Towards human-AI collaboration in radiology: a multidimensional evaluation of the acceptability of AI for chest radiograph analysis in supporting pulmonary tuberculosis diagnosis
This study provides preliminary evidence of how an AI system with reasonable diagnostic accuracy and a human-centered user experience can meaningfully augment the TB diagnostic workflow. Future research needs to investigate the impact of AI on clinician accuracy, its relationship with efficiency, and best practices for optimizing the impact of clinician-AI collaboration.
Authors
- Jin‐Gun Cho (ORCID: https://orcid.org/0000-0002-0735-4691)
- Simon Kar-Sing Poon (ORCID: https://orcid.org/0000-0003-2726-9109)
- Neysa Petrina (ORCID: https://orcid.org/0000-0001-9163-1761)
- Alan J Sacks (ORCID: https://orcid.org/0000-0002-4786-6771)
- Noel Young (ORCID: https://orcid.org/0000-0001-8591-8897)
- David Hua (ORCID: https://orcid.org/0000-0002-6334-5626)
- Ross C. Smith (ORCID: https://orcid.org/0000-0001-8132-9024)
Institutions
- The University of Sydney (AU)
- New South Wales Department of Health (AU)
- Sydney Local Health District (AU)
Publication Details
- Journal
- JAMIA Open
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/jamiaopen/ooae151
- Citations
- 4
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 1.62