Search Results (15)

Artificial intelligence (AI) is increasingly influencing healthcare administration and clinical informatics by supporting disease diagnosis, clinical decision-making, treatment personalization, drug discovery, remote monitoring, public health surveillance, and hospital operations. However, the successful adoption of AI in healthcare depends not only on algorithmic performance, but also on its safe integration into clinical information systems, organizational workflows, and governance structures. This article presents a narrative critical review of recent advances in AI-driven healthcare, with a focus on four major domains: AI-enabled disease diagnosis, treatment personalization and clinical decision support, drug discovery and biomedical knowledge generation, and healthcare administration. Evidence from radiology, pathology, ophthalmology, dermatology, and cardiology shows that AI systems can achieve strong diagnostic performance in selected settings, while applications in electronic health records, natural language processing, telemedicine, and predictive analytics are increasingly used to support healthcare delivery and operational decision-making. At the same time, important barriers continue to limit real-world implementation, including fragmented data infrastructures, limited interoperability, poor data quality, algorithmic bias, lack of explainability, privacy and cybersecurity risks, unclear accountability, and insufficient external validation. This review critically examines these challenges and proposes a governance-oriented roadmap for responsible AI integration in healthcare administration and clinical informatics. The proposed roadmap emphasizes data readiness, model validation, workflow integration, institutional accountability, post-deployment monitoring, and workforce readiness. The findings suggest that AI can contribute to more efficient, accessible, and patient-centered healthcare only when it is implemented within trustworthy medical informatics ecosystems supported by ethical governance, human oversight, and continuous evaluation. Full article

Photodynamic therapy (PDT) is a minimally invasive therapeutic modality that combines a photosensitizer, light of an appropriate wavelength, and molecular oxygen to generate cytotoxic reactive oxygen species for selective tissue destruction. Over recent decades, PDT has evolved from early porphyrin-based systems to advanced third-generation photosensitizers incorporating nanotechnology, targeting ligands, and activatable designs, significantly improving tumor selectivity, pharmacokinetics, and therapeutic efficacy. This article offers an in-depth look at the fundamental principles of PDT, including the roles of photosensitizers, light delivery systems, and oxygen dynamics, as well as the resulting biological effects such as direct tumor cell death, vascular shutdown, and immune activation. Clinical applications across oncology, dermatology, ophthalmology, and antimicrobial therapy are discussed, highlighting both established and emerging indications. Furthermore, the review critically examines recent advances in machine learning (ML) and deep learning (DL) applied to PDT, including treatment planning, dosimetry optimization, photosensitizer and nanoparticle design, real-time treatment monitoring, and outcome prediction. By integrating physics-based modeling, multimodal imaging, and artificial intelligence-driven approaches, PDT is transitioning toward adaptive, personalized photomedicine. This work outlines current challenges, future research directions, and the translational potential of AI-enabled PDT systems, emphasizing their role in improving precision, reproducibility, and clinical outcomes. Full article

Antimicrobial resistance (AMR) is a growing global health concern with profound implications for ophthalmology, where it compromises the management of ocular infections such as bacterial keratitis, conjunctivitis, endophthalmitis, and postoperative complications. Resistance in common ocular pathogens, including Staphylococcus aureus (S. aureus), Streptococcus pneumoniae (S. pneumoniae), Pseudomonas aeruginosa (P. aeruginosa), and coagulase-negative staphylococci (CoNS) emerge through genetic mutations, horizontal gene transfer, and biochemical mechanisms such as enzymatic degradation, target modification, efflux pumps, and reduced membrane permeability. Biofilm formation further complicates eradication on the ocular surface and interior. The key drivers of resistance include inappropriate or prolonged topical antibiotic use, routine prophylaxis in ocular surgery, subtherapeutic dosing, and cross-resistance with systemic antimicrobials. The rise in multidrug-resistant strains, particularly methicillin-resistant S. aureus, fluoroquinolone-resistant P. aeruginosa, and drug-resistant S. pneumoniae has been linked to delayed treatment response, increased healthcare costs, and sight-threatening outcomes. Recent advances in rapid diagnostics, molecular assays, and point-of-care testing support earlier and more precise detection of resistance, enabling timely therapeutic decisions. Promising strategies to address AMR in ophthalmology include antimicrobial stewardship, novel drug delivery platforms, and alternative approaches such as bacteriophage therapy and antimicrobial peptides. Emerging tools, including genomic surveillance, artificial intelligence (AI)-driven resistance prediction, and personalized antimicrobial regimens, further expand opportunities for innovation. Collectively, this review synthesizes current evidence on AMR in ocular disease, summarizing patterns of resistance, underlying mechanisms, and clinical consequences, while highlighting strategies for mitigation and underscoring the need for global awareness and collaboration among clinicians, researchers, and policymakers to safeguard vision. Full article

