Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
IJCM_269A: Knowledge, attitude, and practice of artificial intelligence in medical field among Undergraduate and Postgraduate medical students in Tamil Nadu: A cross-sectional online survey.
2
Zitationen
2
Autoren
2024
Jahr
Abstract
Background: Artificial Intelligence (AI) in the health care has gained attention worldwide due to its potential to revolutionize health care delivery, improve diagnostics, and enhance patient outcomes. AI has the potential to address challenges in Indian health care sector like shortage of workforce by providing decision support, automating repetitive tasks, and improving diagnostic accuracy. Objective: To assess the Knowledge, Attitude and Practice (KAP) of Artificial Intelligence in health care among Undergraduate and Postgraduate medical students in Tamil Nadu. Methodology: A cross-sectional online survey was conducted among 200 undergraduate and postgraduate medical students over a period of 2 months (Sept to Oct 2023). Data collection was done using Google form which comprises of a pretested, semi-structured questionnaire and statistical analysis was done using IBM SPSS version 21. Results: Out of 200 participants, 94 (47%) were Undergraduates and 106 (53%) were Postgraduates. In total 123 (61.5%) had good knowledge, 190 (95%) had positive attitude and 61 (30.5%) had good practice towards Artificial Intelligence in healthcare. Postgraduates had better knowledge and practice than undergraduates Conclusion: A majority of the participants displayed good knowledge and positive attitude towards Artificial Intelligence in healthcare. Notably, postgraduates outperformed undergraduates, suggesting the need for tailored educational interventions. While AI training in the curriculum remains limited, the high willingness to engage with AI in the future underscores its potential for transformative impact in healthcare.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.200 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.051 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.416 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 · 5.410 Zit.