Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging

Background

Diabetes mellitus (DM) increases the risk of vascular complications, and retinal vasculature imaging serves as a valuable indicator of both microvascular and macrovascular health. Moreover, artificial intelligence (AI)-enabled systems developed for high-throughput detection of diabetic retinopathy (DR) using digitized retinal images have become clinically adopted. This study reviews AI applications using retinal images for DM-related complications, highlighting advancements beyond DR screening, diagnosis, and prognosis, and addresses implementation challenges, such as ethics, data privacy, equitable access, and explainability.

Methods

We conducted a thorough literature search across several databases, including PubMed, Scopus, and Web of Science, focusing on studies involving diabetes, the retina, and artificial intelligence. We reviewed the original research based on their methodology, AI algorithms, data processing techniques, and validation procedures to ensure a detailed analysis of AI applications in diabetic retinal imaging.

Results

Retinal images can be used to diagnose DM complications including DR, neuropathy, nephropathy, and atherosclerotic cardiovascular disease, as well as to predict the risk of cardiovascular events. Beyond DR screening, AI integration also offers significant potential to address the challenges in the comprehensive care of patients with DM.

Conclusion

With the ability to evaluate the patient's health status in relation to DM complications as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.

Diabetic retinopathy-recommendations for screening and treatment

Diabetic retinopathy (DR), the prevalence of which continues to rise, is one of the most common causes of vision loss worldwide. Experimental and clinical research in recent years has contributed to a better understanding of the pathogenesis of DR, which is complex and results from many interrelated processes leading to abnormal permeability and occlusion of the retinal vasculature, with ischemia and subsequent neovascularization. According to the absence or presence of neovascularization, DR is divided into two main forms: nonproliferative and proliferative DR. From nonproliferative to proliferative disease, diabetic macular edema (DME) can develop anywhere along the spectrum. As the majority of diabetics have no ophthalmologic symptoms, screening plays an important role in preventing the development of retinal disease. Specific treatment options beyond metabolic risk factor control, including intravitreal administration of anti-vascular endothelial growth factor (VEGF) agents or corticosteroids, laser photocoagulation, and vitreous surgery, are effective approaches for ocular diabetic complications.

Application of Artificial Intelligence to Deliver Healthcare From the Eye

Importance

Oculomics is the science of analyzing ocular data to identify, diagnose, and manage systemic disease. This article focuses on prescreening, its use with retinal images analyzed by artificial intelligence (AI), to identify ocular or systemic disease or potential disease in asymptomatic individuals. The implementation of prescreening in a coordinated care system, defined as Healthcare From the Eye prescreening, has the potential to improve access, affordability, equity, quality, and safety of health care on a global level. Stakeholders include physicians, payers, policymakers, regulators and representatives from industry, government, and data privacy sectors.

Observations

The combination of AI analysis of ocular data with automated technologies that capture images during routine eye examinations enables prescreening of large populations for chronic disease. Retinal images can be acquired during either a routine eye examination or in settings outside of eye care with readily accessible, safe, quick, and noninvasive retinal imaging devices. The outcome of such an examination can then be digitally communicated across relevant stakeholders in a coordinated fashion to direct a patient to screening and monitoring services. Such an approach offers the opportunity to transform health care delivery and improve early disease detection, improve access to care, enhance equity especially in rural and underserved communities, and reduce costs.

Conclusions and Relevance

With effective implementation and collaboration among key stakeholders, this approach has the potential to contribute to an equitable and effective health care system.

What do You Need to Know after Diabetes and before Diabetic Retinopathy?

Diabetic retinopathy (DR) is a leading cause of vision impairment and blindness among individuals with diabetes mellitus. Current clinical diagnostic criteria mainly base on visible vascular structure changes, which are insufficient to identify diabetic patients without clinical DR (NDR) but with dysfunctional retinopathy. This review focuses on retinal endothelial cells (RECs), the first cells to sense and respond to elevated blood glucose. As blood glucose rises, RECs undergo compensatory and transitional phases, and the correspondingly altered molecules are likely to become biomarkers and targets for early prediction and treatment of NDR with dysfunctional retinopathy. This article elaborated the possible pathophysiological processes focusing on RECs and summarized recently published and reliable biomarkers for early screening and emerging intervention strategies for NDR patients with dysfunctional retinopathy. Additionally, references for clinical medication selection and lifestyle recommendations for this population are provided. This review aims to deepen the understanding of REC biology and NDR pathophysiology, emphasizes the importance of early detection and intervention, and points out future directions to improve the diagnosis and treatment of NDR with dysfunctional retinopathy and to reduce the occurrence of DR.

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning

OBJECTIVE

Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subphenotypes of Alzheimer's disease progression based on longitudinal real-world patient records.

