Evolution of radiology staff perspectives during artificial intelligence (AI) implementation for expedited lung cancer triage.

Abstract

Aims: To investigate radiology staff perceptions of an AI tool for chest radiography triage, flagging findings suspicious for lung cancer to expedite same-day CT chest examination studies.

Materials and methods: Surveys were distributed to all radiology staff at three time points: at pre-implementation, one month and also seven months post-implementation of artificial intelligence (AI). Survey questions captured feedback on AI use and patient impact.

Results: Survey response rates at the three time periods were 23.1% (45/195), 14.9% (29/195) and 27.2% (53/195), respectively. Most respondents initially anticipated AI to be time-saving for the department and patient (50.8%), but this shifted to faster follow-up care for patients after AI implementation (51.7%). From the free text comments, early apprehension about job role changes evolved into frustration regarding technical integration challenges after implementation. This later transitioned to a more balanced view of recognised patient benefits versus minor ongoing logistical issues by the late post-implementation stage. There was majority disagreement across all survey periods that AI could be considered to be used autonomously (53.3-72.5%), yet acceptance grew for personal AI usage if staff were to be patients themselves (from 31.1% pre-implementation to 47.2% post-implementation).

Conclusion: Successful AI integration in radiology demands active staff engagement, addressing concerns to transform initial mixed excitement and resistance into constructive adaptation. Continual feedback is vital for refining AI deployment strategies, ensuring its beneficial and sustainable incorporation into clinical care pathways.