Corrigendum: Effectiveness of myopia control interventions: a systematic review of 12 randomized control trials published between 2019 and 2021.

[This corrects the article DOI: 10.3389/fpubh.2023.1125000.].

Neurosensory retina detachment combined with retinal pigment epithelium separation from the choroid in the course of dry macular degeneration - A case report.

of advanced diagnostic methods shed the light on the variable course of age-related macular degeneration (AMD). Despite establishing AMD classifications used in clinical practice, there are still forms of AMD that do not fit into these systems. The case report presents a rare evolution of non-neovascular form of AMD presenting at baseline as large soft drusen. Within the 5 years of observation one eye with such form of AMD transformed to retinal pigment epithelial detachment and subsequently simultaneous separation of the neurosensory retina and the choroid from the RPE. As a result, on the spectral domain optical coherence tomography scan, the case presented with lone line of the RPE neighbored by subretinal fluid from the inner side and choroidal excavation from the outside. Macular neovascularization was excluded at each timepoint of the follow-up. During 2.5 years of observation post the onset of RPE separation, the case remained stable with maintained visual acuity at 0.25 Snellen and lack of progression to wet form of AMD. Further observation is needed to fully assess the eye's potential for visual preservation in the long term.

Analysis of international publication trends in artificial intelligence in skin cancer.

Bibliometric methods were used to analyze publications on artificial intelligence in skin cancer from 2010 to 2022, aiming to explore current publication trends and future directions. A comprehensive search using four terms - "artificial intelligence," "machine learning," "deep learning," and "skin cancer" was performed in the Web of Science database for original English-language publications on artificial intelligence in skin cancer from 2010 to 2022. We visually analyzed publication, citation, and coupling information, focusing on authors, countries and regions, publishing journals, institutions, and core keywords. The analysis of 989 publications revealed a consistent year-on-year increase in publications from 2010 to 2022 (0.51% vs. 33.57%). The USA, India, and China emerged as the leading contributors. IEEE Access was identified as the most prolific journal in this area. Key journals and influential authors were highlighted. Examination of the top 10 most cited publications highlights the significant potential of AI in oncology. Co-citation network analysis identified four primary categories of classical literature on AI in skin tumors. Keyword analysis indicated that "melanoma," "classification," and "deep learning" were the most prevalent keywords, suggesting that deep learning for melanoma diagnosis and grading is the current research focus. The term "pigmented skin lesions" showed the strongest burst and longest duration, while "texture" was the latest emerging keyword. AI represents a rapidly growing area of research in skin cancer with the potential to significantly improve skin cancer management. Future research is likely to focus on machine learning and deep learning technologies for screening and diagnostic purposes.

The AI revolution in glaucoma: Bridging challenges with opportunities.

Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.

Utilizing Large Language Models in Ophthalmology: The Current Landscape and Challenges.

A large language model (LLM) is an artificial intelligence (AI) model that uses natural language processing (NLP) to understand, interpret, and generate human-like language responses from unstructured text input. Its real-time response capabilities and eloquent dialogue enhance the interactive user experience in human-AI communication like never before. By gathering several sources on the internet, LLM chatbots can interact and respond to a wide range of queries, including problem solving, text summarization, and creating informative notes. Since ophthalmology is one of the medical fields integrating image analysis, telemedicine, AI, and other technologies, LLMs are likely to play an important role in eye care in the near future. This review summarizes the performance and potential applicability of LLMs in ophthalmology according to currently available publications.

Holy Medicine: Patron Saints of Wounds Due to Animal Bites.

The cult of saints in Western Europe developed during the late period of antiquity and the early Middle Ages. Their importance to European society was undeniable; holy medicine was the only hope for people, because there were no doctors. The number of saints had increased over the years, and people sought medical help from them through prayer and other religious practices. Some of the saints became "specialized" in treating various wounds and dermatologic diseases. During our research, we tried to determine whether the cult of saints led to the develop-ment of hospitals that treated skin diseases, as discovered in the Hospital Brother of Saint Anthony. A large number of saints who were patrons of wounds and skin diseases were collected in three studies. In the first report, we presented a great number of saints who were patrons to treat animal bites. The second report presented patron saints of wounds, ulcers, burns, and frostbites; and the third report decsribed saints who treated contagious diseases (such as ergotism, leprosy, and scabies). The phenomenon of holy medicine is part of the history of dermatology and is important due to "specializations," which refer to an understanding of skin diseases and the methods of treating various wounds and dermatologic diseases.

