Maria, Jung-Ah Lee, Anna Strömberg, Lorraine S Evangelista","doi":"10.1007/s12553-025-00969-5","DOIUrl":"10.1007/s12553-025-00969-5","url":null,"abstract":"Purpose: New research shows the benefits of mobile health (mHealth) interventions for older adults. However, older adults adopt digital technology less than younger ones. This study measures the task effectiveness, perceived usability, and acceptability of a mHealth intervention (i.e., Get FIT +) consisting of a wearable activity tracker, access to the MyFitnessPal app, and personalized text messages to promote healthy behaviors in older adults.Methods: Participants used the Get FIT + intervention for 12 weeks and engaged in monthly clinic visits with an advanced practice nurse (APRN) to monitor their progress. The monthly sessions instructed them to use the think-aloud process while doing specific tasks (e.g., using the app). Participants also completed the USABILITY Survey and provided feedback on the intervention's acceptability after the 12-week trial.Results: Thirty older adults (mean age 66.6 ± 5.9 years, 60% female, 60% married, 50% Asian, 37% White, and 13% Hispanic) participated in this sub-analysis. Participants were able to complete the assigned tasks efficiently. The usability satisfaction assessment suggests a high level of satisfaction. The participants responded positively to Get FIT + and successfully incorporated it into their routines.Conclusions: Our findings show that Get FIT + technologies, including smartphones, smartphone-based applications, and integrated sensors, are practical, usable, and acceptable for older adults at risk for cardiovascular disease. These scalable, low-cost technologies offer methods to monitor and promote a healthy lifestyle and enhance overall well-being.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"15 3","pages":"531-538"},"PeriodicalIF":2.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144951528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The role of AI in mitigating the impact of radiologist shortages: a systematised review. 人工智能在缓解放射科医生短缺影响中的作用：系统回顾。

IF 3.1

Health and Technology Pub Date : 2025-01-01 Epub Date: 2025-04-25 DOI: 10.1007/s12553-025-00970-y

Nebil Achour, Tomas Zapata, Yousef Saleh, Barbara Pierscionek, Natasha Azzopardi-Muscat, David Novillo-Ortiz, Cathal Morgan, Mafaten Chaouali

{"title":"The role of AI in mitigating the impact of radiologist shortages: a systematised review.","authors":"Nebil Achour, Tomas Zapata, Yousef Saleh, Barbara Pierscionek, Natasha Azzopardi-Muscat, David Novillo-Ortiz, Cathal Morgan, Mafaten Chaouali","doi":"10.1007/s12553-025-00970-y","DOIUrl":"10.1007/s12553-025-00970-y","url":null,"abstract":"Purpose: This study aims to explore the application of Artificial intelligence (AI) systems in radiology departments and the role they play in the shortage of radiologists. It examines the ethical and legal considerations for uptake of AI both in relation to patient safety and for the profession of radiology.Methods: A systematised review was selected for this research study to collect maximum relevant evidence that provides a comprehensive overview of AI application in radiology specifically in terms of addressing radiologist shortages in hospitals. The search was complemented by grey literature to fill potential gaps.Results: Findings suggest that AI can read and interpret images more effectively and faster than radiologists and that it could be more widely used to reduce the impact of the global radiologist shortage, leading to better patient outcomes and safety. However, there are potential challenges predominantly ethical and legal. Concerns over complete radiologist replacement by AI do not currently seem likely, but rather the use of AI to complement radiologists in their work.Conclusions: AI cannot replace radiologists, instead radiology services will need the input of radiologists, AI systems and radiographers to provide a safe healthcare for all patients, therefore they are complementary. Radiologist jobs will most probably change to reduce repetitive tasks that can be conducted by AI. Radiologists and radiographers play a role in the provision of quality care in both normal day-to-day events and during times of disaster. Their role in diagnosing and prognosing diseases provides guidance during preparedness, response and recovery.","PeriodicalId":12941,"journal":{"name":"Health and Technology","volume":"15 3","pages":"489-501"},"PeriodicalIF":3.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12085355/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Health disparity in digital health technology design 数字医疗技术设计中的健康差距

IF 2.5

Health and Technology Pub Date : 2024-01-10 DOI: 10.1007/s12553-024-00814-1

Yanting Wu, Yawen Li, Andrius Baskys, J. Chok, Janice Hoffman, Don Roosan

引用次数: 0

COVID-19 vaccine prediction based on an interpretable CNN-LSTM model with three-stage feature engineering 基于三阶段特征工程的可解释 CNN-LSTM 模型的 COVID-19 疫苗预测

IF 2.5

Health and Technology Pub Date : 2024-01-09 DOI: 10.1007/s12553-023-00797-5

Lubna Altarawneh, Hao Wang, Yu Jin

引用次数: 0

Training protocol for driving power wheelchairs using virtual environment: preliminary results from a pilot study 利用虚拟环境驾驶电动轮椅的培训方案：试点研究的初步结果

IF 2.5

Health and Technology Pub Date : 2024-01-09 DOI: 10.1007/s12553-023-00811-w

Caroline Valentini, F. Martins, A. A. D. de Sá, E. Naves

引用次数: 0

Detection of Cardio Vascular abnormalities using gradient descent optimization and CNN 利用梯度下降优化和 CNN 检测心血管异常

IF 2.5

Health and Technology Pub Date : 2024-01-05 DOI: 10.1007/s12553-023-00807-6

Ninni Singh, Vinit Kumar Gunjan, F. Shaik, Sudipta Roy

引用次数: 0

Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets 用于疾病预测的无监督机器学习：使用多个数据集的性能比较分析

IF 2.5

Health and Technology Pub Date : 2023-12-29 DOI: 10.1007/s12553-023-00805-8

Haohui Lu, S. Uddin

引用次数: 0

Preliminary results of performance testing in diagnostic radiology facilities: Implementation of harmonized IAEA protocol for Africa 放射诊断设施性能测试的初步结果：在非洲实施国际原子能机构统一议定书

IF 2.5

Health and Technology Pub Date : 2023-12-28 DOI: 10.1007/s12553-023-00808-5

F. Hasford, Nadia Khelassi-Toutaoui, Ehab Attalla, Theophilus Sackey, Mohammed Talbi, Alsadeg Ahmed, Abir Darsalih, Ahmad Refaat Thabet, Peter Knoll, Virginia Tsapaki

引用次数: 0