Multi-centre, multi-vendor validation of artificial intelligence (AI) software to detect clinically significant prostate cancer (PCa) using multiparametric magnetic resonance imaging (MRI) is lacking. We compared a new AI solution, validated on a separate dataset from different UK hospitals, to the original multidisciplinary team (MDT)-supported radiologist’s interpretations. A Conformité Européenne (CE)-marked deep-learning (DL) computer-aided detection (CAD) medical device (Pi) was trained to detect Gleason Grade Group (GG) ≥ 2 cancer using retrospective data from the PROSTATEx dataset and five UK hospitals (793 patients). Our separate validation dataset was on six machines from two manufacturers across six sites (252 patients). Data included in the study were from MRI scans performed between August 2018 to October 2022. Patients with a negative MRI who did not undergo biopsy were assumed to be negative (90.4

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.

The success of medical imaging as a diagnostic tool has resulted in a continuing increase in its use. Technological advances mean that images are now acquired at higher resolution and in greater volumes than ever before. This has led to an increase in the detection of findings which do not appear to be related to the primary purpose of the examination and have been termed "incidental". Many of these will be harmless but some will carry significant implications for the patient's health. Determining which of these findings are significant and which may be safely disregarded is an increasing problem in radiology practice. Radiologists should familiarise themselves with the more common incidental findings in order to make the best possible estimation of their importance in each case and to allow them to make appropriate recommendations for further investigation where this is indicated. The decision to advise further investigation carries implications for the patient and the service as a whole and requires careful consideration.

It’s a truism that the more we look the more we’ll find—but more imaging also means ever more opportunities to get things wrong, says Giles Maskell

Getting it Right First Time (GIRFT) is a national programme designed to improve medical care in the National Health Service (NHS) in England by reducing unwarranted variation. By tackling variation in the way services are delivered across the NHS and by sharing best practice, GIRFT identifies changes that will help improve care and patient outcomes as well as delivering efficiencies. Getting it Right First Time (GIRFT) is a national programme designed to improve medical care in the National Health Service (NHS) in England by reducing unwarranted variation. By tackling variation in the way services are delivered across the NHS and by sharing best practice, GIRFT identifies changes that will help improve care and patient outcomes as well as delivering efficiencies.

Currently the approach taken to duty of candour implies a dichotomy between “things going well” and “mistakes being made”

Error is inherent in radiological practice. Our awareness of the extent of this and the reasons behind it has increased in recent times. Our next step must be the development of a shared understanding with our patients of the limitations as well as the huge benefits of medical imaging.

As a radiologist I’m used to getting things wrong, but this one shook me up a bit

Objective: To determine the rate of incidental urological findings in patients undergoing computed tomography (CT) colonography/CT colonoscopy (CTC) for investigation of suspected colorectal cancer. Methods: Retrospective analysis of patients undergoing CTC between January 2011 and December 2013. All patients with new incidental urological findings were included with their type and number of urological findings. These were stratified as per the colonography reporting and data system (C-RADS) criteria, and a note made of any further imaging, intervention and histology where appropriate. Results: Within the time period, n = 1891 CTCs were undertaken. Of these, n = 333 (17.6%) had an incidental urological finding and n = 41 of these patients had dual incidental urological pathologies. In total, n = 49 had significant pathology which required monitoring, further imaging, and medical or surgical intervention; n = 12 required further imaging. In n = 1, the imaging result led to a decision to operate and in n = 9 the results excluded the need for surgical intervention; n = 24 underwent operative intervention. The rates of incidental urological findings were similar to those quoted in literature. Conclusion: Our study demonstrates a 17.6% rate of incidental urological findings. Of all findings, 13.1% were deemed significant to warrant further investigation or intervention, and 7.2% of patients with urological findings required intervention. CTCs can adequately image renal masses and further imaging of renal masses did not change management. Level of evidence: 4

What would we need if we seriously contemplated replacing chest radiography with CT scanning in acute care?

A truism in radiology is that the more we image, the more we will find. Some of it will help to advance the patient’s health, but much of it won’t

Hindsight bias is a real and very powerful phenomenon, and not just in radiology

Current working models and poorly designed working environments need to be improved