Objective To evaluate medical students’ perceptions and attitudes toward artificial intelligence (AI)-assisted diagnosis of renal cell carcinoma (RCC), and to analyze their educational needs regarding AI in pathological diagnosis. Methods A questionnaire survey (including closed and open-ended questions) was conducted to assess medical students’ perceptions, attitudes, and educational needs concerning AI-assisted RCC diagnosis. Participants included medical students from different specialties and standardized training residents. The questionnaire covered demographic information, perceptions and attitudes toward AI, and AI-related educational needs. Results A total of 249 respondents completed the survey. The majority were standardized training residents, mostly aged 23-26 years, and 40.96% had practical experience in pathological diagnosis of RCC. The median scores for most closed-ended questions were 4. Respondents generally considered “efficiency” and “improved accuracy” as the most prominent advantages of AI, with timeliness, automated diagnosis, reduction of human error, and precise diagnosis being the most emphasized aspects. Analysis of AI-related educational needs revealed high-frequency keywords such as “expanding sample size” “balanced responsibility allocation” and “enhancing collaboration skills.” Conclusion Medical students hold a positive attitude toward AI and its application in RCC diagnosis, but there remains a lack of formal AI-related education.
Objective To improve the diagnosis and treatment of pulmonary infiltration with eosinophilia (PIE). Methods Patients who were diagnosed with PIE in the First Affiliated Hospital of Guangzhou Medical University from January 2004 to December 2013 were recruited and retrospectively analyzed. Data of etiology, clinical manifestation, imaging and pathological features were recorded. Results pulmonary eosinophilic granuloma (PEG) (n=2), eosinophilic granulomatosis with polyangiitis (EGPA) (n=7), Löffler syndrome (n=4), allergic bronchopulmonary aspergillosis (ABPA) (n=16), and chronic eosinophilic pneumonia (CEP) (n=19). There were 27 males and 21 females. 47.9% of the PIE patients were diagnosed as asthma and treated with regular treatment but had not been controlled well. PEG was characterized with wheeze and anhelation in clinical manifestations, unelevated blood eosinophil counts and percentage, significant small airway abnormalities in lung function, diffuse pneumonectasis in Chest CT, and appearance of eosinophil cells in alveole. EGPA shows dyspnea and cough in clinical manifestations, as well as other organs function damaged, unelevated blood eosinophil counts and percentage, significant FEV1/FVC and small airway abnormalities in lung function, tree-in-bud in Chest CT, appearance of eosinophilic granuloma outside blood vessels. Löffler syndrome also showed cough, shorter course of disease, normal lung function and diffusion. ABPA showed wheeze and cough, 31.3% of them with hemoptysis, normal blood eosinophil count, central bronchiectasis in Chest CT. CEP also showed dyspnea and cough. 21.1% of CEP patientshad chest pain, increasing sputum eosinophil percentage compare with blood eosinophil percentage, and small airway abnormalities in lung function. Conclusions Most of PIE patients are diagnosed as asthma but haven’t gotten well controlled under the regular anti-asthmatic treatment. Patients with PIE have increasing eosinophil counts and decreasing lung function. The diagnosis of PIE still depends on clinical manifestation, laboratory test, imaging and pathological examination.