Global research of artificial intelligence in eyelid diseases: A bibliometric analysis.

Purpose: To generate an overview of global research on artificial intelligence (AI) in eyelid diseases using a bibliometric approach. Methods: All publications related to AI in eyelid diseases from 1900 to 2023 were retrieved from the Web of Science (WoS) Core Collection database. After manual screening, 98 publications published between 2000 and 2023 were finally included. We analyzed the annual trend of publication and citation count, productivity and co-authorship of countries/territories and institutions, research domain, source journal, co-occurrence and evolution of the keywords and co-citation and clustering of the references, using the analytic tool of the WoS, VOSviewer, Wordcloud Python package and CiteSpace. Results: By analyzing a total of 98 relevant publications, we detected that this field had continuously developed over the past two decades and had entered a phase of rapid development in the last three years. Among these countries/territories and institutions contributing to this field, China was the most productive country and had the most institutions with high productivity, while USA was the most active in collaborating with others. The most popular research domains was Ophthalmology and the most productive journals were Ocular Surface. The co-occurrence network of keywords could be classified into 3 clusters respectively concerned about blepharoptosis, meibomian gland dysfunction and blepharospasm. The evolution of research hotspots is from clinical features to clinical scenarios and from image processing to deep learning. In the clustering analysis of co-cited reference network, cluster "0# deep learning" was the largest and latest, and cluster "#5 meibomian glands visibility assessment" existed for the longest time. Conclusions: Although the research of AI in eyelid diseases has rapidly developed in the last three years, there are still gaps in this area. Our findings provide researchers with a better understanding of the development of the field and a reference for future research directions.

Lower indoor spatial frequency increases the risk of myopia in children.

BACKGROUND/AIMS: Animal models have shown that the absence of high-frequency visual information can precipitate the onset of myopia, but this relationship remains unclear in humans. This study aims to explore the association between the spatial frequency content of the visual environment and myopia in children. METHODS: Images from the rooms of children and their frequently visited outdoor areas were taken by their parents and collected by the researcher through questionnaires. The spatial frequency was quantified using Matlab. Cycloplegic refraction was used to measure the spherical equivalent (SE), and IOL Master was used to measure axial length (AL) and corneal radius (CR). AL/CR ratio was calculated. RESULTS: The study included 566 children with an average age of (8.04±1.47) years, of which 270 were girls (47.7%), and the average SE was (0.70±1.21) D. Image analysis revealed that indoor spatial frequency slope was lower than that of the outdoor environment (-1.43±0.18 vs -1.11±0.23, p<0.001). There were 79 myopic individuals (14.0%). Images from indoor content of myopic children had a lower spatial frequency slope than non-myopic children (-1.47±0.16 vs 1.43±0.18, p=0.03) while there was no significant difference in outdoor spatial frequency slope. Regression analysis indicated that the indoor spatial frequency slope was positively associated with SE value (ß=0.60, p=0.02) and inversely related to myopia (OR=0.24, p<0.05). CONCLUSION: The spatial frequency of the outdoor environment is significantly higher than that of the indoor environment. Indoor spatial frequency is related to children's refractive status, with lower indoor spatial frequency being associated with a higher degree of myopia.

Intraocular Lens Power Calculation Formulas in Children-A Systematic Review.

Objectives: The selection of an appropriate formula for intraocular lens power calculation is crucial in phacoemulsification, particularly in pediatric patients. The most commonly used formulas are described and their accuracy evaluated in this study. Methods: This review includes papers evaluating the accuracy of intraocular lens power calculation formulas for children's eyes published from 2019-2024. The articles were identified by a literature search of medical and other databases (Pubmed/MEDLINE, Crossref, Google Scholar) using the combination of the following key words: "IOL power calculation formula", "pediatric cataract", "congenital cataract", "pediatric intraocular lens implantation", "lens power estimation", "IOL power selection", "phacoemulsification", "Hoffer Q", "Holladay 1", "SRK/T", "Barrett Universal II", "Hill-RBF", and "Kane". A total of 14 of the most recent peer-reviewed papers in English with the maximum sample sizes and the greatest number of compared formulas were considered. Results: The outcomes of mean absolute error and percentage of predictions within ±0.5 D and ±1.0 D were used to assess the accuracy of the formulas. In terms of MAE, Hoffer Q yielded the best result most often, just ahead of SRK/T and Barrett Universal II, which, together with Holladay 1, most often yielded the second-best outcomes. Considering patients with PE within ±1.0 D, Barrett Universal II most often gave the best results and Holladay 1 most often gave the second-best. Conclusions: Barrett Universal II seems to be the most accurate formula for intraocular lens calculation for children's eyes. Very good postoperative outcomes can also be achieved using the Holladay 1 formula. However, there is still no agreement in terms of formula choice.

