Lessons on AI implementation from senior clinical practitioners: An exploratory qualitative study in medical imaging and radiotherapy in the UK.

Introduction: Artificial Intelligence (AI) has the potential to transform medical imaging and radiotherapy; both fields where radiographers' use of AI tools is increasing. This study aimed to explore the views of those professionals who are now using AI tools.

Methods: A small-scale exploratory research process was employed, where qualitative data was obtained from five UK-based participants; all professionals working in medical imaging and radiotherapy who use AI in clinical practice.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting recommendations for studies developing or evaluating the performance of a prediction model. Methodological advances in the field of prediction have since included the widespread use of artificial intelligence (AI) powered by machine learning methods to develop prediction models. An update to the TRIPOD statement is thus needed.

How AI should be used in radiology: assessing ambiguity and completeness of intended use statements of commercial AI products.

Background: Intended use statements (IUSs) are mandatory to obtain regulatory clearance for artificial intelligence (AI)-based medical devices in the European Union. In order to guide the safe use of AI-based medical devices, IUSs need to contain comprehensive and understandable information. This study analyzes the IUSs of CE-marked AI products listed on AIforRadiology.

Black box no more: a scoping review of AI governance frameworks to guide procurement and adoption of AI in medical imaging and radiotherapy in the UK.

Technological advancements in computer science have started to bring artificial intelligence (AI) from the bench closer to the bedside. While there is still lots to do and improve, AI models in medical imaging and radiotherapy are rapidly being developed and increasingly deployed in clinical practice. At the same time, AI governance frameworks are still under development.

Can Apple and Google continue as health app gatekeepers as well as distributors and developers?

Mobile apps are the primary means by which consumers access digital health and wellness software, with delivery dominated by the 'Apple App Store' and the 'Google Play Store'. Through these virtual storefronts Apple and Google act as the distributor (and sometimes, importer) of many thousands of health and wellness apps into the EU, some of which have a medical purpose. As a result of changes to EU law which came into effect in May 2021, they must now ensure that apps are compliant with medical devices regulation and to inform authorities of serious incidents arising from their use.

Perceived Realism of High-Resolution Generative Adversarial Network-derived Synthetic Mammograms.

Purpose: To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts.

Materials And Methods: In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 × 1024 pixels by using images from 90 000 patients (average age, 56 years ± 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra-high-dimensional pixel distributions was used, which was based on moment plots.

To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines).

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase.

The effect of Brexit on, and changes in, the European Union medical device regulations.

With the exit of the UK from the European Union and the European Union Regulation 201/745 coming into effect on 26 May 2021, the regulatory landscape for medical devices is undergoing a substantial change, the implications of which will be felt by those procuring and using medical devices in clinical settings. This article outlines the changes that clinicians, as users of medical devices, should be aware of in the immediate future.

Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations.

Although artificial intelligence (AI)-based algorithms for diagnosis hold promise for improving care, their safety and effectiveness must be ensured to facilitate wide adoption. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. In this article, we review the major regulatory frameworks for software as a medical device applications, identify major gaps, and propose additional strategies to improve the development and evaluation of diagnostic AI algorithms.

Preparing Medical Imaging Data for Machine Learning.

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potential applications are vast and include the entirety of the medical imaging life cycle from image creation to diagnosis to outcome prediction. The chief obstacles to development and clinical implementation of AI algorithms include availability of sufficiently large, curated, and representative training data that includes expert labeling (eg, annotations).

Artificial intelligence in diagnostic imaging: impact on the radiography profession.

The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. This paper discusses the potential impact of artificial intelligence (AI) on the radiography profession by assessing current workflow and cross-mapping potential areas of AI automation such as procedure planning, image acquisition and processing.

Ultrasound Shear Wave Elastography of the Normal Prostate: Interobserver Reproducibility and Comparison with Functional Magnetic Resonance Tissue Characteristics.

The purpose of this study was to establish interobserver reproducibility of Young's modulus (YM) derived from ultrasound shear wave elastography (US-SWE) in the normal prostate and correlate it with multiparametric magnetic resonance imaging (mpMRI) tissue characteristics. Twenty men being screened for prostate cancer underwent same-day US-SWE (10 done by two blinded, newly-trained observers) and mpMRI followed by 12-core biopsy. Bland-Altman plots established limits of agreement for YM.

The role of imaging in the diagnosis of primary prostate cancer.

Ultrasound and magnetic resonance imaging (MRI) are key imaging modalities in prostate cancer diagnosis. MRI offers a range of intrinsic contrast mechanisms (T2, diffusion-weighted imaging (DWI), MR spectroscopy (MRS)) and extrinsic contrast-generating options based on tumour vascular state following injection of weakly paramagnetic agents such as gadolinium. Together these parameters are referred to as multiparametric (mp)MRI and are used for detecting and guiding biopsy and staging prostate cancer.

Volumetry of the dominant intraprostatic tumour lesion: intersequence and interobserver differences on multiparametric MRI.

Objective: To establish the interobserver reproducibility of tumour volumetry on individual multiparametric (mp) prostate MRI sequences, validate measurements with histology and determine whether functional to morphological volume ratios reflect Gleason score.

Methods: 41 males with prostate cancer treated with prostatectomy (Cohort 1) or radical radiotherapy (Cohort 2), who had pre-treatment mpMRI [T weighted (T2W) MRI, diffusion-weighted (DW)-MRI and dynamic contrast-enhanced (DCE)-MRI], were studied retrospectively. Dominant intraprostatic lesions (DIPLs) were manually delineated on each sequence and volumes were compared between observers (n = 40 analyzable) and with radical prostatectomy (n = 20).

Silent uterine perforation by an IUCD inserted during the puerperium.

A 38-year-old lady, with a history of recent caesarean section, was diagnosed with a silent uterine perforation by a copper intrauterine contraceptive device under fluoroscopic examination. The incidence of uterine perforation and the increased risk in the puerperium are discussed. The use of ultrasound as the first line investigation is recommended.