ChatGPT enters the room: what it means for patient counseling, physician education, academics, and disease management.

PURPOSE OF REVIEW: This review seeks to provide a summary of the most recent research findings regarding the utilization of ChatGPT, an artificial intelligence (AI)-powered chatbot, in the field of ophthalmology in addition to exploring the limitations and ethical considerations associated with its application. RECENT FINDINGS: ChatGPT has gained widespread recognition and demonstrated potential in enhancing patient and physician education, boosting research productivity, and streamlining administrative tasks. In various studies examining its utility in ophthalmology, ChatGPT has exhibited fair to good accuracy, with its most recent iteration showcasing superior performance in providing ophthalmic recommendations across various ophthalmic disorders such as corneal diseases, orbital disorders, vitreoretinal diseases, uveitis, neuro-ophthalmology, and glaucoma. This proves beneficial for patients in accessing information and aids physicians in triaging as well as formulating differential diagnoses. Despite such benefits, ChatGPT has limitations that require acknowledgment including the potential risk of offering inaccurate or harmful information, dependence on outdated data, the necessity for a high level of education for data comprehension, and concerns regarding patient privacy and ethical considerations within the research domain. SUMMARY: ChatGPT is a promising new tool that could contribute to ophthalmic healthcare education and research, potentially reducing work burdens. However, its current limitations necessitate a complementary role with human expert oversight.

Application of deep learning algorithms in classification and localization of implant cutout for the postoperative hip.

OBJECTIVE: This study aims to explore the feasibility of employing convolutional neural networks for detecting and localizing implant cutouts on anteroposterior pelvic radiographs. MATERIALS AND METHODS: The research involves the development of two Deep Learning models. Initially, a model was created for image-level classification of implant cutouts using 40191 pelvic radiographs obtained from a single institution. The radiographs were partitioned into training, validation, and hold-out test datasets in a 6/2/2 ratio. Performance metrics including the area under the receiver operator characteristics curve (AUROC), sensitivity, and specificity were calculated using the test dataset. Additionally, a second object detection model was trained to localize implant cutouts within the same dataset. Bounding box visualizations were generated on images predicted as cutout-positive by the classification model in the test dataset, serving as an adjunct for assessing algorithm validity. RESULTS: The classification model had an accuracy of 99.7%, sensitivity of 84.6%, specificity of 99.8%, AUROC of 0.998 (95% CI: 0.996, 0.999) and AUPRC of 0.774 (95% CI: 0.646, 0.880). From the pelvic radiographs predicted as cutout-positive, the object detection model could achieve 95.5% localization accuracy on true positive images, but falsely generated 14 results from the 15 false-positive predictions. CONCLUSION: The classification model showed fair accuracy for detection of implant cutouts, while the object detection model effectively localized cutout. This serves as proof of concept of using a deep learning-based approach for classification and localization of implant cutouts from pelvic radiographs.

"The Cost in the Individual": Longitudinal Burnout Prevalence Among Pediatric Emergency Physicians Through 9 Months of the COVID-19 Pandemic.

OBJECTIVES: Emergency medicine (EM) confers a high risk of burnout that may be exacerbated by the COVID-19 pandemic. We aimed to determine the longitudinal prevalence of burnout in pediatric EM (PEM) physicians/fellows working in tertiary PEM departments across Canada and its fluctuation during the pandemic. METHODS: A national mixed-methods survey using a validated 2-question proxy for burnout was distributed monthly through 9 months. The primary outcome was the trajectory in probability of burnout, which was examined as both emotional exhaustion (EE) and depersonalization (DP), EE alone, and DP alone. Secondary outcomes investigated burnout and its association with demographic variables. Quantitative data were analyzed using logistic regression for primary outcomes and subanalyses for secondary outcomes. Conventional content analysis was used to analyze qualitative data and generate themes. RESULTS: From February to October 2021, 92 of 98 respondents completed at least 1 survey, 78% completed at least 3 consecutive surveys, and 48% completed at least 6 consecutive surveys. Predicted probability of EE was bimodal with peaks in May (25%) and October (22%) 2021. Rates of DP alone or having both EE and DP were approximately 1% and stable over the study period. Mid-career physicians were at lower risk of EE (odds ratio, 0.02; 95% confidence interval, 0-0.22) compared with early-career physicians. Underlying drivers of burnout were multifaceted. CONCLUSIONS: Our study suggests that increased COVID-19 case burden was correlated with EE levels during the third and fourth waves of the pandemic. Emotional exhaustion was worsened by systemic factors, and interventions must target common themes of unsustainable workloads and overwhelming lack of control.

