Physicians' Perspectives on ChatGPT in Ophthalmology: Insights on Artificial Intelligence (AI) Integration in Clinical Practice DOI Open Access
Anwar Ahmed, Dalal Fatani, Jose M. Vargas

et al.

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

To obtain detailed data on the acceptance of an artificial intelligence chatbot (ChatGPT; OpenAI, San Francisco, CA, USA) in ophthalmology among physicians, a survey explored physician responses regarding using ChatGPT ophthalmology. The included questions about applications ophthalmology, future concerns such as job replacement or automation, research, medical education, patient ethical concerns, and implementation practice. One hundred ninety-nine ophthalmic surgeons participated this study. Approximately two-thirds participants had 15 years more experience sixteen reported that they used ChatGPT. We found no difference age, gender, level between those who did not use users tend to consider (AI) useful (P=0.001). Both non-users think AI is for identifying early signs eye disease, providing decision support treatment planning, monitoring progress, answering questions, scheduling appointments. believe there are some issues related health care, liability issues, privacy accuracy diagnosis, trust chatbot, information bias. other forms increasingly becoming accepted ophthalmologists. seen helpful tool improving support, services, but also displacement, which warrant human oversight.

Language: Английский

How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications DOI Creative Commons
Luís Coelho

Bioengineering, Journal Year: 2023, Volume and Issue: 10(12), P. 1435 - 1435

Published: Dec. 18, 2023

The integration of artificial intelligence (AI) into medical imaging has guided in an era transformation healthcare. This literature review explores the latest innovations and applications AI field, highlighting its profound impact on diagnosis patient care. innovation segment cutting-edge developments AI, such as deep learning algorithms, convolutional neural networks, generative adversarial which have significantly improved accuracy efficiency image analysis. These enabled rapid accurate detection abnormalities, from identifying tumors during radiological examinations to detecting early signs eye disease retinal images. article also highlights various imaging, including radiology, pathology, cardiology, more. AI-based diagnostic tools not only speed up interpretation complex images but improve disease, ultimately delivering better outcomes for patients. Additionally, processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. paradigm shift that brought role revolutionizing By combining techniques their practical applications, it is clear will continue shaping future positive ways.

Language: Английский

Citations

152
Artificial intelligence and digital health in global eye health: opportunities and challenges DOI

Ting Fang Tan,

Arun James Thirunavukarasu, Liyuan Jin

et al.

The Lancet Global Health, Journal Year: 2023, Volume and Issue: 11(9), P. e1432 - e1443

Published: Aug. 15, 2023

Language: Английский

Citations

39
Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study DOI Creative Commons
Xiaocong Liu, Jiageng Wu, An Shao

et al.

Journal of Medical Internet Research, Journal Year: 2023, Volume and Issue: 26, P. e51926 - e51926

Published: Nov. 30, 2023

Benefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, performance of non-English settings, as well its reasoning, not been explored depth.

Language: Английский

Citations

32
How can machine learning and multiscale modeling benefit ocular drug development? DOI
Nannan Wang, Yunsen Zhang, Wei Wang

et al.

Advanced Drug Delivery Reviews, Journal Year: 2023, Volume and Issue: 196, P. 114772 - 114772

Published: March 10, 2023

Language: Английский

Citations

24
MSHF: A Multi-Source Heterogeneous Fundus (MSHF) Dataset for Image Quality Assessment DOI Creative Commons
Kai Jin, Zhiyuan Gao, Xiaoyu Jiang

et al.

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: May 17, 2023

Image quality assessment (IQA) is significant for current techniques of image-based computer-aided diagnosis, and fundus imaging the chief modality screening diagnosing ophthalmic diseases. However, most existing IQA datasets are single-center datasets, disregarding type device, eye condition, environment. In this paper, we collected a multi-source heterogeneous (MSHF) database. The MSHF dataset consisted 1302 high-resolution normal pathologic images from color photography (CFP), healthy volunteers taken with portable camera, ultrawide-field (UWF) diabetic retinopathy patients. Dataset diversity was visualized spatial scatter plot. determined by three ophthalmologists according to its illumination, clarity, contrast overall quality. To best our knowledge, one largest believe work will be beneficial construction standardized medical image

Language: Английский

Citations

23
Application of artificial intelligence in oculoplastics DOI

Yilu Cai,

Xuan Zhang,

Jing Cao

et al.

Clinics in Dermatology, Journal Year: 2024, Volume and Issue: 42(3), P. 259 - 267

Published: Jan. 4, 2024

Language: Английский

Citations

10
Artificial intelligence in retinal screening using OCT images: A review of the last decade (2013–2023) DOI
Muhammed Halil Akpınar, Abdulkadir Şengür, Oliver Faust

et al.

Computer Methods and Programs in Biomedicine, Journal Year: 2024, Volume and Issue: 254, P. 108253 - 108253

Published: May 28, 2024

Language: Английский

Citations

10
A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality DOI Creative Commons

Hongkang Wu,

Kai Jin,

Chee Chew Yip

et al.

Survey of Ophthalmology, Journal Year: 2024, Volume and Issue: 69(4), P. 499 - 507

Published: March 15, 2024

Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field ophthalmology. Nevertheless, there is lack systematic studies on health economics AI this field. This review examines from PubMed, Google Scholar, and Web Science databases that employed quantitative analysis, retrieved up to July 2023. Most indicate leads cost savings improved efficiency On other hand, some suggest using healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, patient adherence. Future should cover wider range ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive economic research, designed collect data relevant its own context, imperative China facilitate clinical implementation within country.

Language: Английский

Citations

7
A Beginner’s Guide to Artificial Intelligence for Ophthalmologists DOI Creative Commons

Daohuan Kang,

Hongkang Wu,

Lu Yuan

et al.

Ophthalmology and Therapy, Journal Year: 2024, Volume and Issue: 13(7), P. 1841 - 1855

Published: May 11, 2024

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

Language: Английский

Citations

7
Choice of refractive surgery types for myopia assisted by machine learning based on doctors’ surgical selection data DOI Creative Commons
Jiajing Li,

Yuanyuan Dai,

Z Mu

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: Feb. 8, 2024

Abstract In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary comprehensively consider differences equipment technology well specificity individual patients, which heavily depend on experience ophthalmologists. our study, we took advantage machine learning learn about ophthalmologists decision-making assist them choice a new case. Our study was based clinical data 7,081 patients who underwent between 2000 2017 at Department Ophthalmology, Peking Union Medical College Hospital, Chinese Academy Sciences. Due long period, there were losses errors this dataset. First, cleaned deleted samples key loss. Then, divided into three groups according type after SMOTE eliminate imbalance groups. Six statistical models, including NBM, RF, AdaBoost, XGBoost, BP neural network, DBN selected, ten-fold cross-validation grid search determine optimal hyperparameters for better performance. tested dataset, multi-class RF model showed best performance, with agreement ophthalmologist decisions high 0.8775 Macro F1 0.8019. Furthermore, results feature importance analysis SHAP technique consistent ophthalmologist’s practical experience. research will appropriate types have beneficial effects.

Language: Английский

Citations

6