Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Knowledge and Awareness of AI Applications in Pulmonary Health Among Chest Diseases and Thoracic Surgery Physicians
0
Zitationen
3
Autoren
2025
Jahr
Abstract
<bold>Introduction and Aims:</bold> Artificial intelligence (AI) applications in lung health are growing, improving diagnostics and treatment. This study evaluates the knowledge of chest diseases and thoracic surgery physicians in Turkey regarding AI and its potential benefits in clinical practice. <bold>Methods:</bold> A 23-question survey was conducted among specialists and residents via digital platforms. Participation was voluntary, with ethics approval. Data were analyzed using averages and proportions. <bold>Results:</bold> A total of 258 physicians participated (70.2% under 45 years; 131 females). Among them, 192 specialized in chest diseases, 96 in thoracic surgery; 75.6% worked in training clinics, 65.7% had over five years of experience, and 68.6% were specialists or higher. Regarding AI, 39.7% rated their knowledge as average, while 61.6% did not use AI in clinical practice. Of 99 AI users, 66.7% used it weekly, mostly for academic studies. Most (85.3%) believed AI would be more prevalent in the future, and 68.6% expected reduced physician workload. Radiology, risk analysis, respiratory function test interpretation, and robotic surgery were seen as key AI application areas. Although 60.5% considered AI inferior in diagnosis and 65.5% in treatment decisions, 91.1% found AI beneficial, 89.5% believed it would enhance service delivery, and 67.4% expected high compliance. Legal regulations (86.0%) and data privacy (56.2%) were major concerns. Overall, 86.4% held a positive view of AI, and 85.3% were open to adopting it. <bold>Conclusions:</bold> Understanding AI awareness in chest diseases and thoracic surgery will guide training and implementation strategies. Physicians show high interest, emphasizing the need for structured education to support effective AI integration.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.214 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.071 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.429 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.418 Zit.