METHODS

The framework, dynaPhenoM, extracts coherent clinical topics across patient visits and employs a time-aware latent class analysis to characterize subphenotypes. We validated dynaPhenoM using three patient databases with a total of 3952 AD patients across the United States, demonstrating its effectiveness in revealing mild cognitive impairment (MCI) progression to AD.

RESULTS

Our study identified five subphenotypes associated with distinct organ systems for disease progression from MCI to AD, including common subtypes across cohorts-respiratory, musculoskeletal, cardiovascular, and endocrine/metabolic-as well as a cohort-specific digestive subtype.

CONCLUSION

Our study unravels the complexity and heterogeneity of the progression from MCI to AD. These findings highlight disease progression heterogeneity and can inform both diagnostic and therapeutic strategies, thereby advancing precision medicine for Alzheimer's disease.

Artificial intelligence in the diagnosis and management of refractive errors

Refractive error is among the leading causes of visual impairment globally. The diagnosis and management of refractive error has traditionally relied on comprehensive eye examinations by eye care professionals, but access to these specialized services has remained limited in many areas of the world. Given this, artificial intelligence (AI) has shown immense potential in transforming the diagnosis and management of refractive error. We review AI applications across various aspects of refractive error care - from axial length prediction using fundus images to risk stratification for myopia progression. AI algorithms can be trained to analyze clinical data to detect refractive error as well as predict associated risks of myopia progression. For treatments such as implantable collamer and orthokeratology lenses, AI models facilitate vault size prediction and optimal lens fitting with high accuracy. Furthermore, AI has demonstrated promise in optimizing surgical planning and outcomes for refractive procedures. Emerging digital technologies such as telehealth, smartphone applications, and virtual reality integrated with AI present novel avenues for refractive error screening. We discuss key challenges, including limited validation datasets, lack of data standardization, image quality issues, population heterogeneity, practical deployment, and ethical considerations regarding patient privacy that need to be addressed before widespread clinical implementation.

Detection Rate of Diabetic Retinopathy Before and After Implementation of Autonomous AI-based Fundus Photograph Analysis in a Resource-Limited Area in Belize

Purpose

To evaluate the use of an autonomous artificial intelligence (AI)-based device to screen for diabetic retinopathy (DR) and to evaluate the frequency of diabetes mellitus (DM) and DR in an under-resourced population served by the Stanford Belize Vision Clinic (SBVC).

Patients and Methods

The records of all patients from 2017 to 2024 were collected and analyzed, dividing the study into two time periods: Pre-AI (before June 2022, prior to the implementation of the LumineticsCore® device at SBVC) and Post-AI (from June 2022 to the present) and subdivided into post-COVID19 and pre-COVID19 periods. Patients were categorized based on self-reported past medical history (PMH) as DM positive (diagnosed DM) and DM negative (no PMH of DM). AI camera outcomes included: negative for more than mild DR (MTMDR), positive for MTMDR, and insufficient exam quality.

Results

A total of 1897 patients with a mean age of 47.6 years were included. The gradability of encounters by the AI device was 89.1%. The frequency of DR detection increased significantly in the Post-AI period (55/639) compared to the Pre-AI period (38/1258), including during the COVID-19 pandemic. The mean age of DR diagnosis was significantly lower in the Post-AI period (44.1 years) compared to Pre-AI period (60.7 years) among DM negative patients. There was a significant association between having DR and hypertension. Additionally, the detection rate of DM increased in the Post-AI period compared to Pre-AI period.

Conclusion

Autonomous AI-based screening significantly improves the detection of patients with DR in areas with limited healthcare resources by reducing dependence on on-field ophthalmologists. This innovative approach can be seamlessly integrated into primary care settings, with technicians capturing images quickly and efficiently within just a few minutes. This study demonstrates the effectiveness of autonomous AI in identifying patients with both DR and DM, as well as associated high-burden diseases such as hypertension, across various age ranges.

Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis

Objective

To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).

Methods

We conducted a comprehensive literature search in multiple databases including PubMed, Cochrane library, Web of Science, Embase and IEEE Xplore up to July 2024. Studies that utilized deep learning techniques for the detection of DR using OCT and retinal images were included. Data extraction and quality assessment were performed independently by two reviewers. Meta-analysis was conducted to determine pooled sensitivity, specificity, and diagnostic odds ratios.

Results

A total of 47 studies were included in the systematic review, 10 were meta-analyzed, encompassing a total of 188268 retinal images and OCT scans. The meta-analysis revealed a pooled sensitivity of 1.88 (95% CI: 1.45-2.44) and a pooled specificity of 1.33 (95% CI: 0.97-1.84) for the detection of DR using deep learning models. All of the outcome of deep learning-based optical coherence tomography ORs ≥0.785, indicating that all included studies with artificial intelligence assistance produced good boosting results.

