| Title | Anaphylaxis and digital medicine. |
| Authors | Anto, A; Sousa-Pinto, B; Bousquet, J |
| Journal | Current opinion in allergy and clinical immunology |
| Publication Date | 1 Oct 2021 |
| Date Added to PubMed | 23 Jul 2021 |
| Abstract | Digital medicine (mHealth) aims to help patients and healthcare providers (HCPs) improve and facilitate the provision of patient care. It encompasses equipment/connected medical devices, mHealth services and mHealth apps (apps). An updated review on digital health in anaphylaxis is proposed. In anaphylaxis, mHealth is used in electronic health records and registries.It will greatly benefit from the new International Classification of Diseases-11 rules and artificial intelligence. Telehealth has been revolutionised by the coronavirus disease 2019 pandemic, and lessons learnt should be extended to shared decision making in anaphylaxis. Very few nonvalidated apps exist and there is an urgent need to develop and validate such tools. Although digital health appears to be of great importance in anaphylaxis, it is still insufficiently used. |
| Link | http://doi.org/10.1097/ACI.0000000000000764 |
| Title | E-health and multiple sclerosis. |
| Authors | Matthews, PM; Block, VJ; Leocani, L |
| Journal | Current opinion in neurology |
| Publication Date | 1 Jun 2020 |
| Date Added to PubMed | 24 Apr 2020 |
| Abstract | To outline recent applications of e-health data and digital tools for improving the care and management of healthcare for people with multiple sclerosis. The digitization of most clinical data, along with developments in communication technologies, miniaturization of sensors and computational advances are enabling aggregation and clinically meaningful analyses of real-world data from patient registries, digital patient-reported outcomes and electronic health records (EHR). These data are allowing more confident descriptions of prognoses for multiple sclerosis patients and the long-term relative benefits and safety of disease-modifying treatments (DMT). Registries allow detailed, multiple sclerosis-specific data to be shared between clinicians more easily, provide data needed to improve the impact of DMT and, with EHR, characterize clinically relevant interactions between multiple sclerosis and other diseases. Wearable sensors provide continuous, long-term measures of performance dynamics in relevant ecological settings. In conjunction with telemedicine and online apps, they promise a major expansion of the scope for patients to manage aspects of their own care. Advances in disease understanding, decision support and self-management using these Big Data are being accelerated by machine learning and artificial intelligence. Both health professionals and patients can employ e-health approaches and tools for development of a more patient-centred learning health system. |
| Link | http://doi.org/10.1097/WCO.0000000000000823 |
| Title | Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different? |
| Authors | Vervoort, D; Tam, DY; Wijeysundera, HC |
| Journal | The Canadian journal of cardiology |
| Publication Date | 1 Feb 2022 |
| Date Added to PubMed | 31 Aug 2021 |
| Abstract | Innovations in health care are growing exponentially, resulting in improved quality of and access to care, as well as rising societal costs of care and variable reimbursement. In recent years, digital health technologies and artificial intelligence have become of increasing interest in cardiovascular medicine owing to their unique ability to empower patients and to use increasing quantities of data for moving toward personalised and precision medicine. Health technology assessment agencies evaluate the money spent on a health care intervention or technology to attain a given clinical impact and make recommendations for reimbursement considerations. However, there is a scarcity of economic evaluations of cardiovascular digital health technologies and artificial intelligence. The current health technology assessment framework is not equipped to address the unique, dynamic, and unpredictable value considerations of these technologies and highlight the need to better approach the digital health technologies and artificial intelligence health technology assessment process. In this review, we compare digital health technologies and artificial intelligence with traditional health care technologies, review existing health technology assessment frameworks, and discuss challenges and opportunities related to cardiovascular digital health technologies and artificial intelligence health technology assessment. Specifically, we argue that health technology assessments for digital health technologies and artificial intelligence applications must allow for a much shorter device life cycle, given the rapid and even potentially continuously iterative nature of this technology, and thus an evidence base that maybe less mature, compared with traditional health technologies and interventions. |
| Link | http://doi.org/10.1016/j.cjca.2021.08.015 |
| Title | Advances and opportunities in the new digital era of telemedicine, e-health, artificial intelligence, and beyond. |
| Authors | Wang, HHX; Li, YT; Huang, J; Huang, W; Wong, MCS |
| Journal | Hong Kong medical journal = Xianggang yi xue za zhi |
| Publication Date | 1 Oct 2023 |
| Date Added to PubMed | 26 Oct 2023 |
| Abstract | |
| Link | http://doi.org/10.12809/hkmj235152 |
| Title | Emerging Artificial Intelligence-Empowered mHealth: Scoping Review. |
| Authors | Bhatt, P; Liu, J; Gong, Y; Wang, J; Guo, Y |
| Journal | JMIR mHealth and uHealth |
| Publication Date | 9 Jun 2022 |
| Date Added to PubMed | 10 Jun 2022 |
| Abstract | Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years. Using Arksey and O'Malley's 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as "mobile healthcare," "wearable medical sensors," "smartphones", and "AI." We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain. We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research. The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry. |