170th anniversary of Graefe's Archive for Clinical and Experimental Ophthalmology.

The year 2024 marks the 170th anniversary of the journal Graefe's Archive for Clinical and Experimental Ophthalmology. The journal is the first printed journal specializing in ophthalmology, founded by Albrecht von Graefe (1828-1870) in 1854. The reason behind creating the journal was to publish useful clinical information and develop discussion and debate among ophthalmologists and vision scientists. Thanks to its diligence and appropriate selection of published content, the journal has become widely read not only among the German scientific community, but also internationally. The aim of this review article is to present the activities of Graefe's Archive for Clinical and Experimental Ophthalmology during the past 170 years. KEY MESSAGES : What is known Graefe's Archive for Clinical and Experimental Ophthalmology is the oldest worldwide ophthalmology journal that in 2024 celebrates its 170th anniversary. The journal was founded by Albrecht von Graefe, and after his death continued by other giants in ophthalmology, including Ferdinand von Arlt and Franciscus Cornelius Donders. WHAT IS NEW : There were mostly male editors-in-chief, with Antonia Joussen as the first female editor-in-chief in the long journal's history. This article presents for the first time the complete list of all editors-in-chief in the 170 year long history of the journal.

Automated interpretation of retinal vein occlusion based on fundus fluorescein angiography images using deep learning: A retrospective, multi-center study.

Purpose: Fundus fluorescein angiography (FFA) is the gold standard for retinal vein occlusion (RVO) diagnosis. This study aims to develop a deep learning-based system to diagnose and classify RVO using FFA images, addressing the challenges of time-consuming and variable interpretations by ophthalmologists. Methods: 4028 FFA images of 467 eyes from 463 patients were collected and annotated. Three convolutional neural networks (CNN) models (ResNet50, VGG19, InceptionV3) were trained to generate the label of image quality, eye, location, phase, lesions, diagnosis, and macular involvement. The performance of the models was evaluated by accuracy, precision, recall, F-1 score, the area under the curve, confusion matrix, human-machine comparison, and Clinical validation on three external data sets. Results: The InceptionV3 model outperformed ResNet50 and VGG19 in labeling and interpreting FFA images for RVO diagnosis, achieving 77.63%-96.45% accuracy for basic information labels and 81.72%-96.45% for RVO-relevant labels. The comparison between the best CNN and ophthalmologists showed up to 19% accuracy improvement with the inceptionV3. Conclusion: This study developed a deep learning model capable of automatically multi-label and multi-classification of FFA images for RVO diagnosis. The proposed system is anticipated to serve as a new tool for diagnosing RVO in places short of medical resources.

Myopia Control in Caucasian Children with 0.01% Atropine Eye Drops: 1-Year Follow-Up Study.

Background and Objectives: Myopia is the most widespread ocular disorder globally and its prevalence has been increasing over the past decades. Atropine eye drops stand out as the only pharmacological intervention used in clinical practice to control myopia progression. The aim of this study was to explore the effect of 0.01% atropine eye drops on myopia progression. Patients and Methods: Healthy children aged 6-12 years with cycloplegic spherical equivalent (SE) from -0.5 D to -5.0 D and astigmatism ≤1.5 D were included. Myopia progression was assessed by changes in SE and axial length (AL) over 1 year and SE changes 1 year before the study enrollment and during the 1-year follow-up. Adverse events were evaluated based on complaints reported by either parents or the children themselves during follow-up visits. Results: The analysis involved 55 patients in the 0.01% atropine eye drops group and 66 in the control group. After the 1-year follow-up, the change in SE was -0.50 (-2.25-0.50) D in the control group compared to -0.50 (-1.50-0.50) D in the 0.01% atropine group (p = 0.935); AL change was 0.31 (0.18) mm in the control group and 0.29 (0.18) mm in the 0.01% atropine group (p = 0.480). The change in SE was -0.68 (-2.0--0.25) D/year before the study and remained similar -0.50 (-2.25-0.25) D over the 1-year follow-up in the control group (p = 0.111); SE change was reduced from -1.01 (-2.0--0.25) D/year before the study to -0.50 (-1.5-0.5) D over the 1-year follow-up in the 0.01% atropine group (p < 0.001). In the 0.01% atropine group, ten (16.4%) children experienced mild adverse events, including blurred near vision, ocular discomfort, photophobia, dry eyes, and anisocoria. Conclusions: Compared to the control group, the administration of 0.01% atropine eye drops demonstrated no significant effect on changes in SE and AL over a 1-year follow-up. However, children in the 0.01% atropine group initially experienced higher myopia progression, which decreased with treatment over the course of 1 year. Future studies should explore the long-term effects, rebound effects, potential genetic associations, and efficacy of higher doses of atropine in managing myopia progression.

