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

Author:

Togher Daniel1,Dean Geraldine1,Moon Jo1,Mayola Richard1,Medina Arman1,Repec Jadwiga1,Meheux Moesha1,Mather Sarah1,Storey Mathew2,Rickaby Simon3,Abubacker Mohamed Ziad1,Shelmerdine Susan4ORCID

Affiliation:

1. Epsom & St Helier University Hospitals NHS Trust, London, United Kingdom SM5 1AA

2. St George’s University Hospital, Blackshaw Road, London, SW17 0QT

3. Radiology Digital Transformation Lead, South West London APC, NHS South West London Health and Care Partnership, London, SW19 1RH

4. 4. Department of Clinical Radiology, Great Ormond Street Hospital for Children, London, WC1H 3JH, UK

Abstract

Abstract

Objectives 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. Methods Surveys were distributed to all radiology staff at three time points: at pre-implementation, one month and also seven months post-implementation of 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 implmentation. 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.

Funder

National Institute for Health Research

Publisher

Springer Science and Business Media LLC

Reference22 articles.

1. NHS Transformation Directive. Artificial Intelligence Diagnostic Fund (2023) https://transform.england.nhs.uk/ai-lab/ai-lab-programmes/ai-in-imaging/ai-diagnostic-fund/ (accessed 31 January 2024)

2. (NICE). Artificial intelligence-derived software to analyse chest X-rays for suspected lung cancer in primary care referrals: early value assessment (2023) https://www.nice.org.uk/guidance/hte12/chapter/1-Recommendations (accessed 31 January 2024)

3. Royal College of Radiologists. Overcoming Barriers to AI Implementation in Imaging: Outcome of an RCR Expert Stakeholder Day (2022) file:///Users/susanshelmerdine/Downloads/overcoming_barriers_to_ai_implementation_in_imaging_v3.pdf (accessed 5 January 2024)

4. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence;Hogg HDJ;J Med Internet Res,2023

5. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study;Seah JCY;Lancet Digit health,2021

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