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Artificial intelligence (AI) has demonstrated revolutionary potential and wide-ranging applications in the comprehensive management of fundus diseases, yet it faces challenges in clinical translation, data quality, algorithm interpretability, and cross-cultural adaptability. AI has proven effective in the efficient screening, accurate diagnosis, personalized treatment recommendations, and prognosis prediction for conditions such as diabetic retinopathy, age-related macular degeneration, and other fundus diseases. However, there is a significant gap between the need for large-scale, high-quality, and diverse datasets and the limitations of current research data. Additionally, the black-box nature of AI algorithms, the acceptance by clinicians and patients, and the generalizability of these algorithms pose barriers to their widespread clinical adoption. Researchers are addressing these challenges through approaches such as federated learning, standardized data collection, and prospective trials to enhance the robustness, interpretability, and practicality of AI systems. Despite these obstacles, the benefits of AI in fundus disease management are substantial. These include improved screening efficiency, support for personalized treatment, the discovery of novel disease characteristics, and the development of precise treatment strategies. Moreover, AI facilitates the advancement of telemedicine through 5G and the Internet of Things. Future research should continue to tackle existing issues, fully leverage the potential of AI in the prevention and treatment of fundus diseases, and advance intelligent, precise, and remote ophthalmic services to meet global eye health needs.

Objective: To understand the utilization and characteristics of outpatient services for pneumoconiosis patients within two weeks in Chongqing, and analyze the influencing factors, so as to provide reference for relevant policy making. Methods: From October 2020 to October 2022, 1771 pneumoconiosis patients who met the inclusion criteria were selected by multi-stage stratified random cluster sampling. A questionnaire survey was conducted on their basic situation, utilization of outpatient services within two weeks, treatment for pneumoconiosis-related symptoms, and selection of medical service institutions using χ(2)-test and logistic regression analysis. Results: All the 1771 pneumoconiosis patients were male, with the average age of (56.1±10.19) years old. In the pneumoconiosis patients were treated in outpatient department within 2 weeks.40.0% (204/510) of aged 41~50 years Rural patients accounted for 87.8% (448/510) ; 65.1% (332/510) of silicosis patients, 37.5% (191/510) of stage II patients, 75.1% (383/510) of patients did not continue to engage in dust work after diagnosis of pneumoconiosis, and 57.1% (291/510) of patients never had work-related injury insurance at work. The outpatient rate within two weeks of pneumoconiosis related assistance and subsistence allowance was 17.6% (90/510) and 12.5% (64/510), respectively. The average self-health score of the patients was (52.9±16.2). 28.2% of the patients had purchased work-related injury insurance; Among the 1204 patients who received the treatment within two weeks, 42.2% were in the outpatient department, 20.7% were in the inpatient department, and 36.9% were self-buyers. There was a significant difference between the different treatment methods of the patients (χ(2)=27.53, P <0.05). There was a significant difference in patients from different residence choosing to visit different medical institutions (χ(2)=13.97, P <0.05). The stage of pneumoconiosis, presence of complications, presence of work injury insurance, self-health score, and whether he/she has been hospitalized in the past year are the important factors affecting the outpatient treatment of pneumoconiosis patients. Conclusion: The utilization of outpatient service of pneumoconiosis patients is influenced by demographic sociology, social support and disease characteristics. The quality of occupational disease medical service in primary health institutions should be strengthened so that pneumoconiosis patients can get convenient and effective treatment. Establish a more perfect social security support system to reduce the disease burden of pneumoconiosis patients.