| Title | Designing Futuristic Telemedicine Using Artificial Intelligence and Robotics in the COVID-19 Era. |
| Authors | Bhaskar, S; Bradley, S; Sakhamuri, S; Moguilner, S; Chattu, VK; Pandya, S; Schroeder, S; Ray, D; Banach, M |
| Journal | Frontiers in public health |
| Publication Date | 1 Dec 2020 |
| Date Added to PubMed | 24 Nov 2020 |
| Abstract | Technological innovations such as artificial intelligence and robotics may be of potential use in telemedicine and in building capacity to respond to future pandemics beyond the current COVID-19 era. Our international consortium of interdisciplinary experts in clinical medicine, health policy, and telemedicine have identified gaps in uptake and implementation of telemedicine or telehealth across geographics and medical specialties. This paper discusses various artificial intelligence and robotics-assisted telemedicine or telehealth applications during COVID-19 and presents an alternative artificial intelligence assisted telemedicine framework to accelerate the rapid deployment of telemedicine and improve access to quality and cost-effective healthcare. We postulate that the artificial intelligence assisted telemedicine framework would be indispensable in creating futuristic and resilient health systems that can support communities amidst pandemics. |
| Link | http://doi.org/10.3389/fpubh.2020.556789 |
| Title | Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: Much more than just telemedicine. |
| Authors | Albert, L; Capel, I; García-Sáez, G; Martín-Redondo, P; Hernando, ME; Rigla, M |
| Journal | Diabetes research and clinical practice |
| Publication Date | 1 Nov 2020 |
| Date Added to PubMed | 6 Sep 2020 |
| Abstract | We describe our experience in the remote management of women with gestational diabetes mellitus during the COVID-19 pandemic. We used a mobile phone application with artificial intelligence that automatically classifies and analyses the data (ketonuria, diet transgressions, and blood glucose values), making adjustment recommendations regarding the diet or insulin treatment. |
| Link | http://doi.org/10.1016/j.diabres.2020.108396 |
| Title | Cochlear implant telemedicine: Remote fitting based on psychoacoustic self-tests and artificial intelligence. |
| Authors | Meeuws, M; Pascoal, D; Janssens de Varebeke, S; De Ceulaer, G; Govaerts, PJ |
| Journal | Cochlear implants international |
| Publication Date | 1 Sep 2020 |
| Date Added to PubMed | 14 May 2020 |
| Abstract | Objective: This study aims to assess the feasibility of autonomous cochlear implant (CI) fitting by adult CI recipients based on psychoacoustic self-testing and artificial intelligence (AI). Design: A feasibility study was performed on six adult CI recipients implanted with a Nucleus device. Two weeks after processor activation in the clinic, a 'self-fitting' session was organized in a supervised simulated home environment. The CI recipient performed pure tone audiometry and spectral discrimination tests as self-tests. The AI application FOX analysed the results and recommended a new map. The participants filled out a questionnaire and were tested again after 2 months of take-home experience. Results: Four out of six patients performed the self-tests without any help from the audiologist and four were fitted by FOX without any manual intervention. All patients were comfortable with the concept of self-testing and automated fitting. Patients acknowledged that at this stage the remote supervision of an audiologist remains essential. Conclusions: The study showed that audiological self-assessment and remote CI fitting with AI under the supervision of an audiologist is feasible, at least in a number of CI recipients. Currently, there are still some technical and regulatory challenges to be addressed before this can become routine practice. |
| Link | http://doi.org/10.1080/14670100.2020.1757840 |
| Title | [Artificial intelligence to support telemedicine in Africa]. |
| Authors | Greis, C; Maul, LV; Hsu, C; Djamei, V; Schmid-Grendelmeier, P; Navarini, AA |
| Journal | Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete |
| Publication Date | 1 Sep 2020 |
| Date Added to PubMed | 8 Aug 2020 |
| Abstract | Telemedicine 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. |
| Link | http://doi.org/10.1007/s00105-020-04664-6 |
| Title | Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems. |
| Authors | Kindle, RD; Badawi, O; Celi, LA; Sturland, S |
| Journal | Critical care clinics |
| Publication Date | 1 Jul 2019 |
| Date Added to PubMed | 12 May 2019 |
| Abstract | This 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. |
| Link | http://doi.org/10.1016/j.ccc.2019.02.005 |
| Title | Trust Me, I'm a Chatbot: How Artificial Intelligence in Health Care Fails the Turing Test. |
| Authors | Powell, J |
| Journal | Journal of medical Internet research |
| Publication Date | 28 Oct 2019 |
| Date Added to PubMed | 30 Oct 2019 |
| Abstract | Over 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. |
| Link | http://doi.org/10.2196/16222 |
| Title | Artificial intelligence-assisted telemedicine platform for cataract screening and management: a potential model of care for global eye health. |
| Authors | Ting, DSJ; Ang, M; Mehta, JS; Ting, DSW |
| Journal | The British journal of ophthalmology |
| Publication Date | 1 Nov 2019 |
| Date Added to PubMed | 5 Sep 2019 |
| Abstract | |
| Link | http://doi.org/10.1136/bjophthalmol-2019-315025 |
| Title | Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurity. |
| Authors | Greenwald, MF; Danford, ID; Shahrawat, M; Ostmo, S; Brown, J; Kalpathy-Cramer, J; Bradshaw, K; Schelonka, R; Cohen, HS; Chan, RVP; Chiang, MF; Campbell, JP |
| Journal | Journal of AAPOS : the official publication of the American Association for Pediatric Ophthalmology and Strabismus |
| Publication Date | 1 Jun 2020 |
| Date Added to PubMed | 15 Apr 2020 |
| Abstract | Retrospective evaluation of a deep learning-derived retinopathy of prematurity (ROP) vascular severity score in an operational ROP screening program demonstrated high diagnostic performance for detection of type 2 or worse ROP. To our knowledge, this is the first report in the literature that evaluated the use of artificial intelligence for ROP screening and represents a proof of concept. With further prospective validation, this technology might improve the accuracy, efficiency, and objectivity of diagnosis and facilitate earlier detection of disease progression in patients with potentially blinding ROP. |
| Link | http://doi.org/10.1016/j.jaapos.2020.01.014 |
| Title | Application of mobile health, telemedicine and artificial intelligence to echocardiography. |
| Authors | Seetharam, K; Kagiyama, N; Sengupta, PP |
| Journal | Echo research and practice |
| Publication Date | 1 Jun 2019 |
| Date Added to PubMed | 8 Mar 2019 |
| Abstract | The 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. |
| Link | http://doi.org/10.1530/ERP-18-0081 |
| Title | Telemedicine, Telestroke, and Artificial Intelligence Can Be Coded with the International Classification of Health Interventions. |
| Authors | Ohannessian, R; Fortune, N; Moulin, T; Madden, R |
| Journal | Telemedicine journal and e-health : the official journal of the American Telemedicine Association |
| Publication Date | 1 May 2020 |
| Date Added to PubMed | 18 Jul 2019 |
| Abstract | |
| Link | http://doi.org/10.1089/tmj.2019.0072 |