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Title[Artificial intelligence to support telemedicine in Africa].
AuthorsGreis, C; Maul, LV; Hsu, C; Djamei, V; Schmid-Grendelmeier, P; Navarini, AA
JournalDer Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete
Publication Date1 Sep 2020
Date Added to PubMed8 Aug 2020
AbstractTelemedicine has been used in the daily routine of dermatologists for decades. The potential advantages are especially obvious in African countries having limited medical care, long geographical distances, and a meanwhile relatively well-developed telecommunication sector. National and international working groups support the establishment of teledermatological projects and in recent years have increasingly been using artificial intelligence (AI)-based technologies to support the local physicians. Ethnic variations represent a challenge in the development of automated algorithms. To further improve the accuracy of the systems and to be able to globalize, it is important to increase the amount of available clinical data. This can only be achieved with the active participation of local health care providers as well as the dermatological community and must always be in the interest of the individual patient.
Linkhttp://doi.org/10.1007/s00105-020-04664-6
TitleIntensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems.
AuthorsKindle, RD; Badawi, O; Celi, LA; Sturland, S
JournalCritical care clinics
Publication Date1 Jul 2019
Date Added to PubMed12 May 2019
AbstractThis article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in the tele-ICU, applications of machine learning (ML) algorithms to critical care, and opportunities to integrate ML with tele-ICU CDSS. The enormous quantities of data generated by tele-ICU systems is a major driver in the development of the large, comprehensive, heterogeneous, and granular data sets necessary to develop generalizable ML CDSS algorithms, and deidentification of these data sets expands opportunities for ML CDSS research.
Linkhttp://doi.org/10.1016/j.ccc.2019.02.005
TitleTrust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test.
AuthorsPowell, J
JournalJournal of medical Internet research
Publication Date28 Oct 2019
Date Added to PubMed30 Oct 2019
AbstractOver the next decade, one issue which will dominate sociotechnical studies in health informatics is the extent to which the promise of artificial intelligence in health care will be realized, along with the social and ethical issues which accompany it. A useful thought experiment is the application of the Turing test to user-facing artificial intelligence systems in health care (such as chatbots or conversational agents). In this paper I argue that many medical decisions require value judgements and the doctor-patient relationship requires empathy and understanding to arrive at a shared decision, often handling large areas of uncertainty and balancing competing risks. Arguably, medicine requires wisdom more than intelligence, artificial or otherwise. Artificial intelligence therefore needs to supplement rather than replace medical professionals, and identifying the complementary positioning of artificial intelligence in medical consultation is a key challenge for the future. In health care, artificial intelligence needs to pass the implementation game, not the imitation game.
Linkhttp://doi.org/10.2196/16222
TitleArtificial intelligence-assisted telemedicine platform for cataract screening and management: a potential model of care for global eye health.
AuthorsTing, DSJ; Ang, M; Mehta, JS; Ting, DSW
JournalThe British journal of ophthalmology
Publication Date1 Nov 2019
Date Added to PubMed5 Sep 2019
Abstract
Linkhttp://doi.org/10.1136/bjophthalmol-2019-315025
TitleApplication of mobile health, telemedicine and artificial intelligence to echocardiography.
AuthorsSeetharam, K; Kagiyama, N; Sengupta, PP
JournalEcho research and practice
Publication Date1 Jun 2019
Date Added to PubMed8 Mar 2019
AbstractThe intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.
Linkhttp://doi.org/10.1530/ERP-18-0081
TitleThe Real Era of the Art of Medicine Begins with Artificial Intelligence.
AuthorsMeskó, B
JournalJournal of medical Internet research
Publication Date18 Nov 2019
Date Added to PubMed19 Nov 2019
AbstractPhysicians have been performing the art of medicine for hundreds of years, and since the ancient era, patients have turned to physicians for help, advice, and cures. When the fathers of medicine started writing down their experience, knowledge, and observations, treating medical conditions became a structured process, with textbooks and professors sharing their methods over generations. After evidence-based medicine was established as the new form of medical science, the art and science of medicine had to be connected. As a result, by the end of the 20th century, health care had become highly dependent on technology. From electronic medical records, telemedicine, three-dimensional printing, algorithms, and sensors, technology has started to influence medical decisions and the lives of patients. While digital health technologies might be considered a threat to the art of medicine, I argue that advanced technologies, such as artificial intelligence, will initiate the real era of the art of medicine. Through the use of reinforcement learning, artificial intelligence could become the stethoscope of the 21st century. If we embrace these tools, the real art of medicine will begin now with the era of artificial intelligence.
Linkhttp://doi.org/10.2196/16295
TitleArtificial intelligence for diabetic retinopathy screening, prediction and management.
AuthorsGunasekeran, DV; Ting, DSW; Tan, GSW; Wong, TY
JournalCurrent opinion in ophthalmology
Publication Date1 Sep 2020
Date Added to PubMed3 Aug 2020
AbstractDiabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management.
Linkhttp://doi.org/10.1097/ICU.0000000000000693
TitlePreliminary Evaluation of a mHealth Coaching Conversational Artificial Intelligence for the Self-Care Management of People with Sickle-Cell Disease.
AuthorsIssom, DZ; Rochat, J; Hartvigsen, G; Lovis, C
JournalStudies in health technology and informatics
Publication Date16 Jun 2020
Date Added to PubMed24 Jun 2020
AbstractAdherence to the complex set of recommended self-care practices among people with Sickle-Cell Disease (SCD) positively impacts health outcomes. However, few patients possess the required skills (i.e. disease-specific knowledge, adequate levels of self-efficacy). Consequently, adherence rates remain low and only 1% of patients are empowered enough to master the self-care practices. Health coaching and therapeutic patient education have emerged as new approaches to enhance patients' self-management and support health behavior changes. This preliminary feasibility study examined patients' perceived usefulness of the information provided by a chatbot we developed following patient-important requirements collected during our preliminary studies. Participants tested the chatbot and completed a post-test survey. A total of 19 patients were enrolled and 2 withdrew. 15 respondents (15/17, 88%) gave a score of at least 3/4 to the question "The chatbot contains all the information I need". Results suggest that mHealth coaching apps could be used to promote the knowledge acquisition of recommended health behaviors related to the prevention of SCD main symptoms.
Linkhttp://doi.org/10.3233/SHTI200442
TitleConvolutional neural networks for wound detection: the role of artificial intelligence in wound care.
AuthorsOhura, N; Mitsuno, R; Sakisaka, M; Terabe, Y; Morishige, Y; Uchiyama, A; Okoshi, T; Shinji, I; Takushima, A
JournalJournal of wound care
Publication Date1 Oct 2019
Date Added to PubMed11 Oct 2019
AbstractTelemedicine is an essential support system for clinical settings outside the hospital. Recently, the importance of the model for assessment of telemedicine (MAST) has been emphasised. The development of an eHealth-supported wound assessment system using artificial intelligence is awaited. This study explored whether or not wound segmentation of a diabetic foot ulcer (DFU) and a venous leg ulcer (VLU) by a convolutional neural network (CNN) was possible after being educated using sacral pressure ulcer (PU) data sets, and which CNN architecture was superior at segmentation. CNNs with different algorithms and architectures were prepared. The four architectures were SegNet, LinkNet, U-Net and U-Net with the VGG16 Encoder Pre-Trained on ImageNet (Unet_VGG16). Each CNN learned the supervised data of sacral pressure ulcers (PUs). Among the four architectures, the best results were obtained with U-Net. U-Net demonstrated the second-highest accuracy in terms of the area under the curve (0.997) and a high specificity (0.943) and sensitivity (0.993), with the highest values obtained with Unet_VGG16. U-Net was also considered to be the most practical architecture and superior to the others in that the segmentation speed was faster than that of Unet_VGG16. The U-Net CNN constructed using appropriately supervised data was capable of segmentation with high accuracy. These findings suggest that eHealth wound assessment using CNNs will be of practical use in the future.
Linkhttp://doi.org/10.12968/jowc.2019.28.Sup10.S13
TitleCombining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol.
AuthorsBerrouiguet, S; Barrigón, ML; Castroman, JL; Courtet, P; Artés-Rodríguez, A; Baca-García, E
JournalBMC psychiatry
Publication Date7 Sep 2019
Date Added to PubMed9 Sep 2019
AbstractThe screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information's for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone's native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients' data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants' daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patient. NCT03720730. Retrospectively registered.
Linkhttp://doi.org/10.1186/s12888-019-2260-y
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