| Link | http://doi.org/10.2196/35053 |
| Title | Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review. |
| Authors | Chen, Z; Hao, J; Sun, H; Li, M; Zhang, Y; Qian, Q |
| Journal | BMC medical informatics and decision making |
| Publication Date | 13 Feb 2025 |
| Date Added to PubMed | 14 Feb 2025 |
| Abstract | Chronic Obstructive Pulmonary Disease (COPD) represents a significant global health challenge, placing considerable burdens on healthcare systems. The rise of digital health technologies (DHTs) and artificial intelligence (AI) algorithms offers new opportunities to improve COPD predictive capabilities, diagnostic accuracy, and patient management. This systematic review explores the types of data in COPD under DHTs, the AI algorithms employed for data analysis, and identifies key application areas reported in the literature. A systematic search was conducted in PubMed and Web of Science for studies published up to December 2024 that applied AI algorithms in digital health for COPD management. Inclusion criteria focused on original research utilizing AI algorithms and digital health technologies for COPD, while review articles were excluded. Two independent reviewers screened the studies, resolving discrepancies through consensus. From an initial pool of 265 studies, 41 met the inclusion criteria. Analysis of these studies highlighted a diverse range of data types and modalities collected from DHTs in the COPD context, including clinical data, patient-reported outcomes, and environmental/lifestyle data. Machine learning (ML) algorithms were employed in 34 studies, and deep learning (DL) algorithms in 16. Support vector machines and boosting were the most frequently used ML models, while deep neural networks (DNN) and convolutional neural networks (CNN) were the most commonly used DL models. The review identified three key application domains for AI in COPD: screening and diagnosis (10 studies), exacerbation prediction (22 studies), and patient monitoring (9 studies). Disease progression prediction was a prevalent focus across three domains, with promising accuracy and performance metrics reported. Digital health technologies and AI algorithms have a wide range of applications and promise for COPD management. ML models, in particularly, show great potential in improving digital health solutions for COPD. Future research should focus on enhancing global collaboration to explore the cost-effectiveness and data-sharing capabilities of DHTs, enhancing the interpretability of AI models, and validating these algorithms through clinical trials to facilitate their safe integration into the routine COPD management. |
| Link | http://doi.org/10.1186/s12911-025-02870-7 |
| Title | [Internal Medicine 3.0]. |
| Authors | Galland, J |
| Journal | La Revue de medecine interne |
| Publication Date | 1 Mar 2020 |
| Date Added to PubMed | 23 Oct 2019 |
| Abstract | |
| Link | http://doi.org/10.1016/j.revmed.2019.09.007 |
| Title | Digital health in musculoskeletal care: where are we heading? |
| Authors | Gupta, L; Najm, A; Kabir, K; De Cock, D |
| Journal | BMC musculoskeletal disorders |
| Publication Date | 14 Mar 2023 |
| Date Added to PubMed | 16 Mar 2023 |
| Abstract | BMC Musculoskeletal Disorders launched a Collection on digital health to get a sense of where the wind is blowing, and what impact these technologies are and will have on musculoskeletal medicine. This editorial summarizes findings and focuses on some key topics, which are valuable as digital health establishes itself in patient care. Elements discussed are digital tools for the diagnosis, prognosis and evaluation of rheumatic and musculoskeletal diseases, coupled together with advances in methodologies to analyse health records and imaging. Moreover, the acceptability and validity of these digital advances is discussed. In sum, this editorial and the papers presented in this article collection on Digital health in musculoskeletal care will give the interested reader both a glance towards which future we are heading, and which new challenges these advances bring. |
| Link | http://doi.org/10.1186/s12891-023-06309-w |
| Title | Digital health and primary care: Past, pandemic and prospects. |
| Authors | Pagliari, C |
| Journal | Journal of global health |
| Publication Date | 2 Jul 2021 |
| Date Added to PubMed | 6 Jul 2021 |
| Abstract | This article reflects on the breadth of digital developments seen in primary care over time, as well as the rapid and significant changes prompted by the COVID-19 crisis. Recent research and experience have shone further light on factors influencing the implementation and usefulness of these approaches, as well as unresolved challenges and unintended consequences. These are considered in relation to not only digital technology and infrastructure, but also wider aspects of health systems, the nature of primary care work and culture, patient characteristics and inequalities, and ethical issues around data privacy, inclusion, empowerment, empathy and trust. Implications for the future direction and sustainability of these approaches are discussed, taking account of novel paradigms, such as artificial intelligence, and the growing capture of primary care data for secondary uses. Decision makers are encouraged to think holistically about where value is most likely to be added, or risks being taken away, when judging which innovations to carry forward. It concludes that, while responding to this public health emergency has created something of a digital 'big bang' for primary care, an incremental, adaptive, patient-centered strategy, focused on augmenting rather than replacing existing services, is likely to prove most fruitful in the longer term. |
| Link | http://doi.org/10.7189/jogh.11.01005 |