RESUMEN
BACKGROUND: Artificial intelligence (AI)-assisted image interpretation is a fast-developing area of clinical innovation. Most research to date has focused on the performance of AI-assisted algorithms in comparison with that of radiologists rather than evaluating the algorithms' impact on the clinicians who often undertake initial image interpretation in routine clinical practice. This study assessed the impact of AI-assisted image interpretation on the diagnostic performance of frontline acute care clinicians for the detection of pneumothoraces (PTX). METHODS: A multicentre blinded multi-case multi-reader study was conducted between October 2021 and January 2022. The online study recruited 18 clinician readers from six different clinical specialties, with differing levels of seniority, across four English hospitals. The study included 395 plain CXR images, 189 positive for PTX and 206 negative. The reference standard was the consensus opinion of two thoracic radiologists with a third acting as arbitrator. General Electric Healthcare Critical Care Suite (GEHC CCS) PTX algorithm was applied to the final dataset. Readers individually interpreted the dataset without AI assistance, recording the presence or absence of a PTX and a confidence rating. Following a 'washout' period, this process was repeated including the AI output. RESULTS: Analysis of the performance of the algorithm for detecting or ruling out a PTX revealed an overall AUROC of 0.939. Overall reader sensitivity increased by 11.4% (95% CI 4.8, 18.0, p=0.002) from 66.8% (95% CI 57.3, 76.2) unaided to 78.1% aided (95% CI 72.2, 84.0, p=0.002), specificity 93.9% (95% CI 90.9, 97.0) without AI to 95.8% (95% CI 93.7, 97.9, p=0.247). The junior reader subgroup showed the largest improvement at 21.7% (95% CI 10.9, 32.6), increasing from 56.0% (95% CI 37.7, 74.3) to 77.7% (95% CI 65.8, 89.7, p<0.01). CONCLUSION: The study indicates that AI-assisted image interpretation significantly enhances the diagnostic accuracy of clinicians in detecting PTX, particularly benefiting less experienced practitioners. While overall interpretation time remained unchanged, the use of AI improved diagnostic confidence and sensitivity, especially among junior clinicians. These findings underscore the potential of AI to support less skilled clinicians in acute care settings.
RESUMEN
BACKGROUND: The debate on current doctors' attire in Irish hospital resurfaced alongside COVID-19, as a shift in doctors' attire from professional attire to scrubs was observed. AIM: The current study aimed to explore whether COVID-19 has changed the way in which hospital doctors perceive their personal attire, and whether this group wish for changes implemented during COVID-19 to become permanent. METHODS: Hospital doctors (n = 151), across all specialties and seniority at University Hospital Galway (UHG), filled out a ten-point online questionnaire exploring their experience of and attitudes towards hospital attire during COVID-19. Data collected and analysed in August-September 2020. RESULTS: Seventy-six percent (119) changed their attire during COVID-19 to scrubs (54% to hospital provided; 22% to private). Thirty-eight percent (56) reported feeling uncomfortable with bringing clothing home, highlighting the infection control risk. Seventy-four per cent (110) wanted the change to scrubs as standard attire to become permanent (65% to hospital provided; 9% to private). Thirty-two percent (47) noted a change in patients' perception when wearing scrubs. CONCLUSION: Hospital doctors changed their attire during COVID-19, and 75% would like these changes to become permanent. Most (67%) did not notice a change in their patient's perception of them, raising questions about the longstanding beliefs surrounding 'professional attire'. A large number of doctors are also worried about bringing clothing home. The humble hospital scrubs have shown their worth amidst the COVID-19 pandemic. Why not continue to wear them?