Intervention design for artificial intelligence-enabled macular service implementation: a primary qualitative study.

Background: Neovascular age-related macular degeneration (nAMD) is one of the largest single-disease contributors to hospital outpatient appointments. Challenges in finding the clinical capacity to meet this demand can lead to sight-threatening delays in the macular services that provide treatment. Clinical artificial intelligence (AI) technologies pose one opportunity to rebalance demand and capacity in macular services.

Clinical Evaluation of Artificial Intelligence-Enabled Interventions.

Artificial intelligence (AI) health technologies are increasingly available for use in real-world care. This emerging opportunity is accompanied by a need for decision makers and practitioners across healthcare systems to evaluate the safety and effectiveness of these interventions against the needs of their own setting. To meet this need, high-quality evidence regarding AI-enabled interventions must be made available, and decision makers in varying roles and settings must be empowered to evaluate that evidence within the context in which they work.

AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review.

Background: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals.

Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review.

Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation.

AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices.

Background: Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses.

Background: Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy.

Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study.

Background: Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes.

Objective: This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care.

Methods: A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS.

Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research.

Introduction: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation.

Commentary: Patient Perspectives on Artificial Intelligence; What have We Learned and How Should We Move Forward?

Artificial intelligence (AI) in healthcare has now begun to make its contributions to real-world patient care with varying degrees of both public and clinical acceptability around it. The heavy investment from governments, industry and academia needed to reach this point has helped to surface different perspectives on AI. As clinical AI applications become a reality, however, there is an increasing need to harness and integrate patient perspectives, which address the distinct needs of different populations, healthcare systems and clinical problems more closely.

Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study.

Introduction: Neovascular age-related macular degeneration (nAMD) management is one of the largest single-disease contributors to hospital outpatient appointments. Partial automation of nAMD treatment decisions could reduce demands on clinician time. Established artificial intelligence (AI)-enabled retinal imaging analysis tools, could be applied to this use-case, but are not yet validated for it.

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence.

Background: The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this.

Objective: In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target.

Impact of the COVID-19 pandemic on the research activities of UK ophthalmologists.

Background: The COVID-19 pandemic has impacted negatively on many areas of biomedical research and there is concern that academic recovery will take several years. This survey aimed to define the impact of the COVID-19 pandemic on UK ophthalmologists' research activities and understand the implications for recovery.

Methods: An online survey comprising multiple choice and free-text questions was designed, piloted and then distributed to Royal College of Ophthalmologists (RCOphth) members in January 2021.

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence.

Background: Quantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the "AI chasm," which continues to delay patient benefit.

Evaluation of a low-resource screening strategy for ophthalmic pathologies and associated neurological morbidity in an older Tanzanian HIV-positive population.

Globally, 43 million people are living with HIV, 90% in developing countries. Increasing life expectancy with combination antiretroviral therapy (cART) results in chronic complications, including HIV-associated neurocognitive disorders (HAND) and eye diseases. HAND screening is currently challenging.

An observational clinical study of the influence of phacoemulsification on choroidal neovascular membrane activity in age related macular degeneration.

Background: Thousands of phacoemulsification surgeries are performed on eyes with age-related macular degeneration (AMD) complicated by choroidal neovascular membrane (CNV) in the United Kingdom each year. As populations age this number is expected to rise. Controversy over phacoemulsification's influence on CNV activity limits the information which clinicians and these patients use to decide on surgery.

Real-World Visual and Neovascularisation Outcomes from anti-VEGF in Central Retinal Vein Occlusion.

Purpose: Central retinal vein occlusion (CRVO) can be complicated by macular oedema, requiring intravitreal injection (IVI) of anti-vascular endothelial growth factor (VEGF). CRVO can cause neovascularisation, potentially causing persistent pain if not identified early. Whilst clinical trial data describe visual and anti-neovascular benefit from anti-VEGF there are limited real-world data.

Ranibizumab and aflibercept intravitreal injection for treatment naïve and refractory macular oedema in branch retinal vein occlusion.

Background: Branch retinal vein occlusion complicated by macular oedema is a common disease treated with intravitreal injection of anti-vascular endothelial growth factor. Controversy exists surrounding anti-vascular endothelial growth factor selection for both treatment naïve and refractory cases.

Methods: A retrospective electronic medical record review at a single UK centre generated a cohort of 259 treatment naïve eyes from 258 patients receiving ranibizumab, aflibercept or a combination ( = 83, 97 and 79, respectively) from 2013 to 2018 with ⩾6 months follow-up.

A non-randomised trial of video and written educational adjuncts in undergraduate ophthalmology.

Background: Provision of relevant pre-learning materials has been shown to increase student engagement and improve outcomes in medical education. This non-randomised study attempts to quantify the educational gains, and relative efficacy of video and written pre-learning materials, in ophthalmology undergraduate teaching.

Methods: Ninety-eight final year medical students were contacted prior to their three-day ophthalmology placements at a British tertiary ophthalmology unit.

10-year trends in English primary care glaucoma prescribing.

Background: In 2018 NHS prescriptions in England cost £8.83 billion. Within ophthalmic prescribing, glaucoma is the most costly indication.