Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Attitudes towards artificial intelligence in emergency medicine
27
Zitationen
15
Autoren
2023
Jahr
Abstract
OBJECTIVE: To assess Australian and New Zealand emergency clinicians' attitudes towards the use of artificial intelligence (AI) in emergency medicine. METHODS: We undertook a qualitative interview-based study based on grounded theory. Participants were recruited through ED internal mailing lists, the Australasian College for Emergency Medicine Bulletin, and the research teams' personal networks. Interviews were transcribed, coded and themes presented. RESULTS: Twenty-five interviews were conducted between July 2021 and May 2022. Thematic saturation was achieved after 22 interviews. Most participants were from either Western Australia (52%) or Victoria (16%) and were consultants (96%). More participants reported feeling optimistic (10/25) than neutral (6/25), pessimistic (2/25) or mixed (7/25) towards the use of AI in the ED. A minority expressed scepticism regarding the feasibility or value of implementing AI into the ED. Multiple potential risks and ethical issues were discussed by participants including skill loss from overreliance on AI, algorithmic bias, patient privacy and concerns over liability. Participants also discussed perceived inadequacies in existing information technology systems. Participants felt that AI technologies would be used as decision support tools and not replace the roles of emergency clinicians. Participants were not concerned about the impact of AI on their job security. Most (17/25) participants thought that AI would impact emergency medicine within the next 10 years. CONCLUSIONS: Emergency clinicians interviewed were generally optimistic about the use of AI in emergency medicine, so long as it is used as a decision support tool and they maintain the ability to override its recommendations.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.646 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.554 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.071 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.851 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Autoren
Institutionen
- The University of Western Australia(AU)
- Harry Perkins Institute of Medical Research(AU)
- St Vincent's Hospital Melbourne(AU)
- Monash University(AU)
- The University of Notre Dame Australia(AU)
- Fiona Stanley Hospital(AU)
- Government of Western Australia Department of Health(AU)
- University of Tasmania(AU)
- Royal Hobart Hospital(AU)
- Sir Charles Gairdner Hospital(AU)
- Christchurch Hospital(NZ)
- Royal Perth Hospital(AU)
- Monash Health(AU)