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.

We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.

Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review.

Background: The convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care, offering unprecedented opportunities for diagnosis, monitoring, and management of ocular conditions. Nevertheless, there is a lack of systematic studies on discussing the integration of smartphone and AI in this field. Main text: This review includes 52 studies, and explores the integration of smartphones and AI in ophthalmology, delineating its collective impact on screening methodologies, disease detection, telemedicine initiatives, and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases, glaucoma, cataract, visual impairment in children and ocular surface diseases. Moreover, the utilization of smartphone-based imaging modalities, coupled with AI algorithms, is able to provide timely, efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring, remote patient monitoring and enhancing accessibility to eye care services, particularly in underserved regions. Challenges involving data privacy, algorithm validation, regulatory frameworks and issues of trust are still need to be addressed. Furthermore, evaluation on real-world implementation is imperative as well, and real-world prospective studies are currently lacking. Conclusions: Smartphone ocular imaging merged with AI enables earlier, precise diagnoses, personalized treatments, and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations, promising a potential revolution in care access and global eye health equity.

Efficacy and Safety of 0.03% Atropine Eye Drops in Controlling Myopia Progression: A One-Year Prospective Clinical Study.

Objective: To investigate the efficacy and safety of one-year treatment with 0.03% atropine eye drops for slowing myopia progression among children aged 6-12 years. Methods: Healthy Caucasian children aged 6-12 years with cycloplegic spherical equivalent (SE) from -1.0 D to -5.0 D and astigmatism and anisometropia ≤1.5 D were included. Changes in mean axial length (AL) and objective SE as well as changes in intraocular pressure (IOP), central corneal thickness (CCT), anterior chamber depth (ACD) and lens thickness (LT) were assessed in the 0.03% atropine eye drops group and the control group from baseline through the 1-year follow-up. The proportion of participants showing myopia progression of <0.5 D from baseline in each group and any potential side effects in 0.03% atropine group were evaluated. Results: The study involved 31 patients in the 0.03% atropine eye drops group and 41 in the control group. Administration of 0.03% atropine for 1 year resulted in a mean change in SE of -0.34 (0.44) D/year, significantly lower than the -0.60 (0.50) D/year observed in the control group (p = 0.024). The change in AL was 0.19 (0.17) mm in the 0.03% atropine group, compared to 0.31 (0.20) mm in the control group (p = 0.015). There were no significant differences in changes of IOP, CCT and LT between the groups (all p ≥ 0.05). The 0.03% atropine group had a significantly greater increase in ACD compared to the control group (p = 0.015). In total, 64.5% of patients in the 0.03% atropine group showed progression <0.5 D/year, in contrast to 39.0% in the control group (p = 0.032). Adverse events were reported in 13 (35.0%) out of 37 patients in the treatment group, leading to discontinuation of the eye drops in six (16.0%) cases. None of the adverse events were severe. Conclusions: Despite a higher incidence of adverse events, 0.03% atropine eye drops effectively slowed the progression of myopia over 1-year.

Association between ambient air pollution and dry eye symptoms among Chinese individuals during the COVID-19 pandemic: A national-based study.

