Exploring stakeholder perceptions about using artificial intelligence for the diagnosis of rare and atypical infections.

Objectives: To evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision-support system (DSS).

Methods: We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between 11/25/2023 and 1/15/2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use.

Results: A total of 47/143 providers completed the survey. 38/47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). 38/47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights for patient care, but almost three quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47).

Conclusion: Most critical care providers perceived 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 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.