Conclusion

Deep learning-based approaches show high accuracy in detecting diabetic retinopathy from OCT and retinal images, supporting their potential as reliable tools in clinical settings. Future research should focus on standardizing datasets, improving model interpretability, and validating performance across diverse populations.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024575847.

Valuable insights into general practice staff's experiences and perspectives on AI-assisted diabetic retinopathy screening-An interview study

Aim

This study explores the hands-on experiences and perspectives of general practice staff regarding the feasibility of conducting artificial intelligence-assisted (AI-assisted) diabetic retinopathy screenings (DRS) in general practice settings.

Method

The screenings were tested in 12 general practices in the North Denmark Region and were conducted as part of daily care routines over ~4 weeks. Subsequently, 21 staff members involved in the DRS were interviewed.

Results

Thematic analysis generated four main themes: (1) Experiences with DRS in daily practice, (2) Effective DRS implementation in general practice in the future, (3) Trust and approval of AI-assisted DRS in general practice, and (4) Implications of DRS in general practice. The findings suggest that general practice staff recognise the potential for AI-assisted DRS to be integrated into their clinical workflows. However, they also emphasise the importance of addressing both practical and systemic factors to ensure successful implementation of DRS within the general practice setting.

Conclusion

Focusing on the practical experiences and perspectives of general practice staff, this study lays the groundwork for future research aimed at optimising the implementation of AI-assisted DRS in general practice settings, while recognising that the insights gained may also inform broader primary care contexts.

Practical Applications of Artificial Intelligence Diagnostic Systems in Fundus Retinal Disease Screening

Purpose

This study aims to evaluate the performance of a deep learning-based artificial intelligence (AI) diagnostic system in the analysis of retinal diseases, assessing its consistency with expert diagnoses and its overall utility in screening applications.

Methods

A total of 3076 patients attending our hospital underwent comprehensive ophthalmic examinations. Initial assessments were performed using the AI, the Comprehensive AI Retinal Expert (CARE) system, followed by thorough manual reviews to establish final diagnoses. A comparative analysis was conducted between the AI-generated results and the evaluations by senior ophthalmologists to assess the diagnostic reliability and feasibility of the AI system in the context of ophthalmic screening.

Results

: The AI diagnostic system demonstrated a sensitivity of 94.12% and specificity of 98.60% for diabetic retinopathy (DR); 89.50% sensitivity and 98.33% specificity for age-related macular degeneration (AMD); 91.55% sensitivity and 97.40% specificity for suspected glaucoma; 90.77% sensitivity and 99.10% specificity for pathological myopia; 81.58% sensitivity and 99.49% specificity for retinal vein occlusion (RVO); 88.64% sensitivity and 99.18% specificity for retinal detachment; 83.33% sensitivity and 99.80% specificity for macular hole; 82.26% sensitivity and 99.23% specificity for epiretinal membrane; 94.55% sensitivity and 97.82% specificity for hypertensive retinopathy; 83.33% sensitivity and 99.74% specificity for myelinated fibers; and 75.00% sensitivity and 99.95% specificity for retinitis pigmentosa. Additionally, the system exhibited notable performance in screening for other prevalent conditions, including DR, suspected glaucoma, suspected glaucoma, pathological myopia, and hypertensive retinopathy.

Conclusions

: The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices. Its implementation is particularly beneficial for grassroots and community healthcare settings, facilitating initial diagnostic efforts and enhancing the efficacy of tiered ophthalmic care, with important implications for broader clinical adoption.

Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence

The integration of artificial intelligence (AI) in ophthalmology is transforming the field, offering new opportunities to enhance diagnostic accuracy, personalize treatment plans, and improve service delivery. This review provides a comprehensive overview of the current applications and future potential of AI in ophthalmology. AI algorithms, particularly those utilizing machine learning (ML) and deep learning (DL), have demonstrated remarkable success in diagnosing conditions such as diabetic retinopathy (DR), age-related macular degeneration, and glaucoma with precision comparable to, or exceeding, human experts. Furthermore, AI is being utilized to develop personalized treatment plans by analyzing large datasets to predict individual responses to therapies, thus optimizing patient outcomes and reducing healthcare costs. In surgical applications, AI-driven tools are enhancing the precision of procedures like cataract surgery, contributing to better recovery times and reduced complications. Additionally, AI-powered teleophthalmology services are expanding access to eye care in underserved and remote areas, addressing global disparities in healthcare availability. Despite these advancements, challenges remain, particularly concerning data privacy, security, and algorithmic bias. Ensuring robust data governance and ethical practices is crucial for the continued success of AI integration in ophthalmology. In conclusion, future research should focus on developing sophisticated AI models capable of handling multimodal data, including genetic information and patient histories, to provide deeper insights into disease mechanisms and treatment responses. Also, collaborative efforts among governments, non-governmental organizations (NGOs), and technology companies are essential to deploy AI solutions effectively, especially in low-resource settings.

