Artificial intelligence, the internet of things, and virtual clinics: ophthalmology at the digital translation forefront

5G-enabled smart hospitals: Innovations in patient care and facility management

Smart hospitals represent the pinnacle of healthcare innovation, leveraging cutting-edge technologies to transform patient care and facility management. This article addresses the pressing need for effective implementation of 5G technology in smart hospitals, aiming to enhance connectivity, improve patient outcomes, and drive operational efficiency. The methodology employed involves a comprehensive review of existing literature, case studies, and expert insights to analyze the impact of 5G on various aspects of smart hospital operations. The article highlights the significance of 5G technology in enabling real-time data analytics, remote monitoring, and telemedicine, thus revolutionizing healthcare delivery. By providing high-speed, low-latency connectivity, 5G facilitates seamless communication and collaboration among healthcare providers, leading to more efficient diagnosis, treatment, and patient care. Additionally, the adoption of 5G enables smart hospitals to leverage artificial intelligence (AI)-based solutions for predictive analytics, personalized medicine and enhanced patient engagement. Furthermore, the article explores the potential of 5G-enabled smart hospitals in enhancing disaster preparedness and emergency response efforts. Case studies and examples demonstrate how 5G technology can improve situational awareness, coordinate resources, and deliver timely care during natural disasters and pandemics. Overall, this article underscores the transformative impact of 5G technology on smart hospitals and emphasizes the importance of embracing innovation to meet the evolving needs of patients and communities. By adopting 5G technology, smart hospitals can usher in a new era of healthcare delivery characterized by enhanced connectivity, improved patient outcomes, and unparalleled efficiency.

Investigating awareness of artificial intelligence in healthcare among medical students and professionals in Pakistan: a cross-sectional study

Objective

The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan's medical community so as to comment on its future in our healthcare system.

Methods

A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ2 test was used to analyze the subgroups (Significant p value <0.05).

Results

A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan's healthcare system.

Conclusion

This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals' perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.

Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.

Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs

Artificial intelligence (AI) has great potential in ophthalmology. We investigated how ambiguous outputs from an AI diagnostic support system (AI-DSS) affected diagnostic responses from optometrists when assessing cases of suspected retinal disease. Thirty optometrists (15 more experienced, 15 less) assessed 30 clinical cases. For ten, participants saw an optical coherence tomography (OCT) scan, basic clinical information and retinal photography ('no AI'). For another ten, they were also given AI-generated OCT-based probabilistic diagnoses ('AI diagnosis'); and for ten, both AI-diagnosis and AI-generated OCT segmentations ('AI diagnosis + segmentation') were provided. Cases were matched across the three types of presentation and were selected to include 40% ambiguous and 20% incorrect AI outputs. Optometrist diagnostic agreement with the predefined reference standard was lowest for 'AI diagnosis + segmentation' (204/300, 68%) compared to 'AI diagnosis' (224/300, 75% p = 0.010), and 'no Al' (242/300, 81%, p =  < 0.001). Agreement with AI diagnosis consistent with the reference standard decreased (174/210 vs 199/210, p = 0.003), but participants trusted the AI more (p = 0.029) with segmentations. Practitioner experience did not affect diagnostic responses (p = 0.24). More experienced participants were more confident (p = 0.012) and trusted the AI less (p = 0.038). Our findings also highlight issues around reference standard definition.

Teleophthalmology and retina: a review of current tools, pathways and services

Telemedicine, the use of telecommunication and information technology to deliver healthcare remotely, has evolved beyond recognition since its inception in the 1970s. Advances in telecommunication infrastructure, the advent of the Internet, exponential growth in computing power and associated computer-aided diagnosis, and medical imaging developments have created an environment where telemedicine is more accessible and capable than ever before, particularly in the field of ophthalmology. Ever-increasing global demand for ophthalmic services due to population growth and ageing together with insufficient supply of ophthalmologists requires new models of healthcare provision integrating telemedicine to meet present day challenges, with the recent COVID-19 pandemic providing the catalyst for the widespread adoption and acceptance of teleophthalmology. In this review we discuss the history, present and future application of telemedicine within the field of ophthalmology, and specifically retinal disease. We consider the strengths and limitations of teleophthalmology, its role in screening, community and hospital management of retinal disease, patient and clinician attitudes, and barriers to its adoption.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

User-acceptability of an automated telephone call for post-operative follow-up after uncomplicated cataract surgery

BACKGROUND

Innovative technology is recommended to address the current capacity challenges facing the NHS. This study evaluates the patient acceptability of automated telephone follow-up after routine cataract surgery using Dora (Ufonia Limited, Oxford, United Kingdom), which to our knowledge is the first AI-powered clinical assistant to be used in the NHS. Dora has a natural-language, phone conversation with patients about their symptoms after cataract surgery.

METHODS

This is a prospective mixed-methods cohort study that was conducted at Buckinghamshire Healthcare NHS Foundation Trust. All patients who were followed up using Dora were asked to give a Net Promoter Score (NPS), and 24 patients were randomly selected to complete the validated Telephone Usability Questionnaire (TUQ) as well as extended semi-structured interviews that underwent thematic analysis.

RESULTS

A total of 170 autonomous calls were completed. The median NPS score was 9 out of 10. The TUQ (scored out of 5) showed high rates of acceptability, with an overall mean score of 4.0. Simplicity, time saving, and ease of use scored the highest with a median of 5, whilst 'speaking to Dora feels the same as speaking to a clinician' scored a median of 3. The main themes extracted from the qualitative data were 'I can see why you're doing it', 'It went quite well actually', 'I just trust human beings I suppose'.

CONCLUSION

We found high levels of patient acceptability when using Dora across three acceptability measures. Dora provides a potential solution to reduce pressure on hospital capacity whilst also providing a convenient service for patients.

Predicting Acute and Post-Recovery Outcomes in Cerebral Malaria and Other Comas by Optical Coherence Tomography (OCT in CM) - A protocol for an observational cohort study of Malawian children

Cerebral malaria (CM) remains a significant global health challenge with high morbidity and mortality. Malarial retinopathy has been shown to be diagnostically and prognostically significant in the assessment of CM. The major mechanism of death in paediatric CM is brain swelling. Long term morbidity is typically characterised by neurological and neurodevelopmental sequelae. Optical coherence tomography can be used to quantify papilloedema and macular ischaemia, identified as hyperreflectivity. Here we describe a protocol to test the hypotheses that quantification of optic nerve head swelling using optical coherence tomography can identify severe brain swelling in CM, and that quantification of hyperreflectivity in the macula predicts neurodevelopmental outcomes post-recovery. Additionally, our protocol includes the development of a novel, low-cost, handheld optical coherence tomography machine and artificial intelligence tools to assist in image analysis.

The Use of Telemedicine in Medical Education and Patient Care

The COVID-19 pandemic has accelerated and expanded the adoption of telemedicine globally. This allowed telemedicine to engage medical students in patient care and ensured continuity of care for vulnerable patients. In this review, the history of telemedicine and some of its applications in medical education were reviewed. Furthermore, we also shed light on how to incorporate telemedicine into several curricula and the strategies used to include it. The article also explored how to evaluate telemedicine and the major facilitators and barriers any medical and educational institution must address when using telemedicine. At the end of the review, we explored the future promises telemedicine has for medical education.

Challenges and Prospective of AI and 5G-Enabled Technologies in Emerging Applications during the Pandemic

5G is being implemented in the Internet of things (IoT) era. This book chapter focuses on 5G technology and the integration of other digital technologies, such as artificial intelligence (AI) and machine learning, IoT, big data analytics, cloud computing, robotics, and other digital platforms into new healthcare applications. Now, the healthcare industry is implementing 5G-enabled technology to improve health services, medical research, quality of life, and medical professionals’ and patients’ experiences everywhere, at any time. Technology can facilitate faster medical research progress and better clinical and social services management. Furthermore, AI approaches with 5G connectivity may be able to combat the epidemic challenges with minimal resources. This book chapter underlines how 5G technology is growing to address epidemic concerns. The study highlights many technical issues and future developments for creating 5G-powered healthcare solutions. This chapter also addresses the key challenges AI and 5G technology face in emerging healthcare solutions. In addition, this book chapter highlights perspective, policy recommendations, and future research directions of AI and 5G-enabled technologies in confronting future pandemics. More research will be incorporated into future projects, including studies on developing a digital society based on 5G technology in healthcare emergencies.

Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner

The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods.

5G in Healthcare: from COVID-19 to Future Challenges

Worldwide up to May 2022 there have been 515 million cases of COVID-19 infection and over 6 million deaths. The World Health Organization estimated that 115,000 healthcare workers died from COVID-19 from January 2020 to May 2021. This toll on human lives prompted this review on 5G based networking primarily on major components of healthcare delivery: diagnosis, patient monitoring, contact tracing, diagnostic imaging tests, vaccines distribution, emergency medical services, telesurgery and robot-assisted tele-ultrasound. The positive impact of 5G as core technology for COVID-19 applications enabled exchange of huge data sets in fangcang (cabin) hospitals and real-time contact tracing, while the low latency enhanced robot-assisted tele-ultrasound, and telementoring during ophthalmic surgery. In other instances, 5G provided a supportive technology for applications related to COVID-19, e.g., patient monitoring. The feasibility of 5G telesurgery was proven, albeit by a few studies on real patients, in very low samples size in most instances. The important future applications of 5G in healthcare include surveillance of elderly people, the immunosuppressed, and nano- oncology for Internet of Nano Things (IoNT). Issues remain and these require resolution before routine clinical adoption. These include infrastructure and coverage; health risks; security and privacy protection of patients' data; 5G implementation with artificial intelligence, blockchain, and IoT; validation, patient acceptance and training of end-users on these technologies.

How to minimize the impact of COVID-19 on laparoendoscopic single-site surgery training?

Background

Because of special technical challenges, laparoendoscopic single‐site surgery (LESS) has been introduced into surgical practice, with surgeons required to have adequate training. The COVID‐19 pandemic has significantly affected every aspect of healthcare systems, including LESS training, which must be modified to minimize the impact of the COVID‐19 pandemic.

Methods

A 3‐session training programme was designed in 2020 during the epidemic, which was modified in 2019 before the pandemic. Session 1 was an online study on LESS knowledge. Session 2 involved the trainees' self‐directed simulator‐training. Task performance was evaluated using the fundamentals of laparoscopic surgery (FLS) scoring. Session 3 was practical training, including trainers' live surgical video demonstrations and trainees' surgical video feedback after training. Video feedback performance was evaluated using the modified global rating scale (GRS). Furthermore, trainees completed a general self‐efficacy (GSE) instrument. Forty‐two gynaecology trainees were allocated into two groups: novices (n = 32) and experts (n = 10).

Results

Compared with pre‐training, FLS scores improved in peg transfer (P < 0.001 and P = 0.01) and pattern cutting (P = 0.02 and P < 0.001) for novices and experts, respectively. Participants (81% versus 67%) provided first and second video feedback, respectively. Compared to the first feedback, the GRS scores of both groups improved significantly in the second feedback. All trainees showed an increase in GSE after training (P < 0.001).

Conclusion

The modified LESS training programme is a practical and effective option that allows trainees to continue training during the epidemic.

The Impact of the COVID-19 Pandemic on Ophthalmology Resident Perceptions of Clinical Experience, Surgical Training, and Personal Life

Objective The coronavirus disease 2019 (COVID-19) pandemic has affected multiple areas of health care, including residency training programs. Elucidating the effect of the COVID-19 pandemic on resident clinical experience, surgical training, and wellness is essential to identify areas in which programs can provide additional educational and personal resources to trainees. This study aims to evaluate the experiences of ophthalmology residents during the pandemic.