PURPOSE: To examine the association between ambient air pollution and dry eye symptoms (DES) during the COVID-19 pandemic and explore whether air pollution had increased the risk of DES to a greater extent than other risk factors. METHODS: A nationwide cross-sectional survey was conducted from June 20, 2022 to August 31, 2022. The Ocular Surface Disease Index-6 (OSDI-6) questionnaire was used to assess the presence of DES. Logistic regression models were employed to analyze the associations between DES and air pollution variables, including air quality index (AQI), fine particulate matter (PM2.5), PM10, sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3) and residing near industrial zones. We explored the interactions of air pollutants and other risk factors in the additive models by calculating the synergy index (SI). Standardized regression coefficients were calculated to compare the relative importance of risk factors for DES. RESULTS: A total of 21,909 participants were included in the analysis. Residing near industrial zones was significantly correlated with a higher risk of DES (Odds ratio (OR): 1.57, 95 % confidence interval (CI): 1.38-1.79). No significant associations were found between DES and air pollutants except SO2 (OR: 1.05, 95 % CI: 1.02-1.09, per standard deviation increment in SO2 concentration). The restricted cubic spline analyses revealed a linear concentration-response relationship between SO2 and DES. The interaction analyses suggested synergetic interactions of SO2 with depression and problematic internet use. Among the risk factors, depression, anxiety and problematic Internet use contributed more to the increased risk of DES. CONCLUSION: The association between ambient air pollutants and DES may have been mitigated during the pandemic due to increased time spent indoors. Despite this, our findings support the deleterious health impact of air pollutants. Future urban planning should plan industrial zones further away from residential areas.

Myopia Control: Are We Ready for an Evidence Based Approach?

INTRODUCTION: Myopia and its vision-threatening complications present a significant public health problem. This review aims to provide an updated overview of the multitude of known and emerging interventions to control myopia, including their potential effect, safety, and costs. METHODS: A systematic literature search of three databases was conducted. Interventions were grouped into four categories: environmental/behavioral (outdoor time, near work), pharmacological (e.g., atropine), optical interventions (spectacles and contact lenses), and novel approaches such as red-light (RLRL) therapies. Review articles and original articles on randomized controlled trials (RCT) were selected. RESULTS: From the initial 3224 retrieved records, 18 reviews and 41 original articles reporting results from RCTs were included. While there is more evidence supporting the efficacy of low-dose atropine and certain myopia-controlling contact lenses in slowing myopia progression, the evidence about the efficacy of the newer interventions, such as spectacle lenses (e.g., defocus incorporated multiple segments and highly aspheric lenslets) is more limited. Behavioral interventions, i.e., increased outdoor time, seem effective for preventing the onset of myopia if implemented successfully in schools and homes. While environmental interventions and spectacles are regarded as generally safe, pharmacological interventions, contact lenses, and RLRL may be associated with adverse effects. All interventions, except for behavioral change, are tied to moderate to high expenditures. CONCLUSION: Our review suggests that myopia control interventions are recommended and prescribed on the basis of accessibility and clinical practice patterns, which vary widely around the world. Clinical trials indicate short- to medium-term efficacy in reducing myopia progression for various interventions, but none have demonstrated long-term effectiveness in preventing high myopia and potential complications in adulthood. There is an unmet need for a unified consensus for strategies that balance risk and effectiveness for these methods for personalized myopia management.

Accuracy of 20 Intraocular Lens Power Calculation Formulas in Medium-Long Eyes.

INTRODUCTION: The aim of this work is to compare 20 intraocular lens (IOL) power calculation formulas in medium-long eyes (24.50-25.99 mm) in terms of root mean square absolute error (RMSAE), median absolute error (MedAE), and percentage of eyes with prediction error (PE) within ± 0.50 D. METHODS: The data of patients who underwent uneventful phacoemulsification between January 2017 and September 2023 were reviewed. Pre-surgery IOL power was calculated using Holladay1, SRK/T, Hoffer Q, Holladay 2, and Haigis. Three months after phacoemulsification, refraction was measured. Post-surgery IOL power calculations were performed utilizing the following formulas: Barrett Universal II, Kane, K6, Olsen (OLCR), Olsen (standalone), PEARL-DGS, Ladas Super Formula AI (LSF AI), T2, EVO, VRF, Hoffer QST, Castrop, VRF-G, Karmona, and Naeser 2. RMSAE, MedAE, and percentage of eyes with PE within ± 0.25 D, ± 0.50 D, ± 0.75 D and ± 1.00 were calculated. RESULTS: One hundred twenty-four eyes with axial length ranges between 24.52 and 25.97 mm were studied. The SRK/T formula yielded the lowest RMSAE (0.206) just before Holladay 1 (0.260) and T2 (0.261). In terms of MedAE, the best outcome was obtained by SRK/T (0.12) followed by Barrett Universal II (0.15) and LSF AI (0.15). The highest percentage of eyes with prediction error within ± 0.50 D was achieved by SRK/T, T2, and Holladay 1 (97.58, 93.55, and 93.55%, respectively). CONCLUSIONS: Third-generation formulas (SRK/T, Holladay 1) provided highly accurate outcomes in medium-long eyes and still can be wildly used to calculate IOL power.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists.

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.