The Role of Diet and Oral Supplementation for the Management of Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review

Globally, diabetic retinopathy (DR) and diabetic macular edema (DME) are the leading causes of visual loss in working people. Current treatment approaches mostly target proliferative DR and DME, such as intravitreal injections of antivascular endothelial growth factor agents and laser photocoagulation. Before DR progresses into the more severe, sight-threatening proliferative stage, patients with early stages of the disease must get early and appropriate care. It has been suggested that nutraceuticals, which are natural functional foods with minimal adverse effects, may help diabetic patients with DR and DME. Several in vitro and in vivo studies were carried out over the last years, showing the potential benefits of several nutraceuticals in DR due to their neuroprotective, vasoprotective, anti-inflammatory, and antioxidant properties. Although most of the research is restricted to animal models and many nutraceuticals have low bioavailability, these compounds may adjuvate and implement conventional DR therapies. The purpose of this review is (i) to summarize the complex pathophysiology underlying DR and DME and (ii) to examine the main natural-derived molecules and dietary habits that can assist conventional therapies for the clinical management of DR and DME.

Autonomous Artificial Intelligence for Diabetic Eye Disease Testing Improves Access and Equity in the Pediatric and Adult Populations: The Johns Hopkins Medicine Experience

This article discusses the implementation and impact of autonomous artificial intelligence (AI) systems for diabetic eye disease testing at the Johns Hopkins Medicine health system, highlighting improvements in screening rates, access to care, and health equity for underserved populations. The AI technology has been effective in both adult and pediatric populations and has reduced disparities and increased follow-up with eye care professionals. While considering the challenges and successes of this approach, this article also highlights the potential long-term impact of AI systems in improving visual health outcomes for people with diabetes in diverse health care settings.

Current status and future directions of explainable artificial intelligence in medical imaging

The inherent "black box" nature of AI algorithms presents a substantial barrier to the widespread adoption of the technology in clinical settings, leading to a lack of trust among users. This review begins by examining the foundational stages involved in the interpretation of medical images by radiologists and clinicians, encompassing both type 1 (fast thinking - ability of the brain to think and act intuitively) and type 2 (slow analytical - slow analytical, laborious approach to decision-making) decision-making processes. The discussion then delves into current Explainable AI (XAI) approaches, exploring both inherent and post-hoc explainability for medical imaging applications and highlighting the milestones achieved. XAI in medicine refers to AI system designed to provide transparent, interpretable, and understandable reasoning behind AI predictions or decisions. Additionally, the paper showcases some commercial AI medical systems that offer explanations through features such as heatmaps. Opportunities, challenges and potential avenues for advancing the field are also addressed. In conclusion, the review observes that state-of-the-art XAI methods are not mature enough for implementation, as the explanations they provide are challenging for medical experts to comprehend. Deeper understanding of the cognitive mechanisms by medical professionals is important in aiming to develop more interpretable XAI methods.

Cost-effectiveness of AI for pediatric diabetic eye exams from a health system perspective

Autonomous artificial intelligence (AI) for pediatric diabetic retinal disease (DRD) screening has demonstrated safety, effectiveness, and the potential to enhance health equity and clinician productivity. We examined the cost-effectiveness of an autonomous AI strategy versus a traditional eye care provider (ECP) strategy during the initial year of implementation from a health system perspective. The incremental cost-effectiveness ratio (ICER) was the main outcome measure. Compared to the ECP strategy, the base-case analysis shows that the AI strategy results in an additional cost of $242 per patient screened to a cost saving of $140 per patient screened, depending on health system size and patient volume. Notably, the AI screening strategy breaks even and demonstrates cost savings when a pediatric endocrine site screens 241 or more patients annually. Autonomous AI-based screening consistently results in more patients screened with greater cost savings in most health system scenarios.