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).

Artificial intelligence enabled smart digital eye wearables.

PURPOSE OF REVIEW: Smart eyewear is a head-worn wearable device that is evolving as the next phase of ubiquitous wearables. Although their applications in healthcare are being explored, they have the potential to revolutionize teleophthalmology care. This review highlights their applications in ophthalmology care and discusses future scope. RECENT FINDINGS: Smart eyewear equips advanced sensors, optical displays, and processing capabilities in a wearable form factor. Rapid technological developments and the integration of artificial intelligence are expanding their reach from consumer space to healthcare applications. This review systematically presents their applications in treating and managing eye-related conditions. This includes remote assessments, real-time monitoring, telehealth consultations, and the facilitation of personalized interventions. They also serve as low-vision assistive devices to help visually impaired, and can aid physicians with operational and surgical tasks. SUMMARY: Wearables such as smart eyewear collects rich, continuous, objective, individual-specific data, which is difficult to obtain in a clinical setting. By leveraging sophisticated data processing and artificial intelligence based algorithms, these data can identify at-risk patients, recognize behavioral patterns, and make timely interventions. They promise cost-effective and personalized treatment for vision impairments in an effort to mitigate the global burden of eye-related conditions and aging.

Gaps and future of human-centered artificial intelligence in ophthalmology: Future Vision Forum consensus statement.

PURPOSE OF REVIEW: The Future Vision Forum discussed the current state of Human Centered Computing and the future of data collection, curation, and collation in ophthalmology. Although the uptake of electronic health record (EHR) systems and the digitization of healthcare data is encouraging, there are still barriers to implementing a specialty-wide clinical trial database. The article identifies several critical opportunities, including the need for standardization of image metadata and data, the establishment of a centralized trial database, incentives for clinicians and trial sponsors to participate, and resolving ethical concerns surrounding data ownership. FINDINGS: Recommendations to overcome these challenges include the standardization of image metadata using the Digital Imaging and Communications in Medicine (DICOM) guidelines, the establishment of a centralized trial database that uses federated learning (FL), and the use of FL to facilitate cross-institutional collaboration for rare diseases. Forum faculty suggests incentives will accelerate artificial intelligence, digital innovation projects, and data sharing agreements to empower patients to release their data. SUMMARY: A specialty-wide clinical trial database could provide invaluable insights into the natural history of disease, pathophysiology, why trials fail, and improve future clinical trial design. However, overcoming the barriers to implementation will require continued discussion, collaboration, and collective action from stakeholders across the ophthalmology community.

Explainable artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models. RECENT FINDINGS: Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks. SUMMARY: We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.

The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus.

PURPOSE OF REVIEW: The aim of this review is to define the "state-of-the-art" in artificial intelligence (AI)-enabled devices that support the management of retinal conditions and to provide Vision Academy recommendations on the topic. RECENT FINDINGS: Most of the AI models described in the literature have not been approved for disease management purposes by regulatory authorities. These new technologies are promising as they may be able to provide personalized treatments as well as a personalized risk score for various retinal diseases. However, several issues still need to be addressed, such as the lack of a common regulatory pathway and a lack of clarity regarding the applicability of AI-enabled medical devices in different populations. SUMMARY: It is likely that current clinical practice will need to change following the application of AI-enabled medical devices. These devices are likely to have an impact on the management of retinal disease. However, a consensus needs to be reached to ensure they are safe and effective for the overall population.

Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology. RECENT FINDINGS: In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models. SUMMARY: The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with headache, hypertension, or any neurological symptoms, yet it is rarely or poorly performed in general clinics. We recently developed a brain and optic nerve study with artificial intelligence-deep learning system (BONSAI-DLS) capable of accurately detecting optic disc abnormalities including papilledema (swelling due to elevated intracranial pressure) on digital fundus photographs with a comparable classification performance to expert neuro-ophthalmologists, but its performance compared to first-line clinicians remains unknown. METHODS: In this international, cross-sectional multicenter study, the DLS, trained on 14,341 fundus photographs, was tested on a retrospectively collected convenience sample of 800 photographs (400 normal optic discs, 201 papilledema and 199 other abnormalities) from 454 patients with a robust ground truth diagnosis provided by the referring expert neuro-ophthalmologists. The areas under the receiver-operating-characteristic curves were calculated for the BONSAI-DLS. Error rates, accuracy, sensitivity, and specificity of the algorithm were compared with those of 30 clinicians with or without ophthalmic training (6 general ophthalmologists, 6 optometrists, 6 neurologists, 6 internists, 6 emergency department [ED] physicians) who graded the same testing set of images. RESULTS: With an error rate of 15.3%, the DLS outperformed all clinicians (average error rates 24.4%, 24.8%, 38.2%, 44.8%, 47.9% for general ophthalmologists, optometrists, neurologists, internists and ED physicians, respectively) in the overall classification of optic disc appearance. The DLS displayed significantly higher accuracies than 100%, 86.7% and 93.3% of clinicians (n = 30) for the classification of papilledema, normal, and other disc abnormalities, respectively. CONCLUSIONS: The performance of the BONSAI-DLS to classify optic discs on fundus photographs was superior to that of clinicians with or without ophthalmic training. A trained DLS may offer valuable diagnostic aid to clinicians from various clinical settings for the screening of optic disc abnormalities harboring potentially sight- or life-threatening neurological conditions.

