The world of optometry presents a diverse tapestry of practices, shaped by regional cultures, socioeconomic conditions, and healthcare systems. This chapter delves into the intricacies of optometry practices across different global regions, aiming to illuminate the unique ways in which societies address visual health and refractive needs.

Artificial intelligence (AI) has emerged as a transformative force in the field of optometry, revolutionizing the way practitioners diagnose, treat, and manage ocular conditions. At the heart of AI lies machine learning (ML) and its subset, deep learning (DL), which have demonstrated remarkable capabilities in analyzing vast amounts of data to extract meaningful patterns and insights. In the realm of optometry, the advent of AI-driven technologies has been particularly notable in leveraging medical imaging data, such as retinal images and optical coherence tomography (OCT) scans. These imaging modalities provide rich information about ocular health and pathology, making them ideal inputs for AI algorithms. Moreover, the availability of large-scale datasets, such as those from initiatives like the UK Biobank, combined with advancements in computing power and chip development, has significantly lowered the barrier for developing sophisticated AI models. These datasets serve as invaluable resources for training and validating AI algorithms, enabling researchers and developers to create robust and accurate models for various optometric applications. Briefly, the development of AI models typically involves several stages, including data preprocessing, model selection, training, tuning, and testing. This iterative process aims to optimize the model’s performance and generalizability across diverse datasets. Evaluating the performance of AI models then requires robust metrics that assess their accuracy, sensitivity, specificity, and predictive value. Common metrics include area under the receiver operating characteristic curve (AUC-ROC), precision, recall, and F1-score. This chapter aims to provide a focused exploration of the impact of AI on optometry, with a particular emphasis on FDA-approved AI products, the transformative potential of early detection and screening, and the evolving scope of optometric practice in the AI era. While acknowledging the vast landscape of AI applications in optometry, this chapter prioritizes key themes and developments relevant to practitioners and stakeholders. It serves as a concise overview, offering insights into the current state of AI integration in optometry and outlining future directions for research and practice.

Artificial intelligence has provided the field of ophthalmology with new, fast, accurate and automated means for diagnosing and sometimes treating ocular diseases, opening new avenues for modern eye care. Some subspecialties of ophthalmology have benefitted from artificial intelligence to a larger extent than others. For instance, integrative medical subspecialties like neuro-ophthalmology have, until recently, been deprived from major advances in the artificial intelligence-driven detection, let alone treatment, of sight and life-threatening neuro-ophthalmological conditions. In this chapter, we summarize the most prominent investigations utilizing artificial intelligence to detect neuro-ophthalmic conditions affecting the optic nerve and eye movements.

Data protection and privacy, algorithmic fairness, informed consent and cybersecurity and an optimal liability framework are key issues that need to be taken into account for an ethically AI-based healthcare system. This chapter briefly apprises the readers about the ethical concerns with big data and AI in healthcare.

In this digital age, artificial intelligence and its applications have become ubiquitous in the world around us. The practice of modern medicine driven by scientific data and evidence is an obvious target for these applications. In this chapter, we explore the history of artificial intelligence, where we are now, how to interpret current evidence generated by algorithms and how to balance the hype and potential that comes with introduction of a new standard of care.

The use of artificial intelligence for cataract detection, grading and management has been explored in recent years. In this chapter, we review the previous works in this regard, the challenges faced and the potential real-world deployment strategies. Owing to the magnitude of the problem, developments in this field are going to have significant impact on public health policy and healthcare delivery models.

Cranial nerve palsies are one of the commonest disorders presenting to the neuro-ophthalmology clinic. Although cranial nerve palsy is a diagnosis in itself—it is usually indicative of underlying disorder and needs thorough evaluation and investigation. This chapter aims to cover the relevant clinical anatomy of the three cranial nerves relevant to the ophthalmologist, highlights the etiology of the disorders affecting these nerves, and also gives relevant tips for clinical evaluation. The latest practices and relevant literature regarding the investigation and management of cranial nerve palsies are covered, including newer imaging modalities, clinical and laboratory investigations, and treatment.

This chapter outlines the various types of hereditary optic neuropathies and their genetics. Leber’s hereditary optic neuropathy (LHON) is the first described and most common mitochondrial optic neuropathy, and is discussed in detail including its genetics and pathophysiology, clinical presentation, relevant investigations relatively recent findings using newer imaging modalities such as OCT. Autosomal dominant optic atrophy (ADOA) is described, and the major differences between ADOA and LHON are discussed. Other entities such as Wolfram syndrome and syndromic optic neuropathies are also included. Recent advances and newer modalities for treatment and ongoing therapeutic clinical trials are outlined.

There are around 285 million visually impaired people in the world with 39 million blind and 256 million with low vision. The main disorders responsible for this disease burden are age-related macular degeneration (AMD), diabetic retinopathy/macular edema (DR/DME), and glaucoma. There are several effective medications that are used to treat these conditions, but it is still a challenge to provide this treatment with a sustained release profile, minimum side effects, and cost-effective rates. In this chapter we will review the ocular drug development process and highlight developments that can potentially improve the efficiency of the current ocular drug discovery industry.

Ophthalmology has been at the forefront of adopting cutting edge technology and using it to deliver effective ocular care. This chapter looks at some of the latest developments that are going to propel the standard of eye care in the coming few years. From deep learning AI/big data analysis to precise gene-based treatment strategies, we look at the future of ophthalmology that is already in the making today.

From the use of advanced computer-based imaging technology, tablet-based perimeters, novel biomarkers to genetic markers, glaucoma diagnostics is currently one of the most exciting areas to work in. This chapter gives an insight at the future of glaucoma diagnosis and how cutting-edge research is improving accuracy in detecting glaucomatous change.