14. Children and Adolescents: Standards of Care in Diabetes-2025

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Innovations in Diabetes Management for Pregnant Women: Artificial Intelligence and the Internet of Medical Things

Pregnancies impacted by diabetes face the compounded challenge of strict glycemic control with mounting insulin resistance as the pregnancy progresses. New technological advances, including artificial intelligence (AI) and the Internet of Medical Things (IoMT), are revolutionizing health care delivery by providing innovative solutions for diabetes care during pregnancy. Together, AI and the IoMT are a multibillion-dollar industry that integrates advanced medical devices and sensors into a connected network that enables continuous monitoring of glucose levels. AI-driven clinical decision support systems (CDSSs) can predict glucose trends and provide tailored evidence-based treatments with real-time adjustments as insulin resistance changes with placental growth. Additionally, mobile health (mHealth) applications facilitate patient education and self-management through real-time tracking of diet, physical activity, and glucose levels. Remote monitoring capabilities are particularly beneficial for pregnant persons with diabetes as they extend quality care to underserved populations and reduce the need for frequent in-person visits. This high-resolution monitoring allows physicians and patients access to an unprecedented wealth of data to make more informed decisions based on real-time data, reducing complications for both the mother and fetus. These technologies can potentially improve maternal and fetal outcomes by enabling timely, individualized interventions based on personalized health data. While AI and IoMT offer significant promise in enhancing diabetes care for improved maternal and fetal outcomes, their implementation must address challenges such as data security, cost-effectiveness, and preserving the essential patient-provider relationship. KEY POINTS: · The IoMT expands how patients interact with their health care.. · AI has widespread application in the care of pregnancies complicated by diabetes.. · A need for validation and black-box methodologies challenges the application of AI-based tools.. · As research in AI grows, considerations for data privacy and ethical dilemmas will be required..

Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease

Where adopted, Autonomous artificial Intelligence (AI) for Diabetic Retinal Disease (DRD) resolves longstanding racial, ethnic, and socioeconomic disparities, but AI adoption bias persists. This preregistered trial determined sensitivity and specificity of a previously FDA authorized AI, improved to compensate for lower contrast and smaller imaged area of a widely adopted, lower cost, handheld fundus camera (RetinaVue700, Baxter Healthcare, Deerfield, IL) to identify DRD in participants with diabetes without known DRD, in primary care. In 626 participants (1252 eyes) 50.8% male, 45.7% Hispanic, 17.3% Black, DRD prevalence was 29.0%, all prespecified non-inferiority endpoints were met and no racial, ethnic or sex bias was identified, against a Wisconsin Reading Center level I prognostic standard using widefield stereoscopic photography and macular Optical Coherence Tomography. Results suggest this improved autonomous AI system can mitigate AI adoption bias, while preserving safety and efficacy, potentially contributing to rapid scaling of health access equity. ClinicalTrials.gov NCT05808699 (3/29/2023).

Feasibility and Patient Experience of a Pilot Artificial Intelligence-Based Diabetic Retinopathy Screening Program in Northern Ontario

PURPOSE

To assess the feasibility, implementation, and patient experience of autonomous artificial intelligence-based diabetic retinopathy detection models.

METHODS

This was a prospective cohort study where consenting adult participants previously diagnosed with diabetes were screened for diabetic retinopathy using retinal imaging with autonomous artificial intelligence (AI) interpretation at their routine primary care appointment from December 2022 through October 2023 in Thunder Bay, Ontario. Demographic (age, sex, race) and clinical (type and duration of diabetes, last reported eye exam) data were collected using a data collection form. A 5-point Likert scale questionnaire was completed by participants to assess patient experience following the AI exam.

RESULTS

Among the 202 participants (38.6% women) with a mean age of 70.8 ± 11.7 years included in the study and screened by AI, the exam was successfully completed by 93.6% (n = 189), with only 1.5% (n = 3) requiring dilating eyedrops. The most common reason for an unsuccessful exam was small pupils with patient refusal for dilating eyedrops (n = 4). Among the participants with successful eye exams, 22.2% (n = 42) had referable diabetic retinopathy detected and were referred to see an ophthalmologist; 32/42 (76.0%) of these attended their ophthalmologist appointment. A total of 184 participants completed the satisfaction questionnaire; the mean score (out of 5) for satisfaction with the addition of an eye exam to their primary care visit was 4.8 ± 0.6.

CONCLUSION

Screening for diabetic retinopathy using autonomous artificial intelligence in a primary care setting is feasible and acceptable. This approach has significant advantages for both physicians and patients while achieving very high patient satisfaction.

Artificial intelligence in corneal diseases: A narrative review

Corneal diseases represent a growing public health burden, especially in resource-limited settings lacking access to specialized eye care. Artificial intelligence (AI) offers promising solutions for automating the diagnosis and management of corneal conditions. This narrative review examines the application of AI in corneal diseases, focusing on keratoconus, infectious keratitis, pterygium, dry eye disease, Fuchs endothelial corneal dystrophy, and corneal transplantation. AI models integrating diverse imaging modalities (e.g., corneal topography, slit-lamp, and anterior segment OCT images) and clinical data have demonstrated high diagnostic accuracy, often outperforming human experts. Emerging trends include the incorporation of biomechanical data to enhance keratoconus detection, leveraging in vivo confocal microscopy for diagnosing infectious keratitis, and employing multimodal approaches for comprehensive disease analysis. Additionally, AI has shown potential in predicting disease progression, treatment outcomes, and postoperative complications in corneal transplantation. While challenges remain such as population heterogeneity, limited external validation, and the "black box" nature of some models, ongoing advancement in explainable AI, data augmentation, and improved regulatory frameworks can serve to address these limitations.