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial.

Deep learning-based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling "hands-on" education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.

Digital Gonioscopy Based on Three-dimensional Anterior-Segment OCT: An International Multicenter Study.

PURPOSE: To develop and evaluate the performance of a 3-dimensional (3D) deep-learning-based automated digital gonioscopy system (DGS) in detecting 2 major characteristics in eyes with suspected primary angle-closure glaucoma (PACG): (1) narrow iridocorneal angles (static gonioscopy, Task I) and (2) peripheral anterior synechiae (PAS) (dynamic gonioscopy, Task II) on OCT scans. DESIGN: International, cross-sectional, multicenter study. PARTICIPANTS: A total of 1.112 million images of 8694 volume scans (2294 patients) from 3 centers were included in this study (Task I, training/internal validation/external testing: 4515, 1101, and 2222 volume scans, respectively; Task II, training/internal validation/external testing: 378, 376, and 102 volume scans, respectively). METHODS: For Task I, a narrow angle was defined as an eye in which the posterior pigmented trabecular meshwork was not visible in more than 180° without indentation in the primary position captured in the dark room from the scans. For Task II, PAS was defined as the adhesion of the iris to the trabecular meshwork. The diagnostic performance of the 3D DGS was evaluated in both tasks with gonioscopic records as reference. MAIN OUTCOME MEASURES: The area under the curve (AUC), sensitivity, and specificity of the 3D DGS were calculated. RESULTS: In Task I, 29.4% of patients had a narrow angle. The AUC, sensitivity, and specificity of 3D DGS on the external testing datasets were 0.943 (0.933-0.953), 0.867 (0.838-0.895), and 0.878 (0.859-0.896), respectively. For Task II, 13.8% of patients had PAS. The AUC, sensitivity, and specificity of 3D DGS were 0.902 (0.818-0.985), 0.900 (0.714-1.000), and 0.890 (0.841-0.938), respectively, on the external testing set at quadrant level following normal clinical practice; and 0.885 (0.836-0.933), 0.912 (0.816-1.000), and 0.700 (0.660-0.741), respectively, on the external testing set at clock-hour level. CONCLUSIONS: The 3D DGS is effective in detecting eyes with suspected PACG. It has the potential to be used widely in the primary eye care community for screening of subjects at high risk of developing PACG.

Multimodal Machine Learning Using Visual Fields and Peripapillary Circular OCT Scans in Detection of Glaucomatous Optic Neuropathy.

PURPOSE: To develop and validate a multimodal artificial intelligence algorithm, FusionNet, using the pattern deviation probability plots from visual field (VF) reports and circular peripapillary OCT scans to detect glaucomatous optic neuropathy (GON). DESIGN: Cross-sectional study. SUBJECTS: Two thousand four hundred sixty-three pairs of VF and OCT images from 1083 patients. METHODS: FusionNet based on bimodal input of VF and OCT paired data was developed to detect GON. Visual field data were collected using the Humphrey Field Analyzer (HFA). OCT images were collected from 3 types of devices (DRI-OCT, Cirrus OCT, and Spectralis). Two thousand four hundred sixty-three pairs of VF and OCT images were divided into 4 datasets: 1567 for training (HFA and DRI-OCT), 441 for primary validation (HFA and DRI-OCT), 255 for the internal test (HFA and Cirrus OCT), and 200 for the external test set (HFA and Spectralis). GON was defined as retinal nerve fiber layer thinning with corresponding VF defects. MAIN OUTCOME MEASURES: Diagnostic performance of FusionNet compared with that of VFNet (with VF data as input) and OCTNet (with OCT data as input). RESULTS: FusionNet achieved an area under the receiver operating characteristic curve (AUC) of 0.950 (0.931-0.968) and outperformed VFNet (AUC, 0.868 [95% confidence interval (CI), 0.834-0.902]), OCTNet (AUC, 0.809 [95% CI, 0.768-0.850]), and 2 glaucoma specialists (glaucoma specialist 1: AUC, 0.882 [95% CI, 0.847-0.917]; glaucoma specialist 2: AUC, 0.883 [95% CI, 0.849-0.918]) in the primary validation set. In the internal and external test sets, the performances of FusionNet were also superior to VFNet and OCTNet (FusionNet vs VFNet vs OCTNet: internal test set 0.917 vs 0.854 vs 0.811; external test set 0.873 vs 0.772 vs 0.785). No significant difference was found between the 2 glaucoma specialists and FusionNet in the internal and external test sets, except for glaucoma specialist 2 (AUC, 0.858 [95% CI, 0.805-0.912]) in the internal test set. CONCLUSIONS: FusionNet, developed using paired VF and OCT data, demonstrated superior performance to both VFNet and OCTNet in detecting GON, suggesting that multimodal machine learning models are valuable in detecting GON.

