Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections
Most critical care providers perceive that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting the usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.
Authors
- Sarah A. Minteer (ORCID: https://orcid.org/0000-0001-8486-6917)
- Aysun Tekin (ORCID: https://orcid.org/0000-0002-1891-2118)
- Amelia Barwise (ORCID: https://orcid.org/0000-0001-9267-1298)
- Ognjen Gajic (ORCID: https://orcid.org/0000-0003-4218-0890)
- Svetlana Herasevich (ORCID: https://orcid.org/0000-0003-0525-9364)
Institutions
- Mayo Clinic (US)
- WinnMed (US)
Publication Details
- Journal
- Applied Clinical Informatics
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1055/a-2451-9046
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Center for Advancing Translational Sciences