Novel artificial intelligence for diabetic retinopathy and diabetic macular edema: what is new in 2024?

PURPOSE OF REVIEW

Given the increasing global burden of diabetic retinopathy and the rapid advancements in artificial intelligence, this review aims to summarize the current state of artificial intelligence technology in diabetic retinopathy detection and management, assessing its potential to improve care and visual outcomes in real-world settings.

RECENT FINDINGS

Most recent studies focused on the integration of artificial intelligence in the field of diabetic retinopathy screening, focusing on real-world efficacy and clinical implementation of such artificial intelligence models. Additionally, artificial intelligence holds the potential to predict diabetic retinopathy progression, enhance personalized treatment strategies, and identify systemic disease biomarkers from ocular images through 'oculomics', moving towards a more precise, efficient, and accessible care. The emergence of foundation model architectures and generative artificial intelligence, which more clearly reflect the clinical care process, may enable rapid advances in diabetic retinopathy care, research and medical education.

SUMMARY

This review explores the emerging technology of artificial intelligence to assess the potential to improve patient outcomes and optimize personalized management in healthcare delivery and medical research. While artificial intelligence is expected to play an increasingly important role in diabetic retinopathy care, ongoing research and clinical trials are essential to address implementation issues and focus on long-term patient outcomes for successful real-world adoption of artificial intelligence in diabetic retinopathy.

Diabetic retinopathy: New concepts of screening, monitoring, and interventions

The science of diabetes care has progressed to provide a better understanding of the oxidative and inflammatory lesions and pathophysiology of the neurovascular unit within the retina (and brain) that occur early in diabetes, even prediabetes. Screening for retinal structural abnormalities, has traditionally been performed by fundus examination or color fundus photography; however, these imaging techniques detect the disease only when there are sufficient lesions, predominantly hemorrhagic, that are recognized to occur late in the disease process after significant neuronal apoptosis and atrophy, as well as microvascular occlusion with alterations in vision. Thus, interventions have been primarily oriented toward the later-detected stages, and clinical trials, while demonstrating a slowing of the disease progression, demonstrate minimal visual improvement and modest reduction in the continued loss over prolonged periods. Similarly, vision measurement utilizing charts detects only problems of visual function late, as the process begins most often parafoveally with increasing number and progressive expansion, including into the fovea. While visual acuity has long been used to define endpoints of visual function for such trials, current methods reviewed herein are found to be imprecise. We review improved methods of testing visual function and newer imaging techniques with the recommendation that these must be utilized to discover and evaluate the injury earlier in the disease process, even in the prediabetic state. This would allow earlier therapy with ocular as well as systemic pharmacologic treatments that lower the and neuro-inflammatory processes within eye and brain. This also may include newer, micropulsed laser therapy that, if applied during the earlier cascade, should result in improved and often normalized retinal function without the adverse treatment effects of standard photocoagulation therapy.

Ethical Challenges and Opportunities in Applying Artificial Intelligence to Cardiovascular Medicine

Much anticipation surrounds artificial intelligence's (AI) emergence as a promising tool in health care. It offers potential to revolutionise clinical practice through assistive and autonomous operation. The high prevalence of cardiac disease globally provides an opportunity for AI technology to increase health care efficiency and improve patient outcomes. This article explores the ethical considerations necessary for safe and acceptable implantation of AI within the health care space. We aim to highlight several challenges such as data privacy, consent, sustainability, and cybersecurity. In addition, we outline the future opportunities for AI use in cardiovascular medicine. Overall, we argue that AI deployment demands robust regulation, transparent algorithms, and safeguarding of patient privacy.

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research

OBJECTIVES

To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation.

DESIGN

This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR.

SETTING

Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling.

PARTICIPANTS

A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study.

RESULTS

Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process).

CONCLUSIONS

The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.

Ethical Considerations in the Design and Conduct of Clinical Trials of Artificial Intelligence

Importance

Safe integration of artificial intelligence (AI) into clinical settings often requires randomized clinical trials (RCT) to compare AI efficacy with conventional care. Diabetic retinopathy (DR) screening is at the forefront of clinical AI applications, marked by the first US Food and Drug Administration (FDA) De Novo authorization for an autonomous AI for such use.

Objective

To determine the generalizability of the 7 ethical research principles for clinical trials endorsed by the National Institute of Health (NIH), and identify ethical concerns unique to clinical trials of AI.

