Attitudes towards artificial intelligence in emergency medicine

Jonathon Stewart, Samuel Freeman, Ege Eroglu, Nicole Dumitrascu, Juan Lu, Adrian Goudie, Peter Sprivulis, Hamed Akhlaghi, Viet Tran, Frank Sanfilippo, Antonio Celenza, Martin Than, Daniel Fatovich, Katie Walker, Girish Dwivedi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)252-265
Number of pages14
JournalEmergency medicine Australasia : EMA
Volume36
Issue number2
Early online date4 Dec 2023
DOIs
Publication statusPublished - Apr 2024

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