| Title | Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective. |
| Authors | Li, JO; Liu, H; Ting, DSJ; Jeon, S; Chan, RVP; Kim, JE; Sim, DA; Thomas, PBM; Lin, H; Chen, Y; Sakomoto, T; Loewenstein, A; Lam, DSC; Pasquale, LR; Wong, TY; Lam, LA; Ting, DSW |
| Journal | Progress in retinal and eye research |
| Publication Date | 1 May 2021 |
| Date Added to PubMed | 9 Sep 2020 |
| Abstract | The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions. |
| Link | http://doi.org/10.1016/j.preteyeres.2020.100900 |
| Title | Artificial intelligence and telemedicine in anesthesia: potential and problems. |
| Authors | Bellini, V; Valente, M; Gaddi, AV; Pelosi, P; Bignami, E |
| Journal | Minerva anestesiologica |
| Publication Date | 1 Sep 2022 |
| Date Added to PubMed | 16 Feb 2022 |
| Abstract | The application of novel technologies like artificial intelligence (AI), machine learning (ML) and telemedicine in anesthesiology could play a role in transforming the future of health care. In the present review we discuss the current applications of AI and telemedicine in anesthesiology and perioperative care, exploring their potential influence and the possible hurdles. AI technologies have the potential to deeply impact all phases of perioperative care from accurate risk prediction to operating room organization, leading to increased cost-effective care quality and better outcomes. Telemedicine is reported as a successful mean within the anesthetic pathway, including preoperative evaluation, remote patient monitoring, and postoperative care. The utilization of AI and telemedicine is promising encouraging results in perioperative management, nevertheless several hurdles remain to be overcome before these tools could be integrated in our daily practice. AI models and telemedicine can significantly influence all phases of perioperative care, helping physicians in the development of precision medicine. |
| Link | http://doi.org/10.23736/S0375-9393.21.16241-8 |
| Title | Artificial Intelligence in Dermatology: A Primer. |
| Authors | Young, AT; Xiong, M; Pfau, J; Keiser, MJ; Wei, ML |
| Journal | The Journal of investigative dermatology |
| Publication Date | 1 Aug 2020 |
| Date Added to PubMed | 2 Apr 2020 |
| Abstract | Artificial intelligence is becoming increasingly important in dermatology, with studies reporting accuracy matching or exceeding dermatologists for the diagnosis of skin lesions from clinical and dermoscopic images. However, real-world clinical validation is currently lacking. We review dermatological applications of deep learning, the leading artificial intelligence technology for image analysis, and discuss its current capabilities, potential failure modes, and challenges surrounding performance assessment and interpretability. We address the following three primary applications: (i) teledermatology, including triage for referral to dermatologists; (ii) augmenting clinical assessment during face-to-face visits; and (iii) dermatopathology. We discuss equity and ethical issues related to future clinical adoption and recommend specific standardization of metrics for reporting model performance. |
| Link | http://doi.org/10.1016/j.jid.2020.02.026 |
| Title | Best practices in digital health literacy. |
| Authors | Conard, S |
| Journal | International journal of cardiology |
| Publication Date | 1 Oct 2019 |
| Date Added to PubMed | 25 Jun 2019 |
| Abstract | The connection between health literacy and health outcomes includes access and utilization of healthcare services, patient/provider interaction and self-care. Digital approaches can be designed to simplify or expand on a concept, test for understanding, and do not have a time constraint. New technologies, such as artificial intelligence and machine learning, virtual and augmented reality, and blockchain can move the role of technology beyond data collection to a more integrated system. Rather than being a passive participant, digital solutions provide the opportunity for the individual to be an active participant in their health. These solutions can be delivered in a way that builds or enhances the individual's belief that the plan will be successful and more confidence that they can stick with it. Digital solutions allow for the delivery of multi-media education, such as videos, voice, and print, at different reading levels, in multiple languages, using formal and informal teaching methods. By giving the patient a greater voice and empowering them to be active participants in their care, they can develop their decision making and shared decision making skills. The first step in our health literacy instructional model is to address the emotional state of the person. Once the emotional state has been addressed, and an engagement strategy has been deployed the final phase is the delivery of an educational solution. While a clear definition of health literacy and an instructional model are important, further research must be done to continually determine more effective ways to incorporate health technology in the process of improving health outcomes. |
| Link | http://doi.org/10.1016/j.ijcard.2019.05.070 |
| Title | Innovation and challenges of artificial intelligence technology in personalized healthcare. |
| Authors | Li, YH; Li, YL; Wei, MY; Li, GY |
| Journal | Scientific reports |
| Publication Date | 16 Aug 2024 |
| Date Added to PubMed | 17 Aug 2024 |
| Abstract | As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient. In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace. By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both ethically unimpeachable and conducive to elevating the global health quotient. |
| Link | http://doi.org/10.1038/s41598-024-70073-7 |
| Title | What is Digital Health? Review of Definitions. |
| Authors | Fatehi, F; Samadbeik, M; Kazemi, A |
| Journal | Studies in health technology and informatics |
| Publication Date | 23 Nov 2020 |
| Date Added to PubMed | 24 Nov 2020 |
| Abstract | Digital technologies are transforming the health sector all over the world, however various aspects of this emerging field of science is yet to be properly understood. Ambiguity in the definition of digital health is a hurdle for research, policy, and practice in this field. With the aim of achieving a consensus in the definition of digital health, we undertook a quantitative analysis and term mapping of the published definitions of digital health. After inspecting 1527 records, we analyzed 95 unique definitions of digital health, from both scholar and general sources. The findings showed that digital health, as has been used in the literature, is more concerned about the provision of healthcare rather than the use of technology. Wellbeing of people, both at population and individual levels, have been more emphasized than the care of patients suffering from diseases. Also, the use of data and information for the care of patients was highlighted. A dominant concept in digital health appeared to be mobile health (mHealth), which is related to other concepts such as telehealth, eHealth, and artificial intelligence in healthcare. |
| Link | http://doi.org/10.3233/SHTI200696 |
| Title | On Chatbots and Generative Artificial Intelligence. |
| Authors | Oermann, EK; Kondziolka, D |
| Journal | Neurosurgery |
| Publication Date | 1 Apr 2023 |
| Date Added to PubMed | 14 Feb 2023 |
| Abstract | |
| Link | http://doi.org/10.1227/neu.0000000000002415 |
| 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 | Digital health-high tech or high touch? |
| Authors | Gangl, C; Krychtiuk, K |
| Journal | Wiener medizinische Wochenschrift (1946) |
| Publication Date | 1 Apr 2023 |
| Date Added to PubMed | 6 Jan 2023 |
| Abstract | Digital transformation in medicine refers to the implementation of information technology-driven developments in the healthcare system and their impact on the way we teach, share, and practice medicine. We would like to provide an overview of current developments and opportunities but also of the risks of digital transformation in medicine. Therefore, we examine the possibilities wearables and digital biomarkers provide for early detection and monitoring of diseases and discuss the potential of artificial intelligence applications in medicine. Furthermore, we outline new opportunities offered by telemedicine applications and digital therapeutics, discuss the aspects of social media in healthcare, and provide an outlook on "Health 4.0." |
| Link | http://doi.org/10.1007/s10354-022-00991-6 |