Design, Setting, and Participants

This qualitative study included semistructured interviews conducted with 11 investigators engaged in the design and implementation of clinical trials of AI for DR screening from November 11, 2022, to February 20, 2023. The study was a collaboration with the ACCESS (AI for Children's Diabetic Eye Exams) trial, the first clinical trial of autonomous AI in pediatrics. Participant recruitment initially utilized purposeful sampling, and later expanded with snowball sampling. Study methodology for analysis combined a deductive approach to explore investigators' perspectives of the 7 ethical principles for clinical research endorsed by the NIH and an inductive approach to uncover the broader ethical considerations implementing clinical trials of AI within care delivery.

Results

A total of 11 participants (mean [SD] age, 47.5 [12.0] years; 7 male [64%], 4 female [36%]; 3 Asian [27%], 8 White [73%]) were included, with diverse expertise in ethics, ophthalmology, translational medicine, biostatistics, and AI development. Key themes revealed several ethical challenges unique to clinical trials of AI. These themes included difficulties in measuring social value, establishing scientific validity, ensuring fair participant selection, evaluating risk-benefit ratios across various patient subgroups, and addressing the complexities inherent in the data use terms of informed consent.

Conclusions and Relevance

This qualitative study identified practical ethical challenges that investigators need to consider and negotiate when conducting AI clinical trials, exemplified by the DR screening use-case. These considerations call for further guidance on where to focus empirical and normative ethical efforts to best support conduct clinical trials of AI and minimize unintended harm to trial participants.

Modern Approach to Diabetic Retinopathy Diagnostics

This article reviews innovative diagnostic approaches for diabetic retinopathy as the prevalence of diabetes mellitus and its complications continue to escalate. Novel techniques focus on early disease detection. Technological innovations, such as teleophthalmology, smartphone-based photography, artificial intelligence with deep learning, or widefield photography, can enhance diagnostic accuracy and accelerate the treatment. The review highlights teleophthalmology and handheld photography as promising solutions for remote eye care. These methods revolutionize diabetic retinopathy screening, offering cost-effective and accessible solutions. However, the use of these techniques may be limited by insurance coverage in certain world regions. Ultra-widefield photography offers a comprehensive view of up to 80.0% of the retina in a single image, compared to the 34.0% coverage of the traditional seven-field imaging protocol. It allows retinal imaging without pupil dilation, especially for individuals with compromised mydriasis. However, they also have drawbacks, including high costs, artifacts from eyelashes, eyelid margins, and peripheral distortion. Recent advances in artificial intelligence and machine learning, particularly through convolutional neural networks, are revolutionizing diabetic retinopathy diagnostics, enhancing screening efficiency and accuracy. FDA-approved Artificial Intelligence-powered devices such as LumineticsCore™, EyeArt, and AEYE Diagnostic Screening demonstrate high sensitivity and specificity in diabetic retinopathy detection. While Artificial Intelligence offers the potential to improve patient outcomes and reduce treatment costs, challenges such as dataset biases, high initial costs, and cybersecurity risks must be considered to ensure safety and efficiency. Nanotechnology advancements further enhance diagnosis, offering highly branched polyethyleneimine particles with fluorescein sodium (PEI-NHAc-FS) for better fluorescein angiography or vanadium oxide-based metabolic fingerprinting for early detection.

The combination of deep learning and pseudo-MS image improves the applicability of metabolomics to congenital heart defect prenatal screening

To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) = 1.00). The convolutional neural network (CNN) model also demonstrated commendable performance and clinical utility (AUC = 0.89 in the training set). Notably, in the validation set, the performance of the CNN model (AUC = 0.66, precision = 0.714) exhibited better robustness than the six models above (AUC≤0.50). In conclusion, the CNN model based on pseudo-MS images holds promise for real-world and clinical applications due to its better repeatability.

Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations

Diabetic eye disease (DED) is a leading cause of blindness in the world. Annual DED testing is recommended for adults with diabetes, but adherence to this guideline has historically been low. In 2020, Johns Hopkins Medicine (JHM) began deploying autonomous AI for DED testing. In this study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and how this differed across patient populations. JHM primary care sites were categorized as "non-AI" (no autonomous AI deployment) or "AI-switched" (autonomous AI deployment by 2021). We conducted a propensity score weighting analysis to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes (>17,000) managed within JHM and has three major findings. First, AI-switched sites experienced a 7.6 percentage point greater increase in DED testing than non-AI sites from 2019 to 2021 (p < 0.001). Second, the adherence rate for Black/African Americans increased by 12.2 percentage points within AI-switched sites but decreased by 0.6% points within non-AI sites (p < 0.001), suggesting that autonomous AI deployment improved access to retinal evaluation for historically disadvantaged populations. Third, autonomous AI is associated with improved health equity, e.g. the adherence rate gap between Asian Americans and Black/African Americans shrank from 15.6% in 2019 to 3.5% in 2021. In summary, our results from real-world deployment in a large integrated healthcare system suggest that autonomous AI is associated with improvement in overall DED testing adherence, patient access, and health equity.