Background/Objectives: The prevailing paradigm in ophthalmic AI involves siloed, single-disease models, which fails to address the complexity of differential diagnosis in clinical practice. This study aimed to develop and validate a unified deep learning framework for the automated multi-class classification of a wide spectrum of retinal pathologies from fundus photographs, moving beyond the single-disease paradigm to create a comprehensive screening tool. Methods: A publicly available dataset was manually curated by an ophthalmologist, resulting in 1841 images across nine classes, including Diabetic Retinopathy, Glaucoma, and Healthy retinas. After extensive data augmentation to mitigate class imbalance, three pre-trained CNN architectures (ResNet-152, EfficientNetV2, and a YOLOv11-based classifier) were comparatively evaluated. The models were trained using transfer learning and their performance was assessed on an independent test set using accuracy, macro-averaged F1-score, and Area Under the Curve (AUC). Results: The YOLOv11-based classifier demonstrated superior performance over the other architectures on the validation set. On the final independent test set, it achieved a robust overall accuracy of 0.861 and a macro-averaged F1-score of 0.861. The model yielded a validation set AUC of 0.961, which was statistically superior to both ResNet-152 (p < 0.001) and EfficientNetV2 (p < 0.01) as confirmed by the DeLong test. Conclusions: A unified deep learning framework, leveraging a YOLOv11 backbone, can accurately classify nine distinct retinal conditions from a single fundus photograph. This holistic approach moves beyond the limitations of single-disease algorithms, offering considerable promise as a comprehensive AI-driven screening tool to augment clinical decision-making and enhance diagnostic efficiency in ophthalmology. Full article

Glaucoma is an eye disease caused by increased intraocular pressure (IOP) that affects the optic nerve head (ONH), leading to vision problems and irreversible blindness. Background/Objectives: Glaucoma is the second leading cause of blindness worldwide, and the number of people affected is increasing each year, with the number expected to reach 111.8 million by 2040. This escalating trend is alarming due to the lack of ophthalmology specialists relative to the population. This study proposes an explainable end-to-end pipeline for automated glaucoma diagnosis from fundus images. It also evaluates the performance of Vision Transformers (ViTs) relative to traditional CNN-based models. Methods: The proposed system uses three datasets: REFUGE, ORIGA, and G1020. It begins with YOLOv11 for object detection of the optic disc. Then, the optic disc (OD) and optic cup (OC) are segmented using U-Net with ResNet50, VGG16, and MobileNetV2 backbones, as well as MaskFormer with a Swin-Base backbone. Glaucoma is classified based on the vertical cup-to-disc ratio (vCDR). Results: MaskFormer outperforms all models in segmentation in all aspects, including IoU OD, IoU OC, DSC OD, and DSC OC, with scores of 88.29%, 91.09%, 93.83%, and 93.71%. For classification, it achieved accuracy and F1-scores of 84.03% and 84.56%. Conclusions: By relying on the interpretable features of the vCDR, the proposed framework enhances transparency and aligns well with the principles of explainable AI, thus offering a trustworthy solution for glaucoma screening. Our findings show that Vision Transformers offer a promising approach for achieving high segmentation performance with explainable, biomarker-driven diagnosis. Full article

Optical Coherence Tomography (OCT) is a relatively new medical imaging device that provides high-resolution and real-time visualization of biological tissues. Initially designed for ophthalmology, OCT is now being applied in other types of pathologies, like cancer diagnosis. This review highlights its impact on disease diagnosis, biopsy guidance, and treatment monitoring. Despite its advantages, OCT has limitations, particularly in tissue penetration and differentiating between malignant and benign lesions. To overcome these challenges, the integration of nanoparticles has emerged as a transformative approach, which significantly enhances contrast and tumor vascularization at the molecular level. Gold and superparamagnetic iron oxide nanoparticles, for instance, have demonstrated great potential in increasing OCT’s diagnostic accuracy through enhanced optical scattering and targeted biomarker detection. Beyond these innovations, integrating OCT with multimodal imaging methods, including magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound, offers a more comprehensive approach to disease assessment, particularly in oncology. Additionally, advances in artificial intelligence (AI) and biosensors have further expanded OCT’s capabilities, enabling real-time tumor characterization and optimizing surgical precision. However, despite these advancements, clinical adoption still faces several hurdles. Issues related to nanoparticle biocompatibility, regulatory approvals, and standardization need to be addressed. Moving forward, research should focus on refining nanoparticle technology, improving AI-driven image analysis, and ensuring broader accessibility to OCT-guided diagnostics. By tackling these challenges, OCT could become an essential tool in precision medicine, facilitating early disease detection, real-time monitoring, and personalized treatment for improved patient outcomes. Full article

This study presents a clinical utility-driven machine learning framework for retinal Optical Coherence Tomography classification, addressing challenges posed by manual interpretation variability and dataset heterogeneity. The methodology integrates biomimetic data partitioning, deep biomarker extraction via pretrained VGG16 networks, and automated model selection optimized for clinical decision-making. Stratified data curation preserved pathological distributions across training, validation, and testing subsets, while SMOTE optimization mitigated class imbalance. Cross-pathology testing evaluated generalizability on anatomically distinct retinal conditions excluded from training, assessing the framework’s robustness to unseen pathologies. Clinical utility metrics prioritized alignment with ophthalmological imperatives, emphasizing negative predictive value to minimize false negatives and enhance diagnostic reliability. The framework advances AI-driven Optical Coherence Tomography diagnostics by harmonizing computational performance with patient-centered outcomes, enabling standardized disease detection across diverse clinical datasets through robust feature generalization. Full article

Objectives: The integration of multimodal capabilities into GPT-4 represents a transformative leap for artificial intelligence in ophthalmology, yet its utility in scenarios requiring advanced reasoning remains underexplored. This study evaluates GPT-4’s multimodal performance on open-ended diagnostic and next-step reasoning tasks in complex ophthalmology cases, comparing it against human expertise. Methods: GPT-4 was assessed across three study arms: (1) text-based case details with figure descriptions, (2) cases with text and accompanying ophthalmic figures, and (3) cases with figures only (no figure descriptions). We compared GPT-4’s diagnostic and next-step accuracy across arms and benchmarked its performance against three board-certified ophthalmologists. Results: GPT-4 achieved 38.4% (95% CI [33.9%, 43.1%]) diagnostic accuracy and 57.8% (95% CI [52.8%, 62.2%]) next-step accuracy when prompted with figures without descriptions. Diagnostic accuracy declined significantly compared to text-only prompts (p = 0.007), though the next-step performance was similar (p = 0.140). Adding figure descriptions restored diagnostic accuracy (49.3%) to near parity with text-only prompts (p = 0.684). Using figures without descriptions, GPT-4’s diagnostic accuracy was comparable to two ophthalmologists (p = 0.30, p = 0.41) but fell short of the highest-performing ophthalmologist (p = 0.0004). For next-step accuracy, GPT-4 was similar to one ophthalmologist (p = 0.22) but underperformed relative to the other two (p = 0.0015, p = 0.0017). Conclusions: GPT-4’s diagnostic performance diminishes when relying solely on ophthalmic images without textual context, highlighting limitations in its current multimodal capabilities. Despite this, GPT-4 demonstrated comparable performance to at least one ophthalmologist on both diagnostic and next-step reasoning tasks, emphasizing its potential as an assistive tool. Future research should refine multimodal prompts and explore iterative or sequential prompting strategies to optimize AI-driven interpretation of complex ophthalmic datasets. Full article

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. Full article

In recent years, artificial intelligence has begun to play a salient role in various medical fields, including ophthalmology. This extensive review is addressed to ophthalmologists and aims to capture the current landscape and future potential of AI applications for eye health. From automated retinal screening processes and machine learning models predicting the progression of ocular conditions to AI-driven decision support systems in clinical settings, this paper provides a comprehensive overview of the clinical implications of AI in ophthalmology. The development of AI has opened new horizons for ophthalmology, offering innovative solutions to improve the accuracy and efficiency of ocular disease diagnosis and management. The importance of this paper lies in its potential to strengthen collaboration between researchers, ophthalmologists, and AI specialists, leading to transformative findings in the early identification and treatment of eye diseases. By combining AI potential with cutting-edge imaging methods, novel biomarkers, and data-driven approaches, ophthalmologists can make more informed decisions and provide personalized treatment for their patients. Furthermore, this paper emphasizes the translation of basic research outcomes into clinical applications. We do hope this comprehensive review will act as a significant resource for ophthalmologists, researchers, data scientists, healthcare professionals, and managers in the healthcare system who are interested in the application of artificial intelligence in eye health. Full article

Ophthalmic diseases such as glaucoma, age-related macular degeneration (ARMD), and optic neuritis involve complex molecular and cellular disruptions that challenge current diagnostic and therapeutic approaches. Advanced artificial intelligence (AI) and machine learning (ML) models offer a novel lens to analyze these diseases by integrating diverse datasets, identifying patterns, and enabling precision medicine strategies. Over the past decade, applications of AI in ophthalmology have expanded from imaging-based diagnostics to molecular-level modeling, bridging critical gaps in understanding disease mechanisms. This paper systematically reviews the application of AI-driven methods, including reinforcement learning (RL), graph neural networks (GNNs), Bayesian inference, and generative adversarial networks (GANs), in the context of these ophthalmic conditions. RL models simulate transcription factor dynamics in hypoxic or inflammatory environments, offering insights into disrupted molecular pathways. GNNs map intricate molecular networks within affected tissues, identifying key inflammatory or degenerative drivers. Bayesian inference provides probabilistic models for predicting disease progression and response to therapies, while GANs generate synthetic datasets to explore therapeutic interventions. By contextualizing these AI tools within the broader framework of ophthalmic disease management, this review highlights their potential to transform diagnostic precision and therapeutic outcomes. Ultimately, this work underscores the need for continued interdisciplinary collaboration to harness AI’s potential in advancing the field of ophthalmology and improving patient care. Full article

Artificial intelligence (AI) is becoming increasingly influential in ophthalmology, particularly through advancements in machine learning, deep learning, robotics, neural networks, and natural language processing (NLP). Among these, NLP-based chatbots are the most readily accessible and are driven by AI-based large language models (LLMs). These chatbots have facilitated new research avenues and have gained traction in both clinical and surgical applications in ophthalmology. They are also increasingly being utilized in studies on ophthalmology-related exams, particularly those containing multiple-choice questions (MCQs). This narrative review evaluates both the opportunities and the challenges of integrating chatbots into ophthalmology research, with separate assessments of studies involving open- and close-ended questions. While chatbots have demonstrated sufficient accuracy in handling MCQ-based studies, supporting their use in education, additional exam security measures are necessary. The research on open-ended question responses suggests that AI-based LLM chatbots could be applied across nearly all areas of ophthalmology. They have shown promise for addressing patient inquiries, offering medical advice, patient education, supporting triage, facilitating diagnosis and differential diagnosis, and aiding in surgical planning. However, the ethical implications, confidentiality concerns, physician liability, and issues surrounding patient privacy remain pressing challenges. Although AI has demonstrated significant promise in clinical patient care, it is currently most effective as a supportive tool rather than as a replacement for human physicians. Full article

Advancements in neuroimaging, particularly diffusion magnetic resonance imaging (MRI) techniques and molecular imaging with positron emission tomography (PET), have significantly enhanced the early detection of biomarkers in neurodegenerative and neuro-ophthalmic disorders. These include Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, neuromyelitis optica, and myelin oligodendrocyte glycoprotein antibody disease. This review highlights the transformative role of advanced diffusion MRI techniques—Neurite Orientation Dispersion and Density Imaging and Diffusion Kurtosis Imaging—in identifying subtle microstructural changes in the brain and visual pathways that precede clinical symptoms. When integrated with artificial intelligence (AI) algorithms, these techniques achieve unprecedented diagnostic precision, facilitating early detection of neurodegeneration and inflammation. Additionally, next-generation PET tracers targeting misfolded proteins, such as tau and alpha-synuclein, along with inflammatory markers, enhance the visualization and quantification of pathological processes in vivo. Deep learning models, including convolutional neural networks and multimodal transformers, further improve diagnostic accuracy by integrating multimodal imaging data and predicting disease progression. Despite challenges such as technical variability, data privacy concerns, and regulatory barriers, the potential of AI-enhanced neuroimaging to revolutionize early diagnosis and personalized treatment in neurodegenerative and neuro-ophthalmic disorders is immense. This review underscores the importance of ongoing efforts to validate, standardize, and implement these technologies to maximize their clinical impact. Full article

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field’s potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs. Full article

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the “black box phenomenon”, biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine. Full article