Design The design is a cross-sectional, nonvalidated survey study. The survey was administered online with data collection from August 22 to August 31, 2020.

Participants Applicants to the Bascom Palmer Eye Institute ophthalmology residency program during the 2016 to 2019 application cycles were invited to complete the survey to encompass trainees currently in ophthalmology residency during the COVID-19 pandemic. Applicants who were not training at an ophthalmology residency program during the pandemic were excluded.

Methods This study involved eliciting feedback from ophthalmology residents on the perceived impact of COVID-19 on their residency experiences through survey questions.

Main Outcome Measures Perceived didactic, clinical, surgical, and overall experiences of residents during the COVID-19 pandemic, effect on cataract and noncataract surgical case volume, and perceived effects on resident personal life.

Results Responses were obtained from 357 (22.8%) individuals, 193 of which met inclusion criteria (59.1% male, 54.9% aged 30–34). Most participants reported overall worsening of their ophthalmology training experience due to COVID-19 (75.1%), with worsening of clinical training reported by 72.5% of participants and worsening of surgical training reported by 89.1% of participants. There were no significant differences in the perception of the impact of COVID-19 on overall training experience, clinical training experience, or surgical training experience among geographic regions (p = 0.43, p = 0.23, p = 0.27, respectively). A higher percentage of post-graduate year 3 (PGY3) and PGY4 trainees reported worsened clinical (p = 0.003) or surgical (p = 0.03) experience compared with PGY2 trainees. Participants also reported impact on personal life including time spent away from family (52.9%), worsened friendships with co-residents (29.5%), forced changes in living situation (15.0%), and increased expenses (13.5%).

Conclusion The COVID-19 pandemic has substantially impacted clinical and surgical experience of ophthalmology residents, who also report personal stressors due to the pandemic. Identifying alterations in the ophthalmology residency experience is essential to provide additional resources to support ophthalmology trainees professionally and personally during this time.

NextGen Public Health Surveillance and the Internet of Things (IoT)

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Blockchain applications in health care for COVID-19 and beyond: a systematic review

The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records and access rights, and mobile health). We conducted a systematic review of COVID-19-related and non-COVID-19-related applications of blockchain in health care. We identified relevant reports published in MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar up to July 29, 2021. Articles that included both clinical and technical designs, with or without prototype development, were included. A total of 85 375 articles were evaluated, with 415 full length reports (37 related to COVID-19 and 378 not related to COVID-19) eventually included in the final analysis. The main COVID-19-related applications reported were pandemic control and surveillance, immunity or vaccine passport monitoring, and contact tracing. The top three non-COVID-19-related applications were management of electronic medical records, internet of things (eg, remote monitoring or mobile health), and supply chain monitoring. Most reports detailed technical performance of the blockchain prototype platforms (277 [66·7%] of 415), whereas nine (2·2%) studies showed real-world clinical application and adoption. The remaining studies (129 [31·1%] of 415) were themselves of a technical design only. The most common platforms used were Ethereum and Hyperledger. Blockchain technology has numerous potential COVID-19-related and non-COVID-19-related applications in health care. However, much of the current research remains at the technical stage, with few providing actual clinical applications, highlighting the need to translate foundational blockchain technology into clinical use.

5G Use in Healthcare: The Future is Present

Background:

Most healthcare providers are unaware of the extraordinary opportunities for implementation in healthcare which can be enabled by 5G wireless networks. 5G created enormous opportunities for a myriad of new technologies, resulting in an integrated through 5G ‘ecosystem’. Although the new opportunities in healthcare are immense, medicine is slow to change, as manifest by the paucity of new, innovative applications based upon this ecosystem. Thus, emerges the need to “avoid technology surprise” - both laparoscopic and robotic assisted minimally invasive surgery were delayed for years because the surgical community was either unaware or unaccepting of a new technology.

Database:

PubMed (Medline) and Scopus (Elsevier) databases were searched and all published studies regarding clinical applications of 5G were retrieved. From a total of 40 articles, 13 were finally included in our review.