Studying harms of interventions with an equity lens in randomized trials

Equity and health equity are fundamental pillars in fostering a just and inclusive society. While equity underscores fairness in resource allocation and opportunity, health equity aims to eradicate avoidable health disparities among social groups. The concept of harms in interventions-undesirable consequences associated with the use of interventions-often varies across populations due to biological and social factors, necessitating a nuanced understanding. An equity lens reveals disparities in harm distribution, urging researchers and policymakers to address these differences in their decision-making processes. Furthermore, interventions, even well-intentioned ones, can inadvertently exacerbate disparities, emphasizing the need for comprehensive harm assessment. Integrating equity considerations in research practices and trial methodologies, through study design or through practices such as inclusive participant recruitment, is pivotal in advancing health equity. By prioritizing interventions that address disparities and ensuring inclusivity in research, we can foster a more equitable healthcare system.

Unveiling the Potential: A Comprehensive Review of Artificial Intelligence Applications in Ophthalmology and Future Prospects

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in the field of ophthalmology. This comprehensive review examines the current applications of AI in ophthalmology, highlighting its significant contributions to diagnostic accuracy, treatment efficacy, and patient care. AI technologies, such as deep learning algorithms, have demonstrated exceptional performance in the early detection and diagnosis of various eye conditions, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Additionally, AI has enhanced the analysis of ophthalmic imaging techniques like optical coherence tomography (OCT) and fundus photography, facilitating more precise disease monitoring and management. The review also explores AI's role in surgical assistance, predictive analytics, and personalized treatment plans, showcasing its potential to revolutionize clinical practice and improve patient outcomes. Despite these advancements, challenges such as data privacy, regulatory hurdles, and ethical considerations remain. The review underscores the need for continued research and collaboration among clinicians, researchers, technology developers, and policymakers to address these challenges and fully harness the potential of AI in improving eye health worldwide. By integrating AI with teleophthalmology and developing AI-driven wearable devices, the future of ophthalmic care promises enhanced accessibility, efficiency, and efficacy, ultimately reducing the global burden of visual impairment and blindness.

Autonomous Artificial Intelligence Increases Access and Health Equity in Underserved Populations with Diabetes

Diabetic eye disease (DED) is a leading cause of blindness in the world. Early detection and treatment of DED have been shown to be both sight-saving and cost-effective. As such, annual testing for DED is recommended for adults with diabetes and is a Healthcare Effectiveness Data and Information Set (HEDIS) measure. However, adherence to this guideline has historically been low, and access to this sight-saving intervention has particularly been limited for specific populations, such as Black or African American patients. In 2018, the US Food and Drug Agency (FDA) De Novo cleared autonomous artificial intelligence (AI) for diagnosing DED in a primary care setting. In 2020, Johns Hopkins Medicine (JHM), an integrated healthcare system with over 30 primary care sites, began deploying autonomous AI for DED testing in some of its primary care clinics. In this retrospective study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and whether this was different for specific populations. JHM primary care sites were categorized as "non-AI" sites (sites with no autonomous AI deployment over the study period and where patients are referred to eyecare for DED testing) or "AI-switched" sites (sites that did not have autonomous AI testing in 2019 but did by 2021). We conducted a difference-in-difference analysis using a logistic regression model to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes managed within our health system (17,674 patients for the 2019 cohort and 17,590 patients for the 2021 cohort) and has three major findings. First, after controlling for a wide range of potential confounders, our regression analysis demonstrated that the odds ratio of adherence at AI-switched sites was 36% higher than that of non-AI sites, suggesting that there was a higher increase in DED testing between 2019 and 2021 at AI-switched sites than at non-AI sites. Second, our data suggested autonomous AI improved access for historically disadvantaged populations. The adherence rate for Black/African Americans increased by 11.9% within AI-switched sites whereas it decreased by 1.2% within non-AI sites over the same time frame. Third, the data suggest that autonomous AI improved health equity by closing care gaps. For example, in 2019, a large adherence rate gap existed between Asian Americans and Black/African Americans (61.1% vs. 45.5%). This 15.6% gap shrank to 3.5% by 2021. In summary, our real-world deployment results in a large integrated healthcare system suggest that autonomous AI improves adherence to a HEDIS measure, patient access, and health equity for patients with diabetes - particularly in historically disadvantaged patient groups. While our findings are encouraging, they will need to be replicated and validated in a prospective manner across more diverse settings.