AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers.

INTRODUCTION: Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption. METHODS: An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables. RESULTS: In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers. CONCLUSION: AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks. IMPLICATIONS FOR PRACTICE: The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training.

Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study.

INTRODUCTION: Breast cancer is the most common malignancy among women, and its diagnosis relies on medical imaging and the invasive, uncomforted biopsy. Recent advances in quantitative imaging and specifically the application of radiomics has proved to be a very promising technique, facilitating both diagnosis and therapy. The purpose of this study is to assess radiomic features derived from post-contrast T1w Magnetic Resonance Imaging (MRI) sequences and Apparent Diffusion Coefficient (ADC) maps for the evaluation of breast pathologies. METHODS: MRI data from 52 women were retrospectively reviewed, involving 54 breast lesions, both malignant and benign. Diffusion Weighted Imaging (DWI) was applied as a standard MRΙ protocol, including dynamic contrast-enhanced (DCE) MRΙ in all cases. All patients were examined on a 1.5T MRI scanner, and 216 features were initially extracted from DCE-MRI images. Histological analysis of the breast lesions was performed, and a comparative analysis of the results was carried out to assess the accuracy of the method. RESULTS: Following surgery and histological analysis, 30 lesions were found to be malignant and 24 benign. Implementation of a Machine Learning (ML) classification algorithm with 5-fold cross-validation resulted in a sensitivity of 70%, specificity of 66%, Negative Predictive Value of 82% and overall accuracy of 67% in differentiating malignancy from benevolence. CONCLUSION: Texture analysis and ML methodology based on the first post-contrast dynamic sequences and ADC maps may be employed to differentiate between malignant and benign breast lesions, offering a promising new tool for diagnostic analysis. IMPLICATIONS FOR PRACTICE: The results of this study will enhance knowledge around application and performance of radiomics in breast MRI, thus helping MRI radiographers who use AI-enabled technologies to better delineate the pros and cons of these procedures.

Autism-friendly MRI: Improving radiography practice in the UK, a survey of radiographer practitioners.

INTRODUCTION: Autistic individuals undergoing magnetic resonance imaging (MRI) examinations may face significant challenges, mainly due to sensory overload and MRI environment-related limitations. This study aimed to explore radiographers' perspectives and experiences regarding MRI scanning of autistic individuals. METHODS: Data collection was achieved using a specifically designed mixed methods questionnaire on Qualtrics. The snowball technique was used. This UK-wide survey was electronically distributed by three main recruitment agencies between December 2020 and February 2021. RESULTS: 130 valid responses were received. A lack of relevant training and knowledge related to autism was noted. Effective communication, optimisation and customisation of the MRI examination, and MRI environment adjustments facilitated the completion of a safe and effective MRI examination. Poor patient-radiographer communication, unavailability of Special Educational Needs (SEN) experts, lack of specialised radiographer training and lack of specific guidelines were identified as the main barriers to successful MRI examinations. CONCLUSION: Although routine MRI safety and patient care rules will apply, MRI scanning of autistic individuals requires customisation and reasonable adjustments in communication, environment, and training of clinical teams. In addition, guidelines should be established to be used as a reference point to improve clinical practice. The adjustments proposed by radiographers were all consistent with the interventions in the wider literature. IMPLICATIONS FOR PRACTICE: MRI practice for personalised care of autistic individuals should be aligned with current evidence, to customise communication and offer workflow and environmental adjustments. Formal training related to autism, integrated within radiography academic curricula and better co-ordination and communication of interdisciplinary teams would provide the necessary skill mix to deliver safe, high quality MRI scans with optimal experience for autistic service users and their carer(s).

COVID-19 in the radiology department: What radiographers need to know.

OBJECTIVES: The aim is to review current literature related to the diagnosis, management, and follow-up of suspected and confirmed Covid-19 cases. KEY FINDINGS: Medical Imaging plays an important auxiliary role in the diagnosis of Covid-19 patients, mainly those most seriously affected. Practice differs widely among different countries, mainly due to the variability of access to resources (viral testing and imaging equipment, specialised staff, protective equipment). It has been now well-documented that chest radiographs should be the first-line imaging tool and chest CT should only be reserved for critically ill patients, or when chest radiograph and clinical presentation may be inconclusive. CONCLUSION: As radiographers work on the frontline, they should be aware of the potential risks associated with Covid-19 and engage in optimal strategies to reduce these. Their role in vetting, conducting and often reporting the imaging examinations is vital, as well as their contribution in patient safety and care. Medical Imaging should be limited to critically ill patients, and where it may have an impact on the patient management plan. IMPLICATIONS FOR PRACTICE: At the time of publication, this review offers the most up-to-date recommendations for clinical practitioners in radiology departments, including radiographers. Radiography practice has to significantly adjust to these new requirements to support optimal and safe imaging practices for the diagnosis of Covid-19. The adoption of low dose CT, rigorous infection control protocols and optimal use of personal protective equipment may reduce the potential risks of radiation exposure and infection, respectively, within Radiology departments.