Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey
191
Zitationen
10
Autoren
2022
Jahr
Abstract
Background: The use of Artificial intelligence (AI) has gained popularity during the last few decades and its use in medicine is increasing globally. Developing countries like Pakistan are lagging in the implementation of AI-based solutions in healthcare. There is a need to incorporate AI in the health system which may help not only in expediting diagnosis and management but also injudicious resource allocation. Objective: To determine the knowledge, attitude, and practice of AI among doctors and medical students in Pakistan. Materials and methods: We conducted a cross-sectional study using an online questionnaire-based survey regarding demographic details, knowledge, perception, and practice of AI. A sample of 470 individuals including doctors and medical students were selected using the convenient sampling technique. The chi-square test was applied for the comparison of variables. Results: Out of 470 individuals, 223(47.45%) were doctors and 247(52.55%) were medical students. Among these, 165(74%) doctors and 170(68.8%) medical students had a basic knowledge of AI but only 61(27.3%) doctors and 48(19.4%) students were aware of its medical applications. Regarding attitude, 237(76.7%) individuals supported AI's inclusion in curriculum, 368(78.3%) and 305(64.9%), 281(59.8%) and 269(57.2%) acknowledged its necessity in radiology, pathology, and COVID-19 pandemic respectively. Conclusion: The majority of doctors and medical students lack knowledge about AI and its applications, but had a positive view of AI in the field of medicine and were willing to adopt it.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.674 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.583 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.105 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.862 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.