Discussion:

The important transformational properties of 5G communications and other innovative technologies are described and compared to healthcare needs, looking for opportunities, limitations, and challenges to implementation of 5G and the ecosystem it has spawned. Furthermore, the needs in the clinical applications, education and research in medicine and surgery, in addition to the administrative infrastructure are addressed. Additionally, we explore the nontechnical challenges, that either support or oppose this new healthcare renovation. Based upon proven advantages of these innovative technologies, current scientific evidence is analyzed for future trends for the transformation of healthcare. By providing awareness of these opportunities and their advantages for patients, it will be possible to decrease the prolonged timeframe for acceptance and implementation for patients.

Artificial intelligence in myopia: current and future trends

PURPOSE OF REVIEW

Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management.

RECENT FINDINGS

There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage.

SUMMARY

Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.

Digital Technology-Based Telemedicine for the COVID-19 Pandemic

In the year 2020, the coronavirus disease 2019 (COVID-19) crisis intersected with the development and maturation of several digital technologies including the internet of things (IoT) with next-generation 5G networks, artificial intelligence (AI) that uses deep learning, big data analytics, and blockchain and robotic technology, which has resulted in an unprecedented opportunity for the progress of telemedicine. Digital technology-based telemedicine platform has currently been established in many countries, incorporated into clinical workflow with four modes, including "many to one" mode, "one to many" mode, "consultation" mode, and "practical operation" mode, and has shown to be feasible, effective, and efficient in sharing epidemiological data, enabling direct interactions among healthcare providers or patients across distance, minimizing the risk of disease infection, improving the quality of patient care, and preserving healthcare resources. In this state-of-the-art review, we gain insight into the potential benefits of demonstrating telemedicine in the context of a huge health crisis by summarizing the literature related to the use of digital technologies in telemedicine applications. We also outline several new strategies for supporting the use of telemedicine at scale.

A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic's dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective

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.

Use of digital healthcare solutions for care delivery during a pandemic-chances and (cyber) risks referring to the example of the COVID-19 pandemic

During pandemics, regular service provisioning processes in medical care may be disrupted. Digital health promises many opportunities for service provisioning during a pandemic. However, a broad penetration of medical processes with information technology also has drawbacks. Within this work, the authors use the COVID-19 pandemic to analyze the chances and the risks that may come with using digital health solutions for medical care during a pandemic. Therefore, a multi-methods approach is used. First we use a systematic literature review for reviewing the state of the art of digital health applications in healthcare. Furthermore, the usage of digital health applications is mapped to the different processes in care delivery. Here we provide an exemplary process model of oncological care delivery. The analysis shows that including digital health solutions may be helpful for care delivery in most processes of medical care provisioning. However, research on digital health solutions focuses strongly on some few processes and specific disciplines while other processes and medical disciplines are underrepresented in literature. Last, we highlight the necessity of a comprehensive risk-related debate around the effects that come with the use of digital healthcare solutions.

Telemedicine in ophthalmology. Part 2. “special teleophthalmology”

In recent years, telemedicine (TM) has been gradually introduced into ophthalmology in the form of teleophthalmology (TO), because most of the eye diseases can be photographed and transmitted via Internet. The most wide development of TO has involved the field of diabetic retinopathy (DR) diagnosis, primarily due to the high prevalence of diabetes mellitus in the world. Examples of the well-established operation of remote DR screening centers exist in different countries of the world. There are many studies published, which compare a remote examination with a personal one, and according to their data, TO screening is no worse, than traditional screening. In addition to DR, TO also covers the diagnosis of glaucoma, age-related macular degeneration, and other ophthalmic conditions. In this article, we present an overview of modern TO centers in different countries, the features of their organization and global results that have been achieved during their existence. New technologies developed to facilitate the work of such centers will also be touched: image analysis algorithms, portable diagnostic equipment, medical information systems. The prospects for introducing TO into Russian medical practice are considered separately.

