Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability.

A look at the emerging trends of large language models in ophthalmology.

Purpose Of Review: As the surge in large language models (LLMs) and generative artificial intelligence (AI) applications in ophthalmology continue to expand, this review seeks to update physicians of the current progress, to catalyze further work to harness its capabilities to enhance healthcare delivery in ophthalmology.

Recent Findings: Generative AI applications have shown promising performance in Ophthalmology. Beyond native LLMs and question-answering based tasks, there has been increasing work in employing novel LLM techniques and exploring wider use case applications.

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings.

Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists.

Cybersecurity in the generative artificial intelligence era.

Generative Artificial Intelligence (GenAI) are algorithms capable of generating original content. The ability of GenAI to learn and generate novel outputs alike human cognition has taken the world by storm and ushered in a new era. In this review, we explore the role of GenAI in healthcare, including clinical, operational, and research applications, and delve into the cybersecurity risks of this technology.

Latest developments of generative artificial intelligence and applications in ophthalmology.

The emergence of generative artificial intelligence (AI) has revolutionized various fields. In ophthalmology, generative AI has the potential to enhance efficiency, accuracy, personalization and innovation in clinical practice and medical research, through processing data, streamlining medical documentation, facilitating patient-doctor communication, aiding in clinical decision-making, and simulating clinical trials. This review focuses on the development and integration of generative AI models into clinical workflows and scientific research of ophthalmology.

Comparison of the Quality of Discharge Letters Written by Large Language Models and Junior Clinicians: Single-Blinded Study.

Background: Discharge letters are a critical component in the continuity of care between specialists and primary care providers. However, these letters are time-consuming to write, underprioritized in comparison to direct clinical care, and are often tasked to junior doctors. Prior studies assessing the quality of discharge summaries written for inpatient hospital admissions show inadequacies in many domains.

Large language models in health care: Development, applications, and challenges.

Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre-training.

Clinical performance of automated machine learning: A systematic review.

Introduction: Automated machine learning (autoML) removes technical and technological barriers to building artificial intelligence models. We aimed to summarise the clinical applications of autoML, assess the capabilities of utilised platforms, evaluate the quality of the evidence trialling autoML, and gauge the performance of autoML platforms relative to conventionally developed models, as well as each other.

Method: This review adhered to a prospectively registered protocol (PROSPERO identifier CRD42022344427).

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.

Background: Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges.

Main Text: This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management.

Leveraging a clinical emergency department dataset to estimate two-dose COVID-19 vaccine effectiveness and duration of protection in Canada.

Background: During the COVID-19 pandemic, clinical care shifted toward virtual and Emergency Department care. We explored the feasibility of mRNA vaccine effectiveness (VE) estimation against SARS-CoV-2-related Emergency Department visits and hospitalizations using prospectively collected Emergency Department data.

Methods: We estimated two-dose VE using a test-negative design and data from 10 participating sites of the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN).

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.

A comparative study of an on premise AutoML solution for medical image classification.

Automated machine learning (AutoML) allows for the simplified application of machine learning to real-world problems, by the implicit handling of necessary steps such as data pre-processing, feature engineering, model selection and hyperparameter optimization. This has encouraged its use in medical applications such as imaging. However, the impact of common parameter choices such as the number of trials allowed, and the resolution of the input images, has not been comprehensively explored in existing literature.

Optical coherence tomography choroidal enhancement using generative deep learning.

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China.

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals.

Ethical and regulatory challenges of large language models in medicine.

With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs.

Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study.

Large language models (LLMs) underlie remarkable recent advanced in natural language processing, and they are beginning to be applied in clinical contexts. We aimed to evaluate the clinical potential of state-of-the-art LLMs in ophthalmology using a more robust benchmark than raw examination scores. We trialled GPT-3.

Replacing the postoperative week 1 visit after routine phacoemulsification with a telephone consult.

Objective: To assess the safety of replacing the postoperative week 1 (POW1) clinic visit with a nurse-conducted telephone call.

Design: Retrospective observational study that included cases from January 2019 to June 2021.

Participants: Patients who had undergone uncomplicated phacoemulsification surgery with an unremarkable postoperative day 1 (POD1) examination.

Medical education and physician training in the era of artificial intelligence.

With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition.