Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys

Han, Jae‐Ho

doi:10.3390/diagnostics12081927

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…In this regard, one AI software by IBM was compared to human experts for 1.000 cancer diagnoses and found treatment options that doctors missed in 30% of the cases [40,41]. Today, AIbased mobile apps can accurately detect skin cancer [42], and algorithms have shown promising results in identifying eye diseases [43]. Longoni et al [41] proposed that individuals may be more reluctant to rely on medical care delivered by AI because they perceive that being cared for by an algorithm-instead of a human doctor-can neglect one's unique characteristics, circumstances, and symptoms.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Eyes of Society: Assessing Social Risk and Social Value Perception in a Novel Classification

Alessandro,

Federica,

Cristina

et al. 2024

Human Behavior and Emerging Technologies

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Artificial intelligence (AI) is a rapidly developing technology that has the potential to create previously unimaginable chances for our societies. Still, the public’s opinion of AI remains mixed. Since AI has been integrated into many facets of daily life, it is critical to understand how people perceive these systems. The present work investigated the perceived social risk and social value of AI. In a preliminary study, AI’s social risk and social value were first operationalized and explored by adopting a correlational approach. Results highlighted that perceived social value and social risk represent two significant and antagonistic dimensions driving the perception of AI: the higher the perceived risk, the lower the social value attributed to AI. The main study considered pretested AI applications in different domains to develop a classification of AI applications based on perceived social risk and social value. A cluster analysis revealed that in the two-dimensional social risk × social value space, the considered AI technologies grouped into six clusters, with the AI applications related to medical care (e.g., assisted surgery) unexpectedly perceived as the riskiest ones. Understanding people’s perceptions of AI can guide researchers, developers, and policymakers in adopting an anthropocentric approach when designing future AI technologies to prioritize human well-being and ensure AI’s responsible and ethical development in the years to come.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Eyes of Society: Assessing Social Risk and Social Value Perception in a Novel Classification

Alessandro,

Federica,

Cristina

et al. 2024

Human Behavior and Emerging Technologies

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“…Lastly, AI-based screening tools empower patients to actively engage in their health, offering timely information about their condition, facilitating informed decisions on treatment options, and encouraging regular monitoring for optimal eye health. In conclusion, leveraging artificial intelligence for the early detection of ocular diseases holds the potential to safeguard visual acuity, enhance treatment outcomes, reduce medical costs, and overall improve individual well-being by addressing ocular conditions before they advance to critical stages [8][9][10][11][12][13][14][15][16][17][18].…”

Section: Skin Pathologiesmentioning

confidence: 99%

Advancements in Healthcare: Development of a Comprehensive Medical Information System with Automated Classification for Ocular and Skin Pathologies—Structure, Functionalities, and Innovative Development Methods

Ștefan,

Rusu,

Ovreiu

et al. 2024

ASI

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This article introduces a groundbreaking medical information system developed in Salesforce, featuring an automated classification module for ocular and skin pathologies using Google Teachable Machine. Integrating cutting-edge technology with Salesforce’s robust capabilities, the system provides a comprehensive solution for medical practitioners. The article explores the system’s structure, emphasizing innovative functionalities that enhance diagnostic precision and streamline medical workflows. Methods used in development are discussed, offering insights into the integration of Google Teachable Machine into the Salesforce framework. This collaborative approach is a significant stride in intelligent pathology classification, advancing the field of medical information systems and fostering efficient healthcare practices.

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“…We discovered that building an efficient neural network classifier requires careful consideration of both the network architecture and the data input. The literature has much research about the use of deep learning as a classification model for diabetic eye disease using fundus images, as in Refs [14], [15], [25], [26].…”

Section: Literature Reviewmentioning

confidence: 99%

DeepDiabetic: An Identification System of Diabetic Eye Diseases Using Deep Neural Networks

Albelaihi,

Ibrahim

2024

IEEE Access

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Deep Learning (DL) plays a successful and influential role in many areas of medical imaging diagnosis, image detection, and image classification. Diabetes is a significant public health concern, and diabetic eye disease will be the leading cause of vision loss across the globe. This research proposed a multi-classification deep learning model for diagnosing and identifying four different diabetic eye diseases: Diabetic Retinopathy (DR), Diabetic Macular Edema (DME), glaucoma, and cataract which we called the DeepDiabetic framework. The proposed models were assessed using 1228 images from six different available datasets (DIARETDB0, DIARETDB1, Messidor, HEI-MED, Ocular, and Retina). In addition to the original dataset, we measured the performance of the deep learning models according to two different geometric augmentation methods called online augmented and offline augmented. The present work considers five architectures' performances: EfficientNetB0, VGG16, ResNet152V2, ResNet152V2 + Gated Recurrent Unit (GRU), and ResNet152V2 + Bidirectional GRU (Bi-GRU). A comprehensive analysis and evaluation of these deep learning architectures is provided using public fundus datasets with four classes (i.e., DR, DME, Glaucoma, and Cataract). To the best of our knowledge, no other deep learning models for choosing between these models for these specific diseases are found in the literature. From the results of the experiments, it was found that the EfficientNetB0 model outperforms the other four proposed models. The EfficientNetB0 model achieved 0.9876 in accuracy, 0.9876 recall, 0.9876 precision, and 0.9977 AUC based on fundus images. Our EfficientNetB0 model achieves 98.76% accuracy, while the previous studies only achieved 88.33%, 89.54%, 97.23%, and 80.33% accuracy, respectively. When compared to the previous studies as Fast-RCNN, RCNN-LSTM, and InceptionResNet, our EfficientNetB0 model achieves much higher accuracy, recall, precision, and AUC. According to the outcomes, our proposed models, especially the EfficientNetB0 model, are significantly more accurate than the state-of-the-art models.

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Artificial Intelligence in Eye Disease: Recent Developments, Applications, and Surveys

Abstract: Artificial intelligence (AI) has expanded by finding applications in medical diagnosis for clinical support systems [...]

Cited by 13 publications

References 21 publications

Artificial Intelligence in the Eyes of Society: Assessing Social Risk and Social Value Perception in a Novel Classification

Artificial Intelligence in the Eyes of Society: Assessing Social Risk and Social Value Perception in a Novel Classification

Advancements in Healthcare: Development of a Comprehensive Medical Information System with Automated Classification for Ocular and Skin Pathologies—Structure, Functionalities, and Innovative Development Methods

DeepDiabetic: An Identification System of Diabetic Eye Diseases Using Deep Neural Networks

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