Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A smartphone-based survey in mHealth to investigate the introduction of the artificial intelligence into cardiology
13
Zitationen
2
Autoren
2021
Jahr
Abstract
BACKGROUND: There is an increasing discussion concerning the integration of artificial intelligence (AI) into medical decision-making. AI science is a branch of engineering that implements novel concepts to resolve complex challenges and defined as the theory and development of computer systems to perform tasks which would normally require human intelligence. AI could aid cardiologists in improving decision-making, workflow, productivity, cost-effectiveness, and ultimately, patient outcomes. The present study proposes a tool for a positioning exercise in cardiology using mobile technology. METHODS: This study is based on a dedicated tool with electronic surveys that collect the opinions, requirements, and desires of the interested actors including both laypeople and professionals. RESULTS: The tool was tested on 30 cardiologists and 30 subjects not involved in health care. The data-analysis revealed several clear trends on the cardiologists: (I) a high desire to invest in AI; (II) high confidence in the use of AI in several fields of cardiology from risk prevention to diagnostics in medical imaging; (III) low confidence in the use of AI in quality control procedures; (IV) a strong belief that ethical issues are hampering the diffusion of AI to different fields. The data-analysis on the 30 subjects not involved in health care highlighted that AI is still not well known and therefore looked with suspicious. CONCLUSIONS: The integration of AI with telemedicine and e-health is a key issue for the health care. The study highlights how the mobile technology-based positioning exercises in mHealth can be useful for health care decision makers.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.707 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.613 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.159 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.875 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.