Digital Technology for AMD Management in the Post-COVID-19 New Normal

PURPOSE

The COVID-19 pandemic has put strain on healthcare systems and the availability and allocation of healthcare manpower, resources and infrastructure. With immediate priorities to protect the health and safety of both patients and healthcare service providers, ophthalmologists globally were advised to defer nonurgent cases, while at the same time managing sight-threatening conditions such as neovascular Age-related Macular Degeneration (AMD). The management of AMD patients both from a monitoring and treatment perspective presents a particular challenge for ophthalmologists. This review looks at how these pressures have encouraged the acceptance and speed of adoption of digitalization.

DESIGN AND METHODS

A literature review was conducted on the use of digital technology during COVID-19 pandemic, and on the transformation of medicine, ophthalmology and AMD screening through digitalization.

RESULTS

In the management of AMD, the implementation of artificial intelligence and "virtual clinics" have provided assistance in screening, diagnosis, monitoring of the progression and the treatment of AMD. In addition, hardware and software developments in home monitoring devices has assisted in self-monitoring approaches.

CONCLUSIONS

Digitalization strategies and developments are currently ongoing and underway to ensure early detection, stability and visual improvement in patients suffering from AMD in this COVID-19 era. This may set a precedence for the post COVID-19 new normal where digital platforms may be routine, standard and expected in healthcare delivery.

Mapping of Health Literacy and Social Panic Via Web Search Data During the COVID-19 Public Health Emergency: Infodemiological Study

BACKGROUND

Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19-related health information.

OBJECTIVE

The aim of this paper is to analyze web search data volumes related to COVID-19 in China.

METHODS

We conducted an infodemiological study to analyze web search data volumes related to COVID-19. Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020).

RESULTS

The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, express more concern about COVID-19 than citizens from other cities since the outbreak first occurred in Wuhan. Wuhan citizens frequently searched for content related to "medical help," "protective materials," and "pandemic progress." Web searches for "return to work" and "go back to school" have increased eight-fold compared to the previous month. Searches for content related to "closed community and remote office" have continued to rise, and searches for "remote office demand" have risen by 663% from the previous quarter. Employees who have returned to work have mainly engaged in the following web searches: "return to work and prevention measures," "return to work guarantee policy," and "time to return to work." Provinces with large, educated populations (eg, Henan, Hebei, and Shandong) have been focusing on "online education" whereas medium-sized cities have been paying more attention to "online medical care."

CONCLUSIONS

Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention and control policies in response to COVID-19.

Reshaping ophthalmology training after COVID-19 pandemic

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on practical activities and didactic teaching of residents and fellows. This survey aimed to propose long-term changes for ophthalmology training based on the changes experienced by trainees and their perception of new training opportunities.

METHODS

An online survey was distributed to ophthalmology trainees in multiple countries. Descriptive statistics were used to analyse the data.

RESULTS

A total of 504 analyzable responses were collected from 32 different countries. The current impact of COVID-19 pandemic was described as "severe" by most trainees (55.2%); however, the future perspective was more optimistic as demonstrated by the greater number of responses reporting a presumed "moderate" (37.3%), "mild" (14.1%) or "slight" (4.2%) long-term impact. The vast majority of trainees reported a decrease ≥50% of clinical activity (76.4%) and >75% of surgical activity (74.6%). Although an initial gap in didactic teaching has been experienced by many (55.4%), regular web-based teaching was reportedly attended by 67.7% of the respondents. A strong agreement was found regarding the worthwhile role of web-based case-presentations in clinical training (91.7%), web-based discussion of edited surgical videos (85.7%) and simulation-based practice (86.9%) in surgical training.

CONCLUSIONS

This survey, focusing on trainees' perspective, strongly reinforces the need to promptly include new technology-based training tools, such as web-based teaching, virtual surgical simulators, and telementoring, in long-term reorganisation of ophthalmology training to ensure its continuity and effectiveness, which would remain available even in the face of another unpredictable crisis within the health system.

Digital technology and COVID-19

The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.