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS: Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY: AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare.

INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications in healthcare, including its benefits, limitations and future scope. SOURCES OF DATA: A review of the English literature was conducted with search terms 'AI' or 'ML' or 'deep learning' and 'healthcare' or 'medicine' using PubMED and Google Scholar from 2000-2021. AREAS OF AGREEMENT: AI could transform physician workflow and patient care through its applications, from assisting physicians and replacing administrative tasks to augmenting medical knowledge. AREAS OF CONTROVERSY: From challenges training ML systems to unclear accountability, AI's implementation is difficult and incremental at best. Physicians also lack understanding of what AI implementation could represent. GROWING POINTS: AI can ultimately prove beneficial in healthcare, but requires meticulous governance similar to the governance of physician conduct. AREAS TIMELY FOR DEVELOPING RESEARCH: Regulatory guidelines are needed on how to safely implement and assess AI technology, alongside further research into the specific capabilities and limitations of its medical use.

Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis.

TOPIC: To provide updated estimates on the global prevalence and number of people with diabetic retinopathy (DR) through 2045. CLINICAL RELEVANCE: The International Diabetes Federation (IDF) estimated the global population with diabetes mellitus (DM) to be 463 million in 2019 and 700 million in 2045. Diabetic retinopathy remains a common complication of DM and a leading cause of preventable blindness in the adult working population. METHODS: We conducted a systematic review using PubMed, Medline, Web of Science, and Scopus for population-based studies published up to March 2020. Random effect meta-analysis with logit transformation was performed to estimate global and regional prevalence of DR, vision-threatening DR (VTDR), and clinically significant macular edema (CSME). Projections of DR, VTDR, and CSME burden were based on population data from the IDF Atlas 2019. RESULTS: We included 59 population-based studies. Among individuals with diabetes, global prevalence was 22.27% (95% confidence interval [CI], 19.73%-25.03%) for DR, 6.17% (95% CI, 5.43%-6.98%) for VTDR, and 4.07% (95% CI, 3.42%-4.82%) for CSME. In 2020, the number of adults worldwide with DR, VTDR, and CSME was estimated to be 103.12 million, 28.54 million, and 18.83 million, respectively; by 2045, the numbers are projected to increase to 160.50 million, 44.82 million, and 28.61 million, respectively. Diabetic retinopathy prevalence was highest in Africa (35.90%) and North American and the Caribbean (33.30%) and was lowest in South and Central America (13.37%). In meta-regression models adjusting for habitation type, response rate, study year, and DR diagnostic method, Hispanics (odds ratio [OR], 2.92; 95% CI, 1.22-6.98) and Middle Easterners (OR, 2.44; 95% CI, 1.51-3.94) with diabetes were more likely to have DR compared with Asians. DISCUSSION: The global DR burden is expected to remain high through 2045, disproportionately affecting countries in the Middle East and North Africa and the Western Pacific. These updated estimates may guide DR screening, treatment, and public health care strategies.

Optic Disc Classification by Deep Learning versus Expert Neuro-Ophthalmologists.

OBJECTIVE: To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. METHODS: The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. RESULTS: The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96-0.98), 0.96 (95% CI = 0.94-0.97), and 0.89 (95% CI = 0.87-0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67-0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68-0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61-0.70) between the system and Expert 2. INTERPRETATION: The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785-795.

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions.

PURPOSE OF REVIEW: Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment. RECENT FINDINGS: The integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology. SUMMARY: Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.

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.