Gender and ethnicity bias in generative artificial intelligence text-to-image depiction of pharmacists.

INTRODUCTION: In Australia, 64% of pharmacists are women but continue to be under-represented. Generative artificial intelligence (AI) is potentially transformative but also has the potential for errors, misrepresentations, and bias. Generative AI text-to-image production using DALL-E 3 (OpenAI) is readily accessible and user-friendly but may reinforce gender and ethnicity biases. METHODS: In March 2024, DALL-E 3 was utilized to generate individual and group images of Australian pharmacists. Collectively, 40 images were produced with DALL-E 3 for evaluation of which 30 were individual characters and the remaining 10 images were comprised of multiple characters (N = 155). All images were independently analysed by two reviewers for apparent gender, age, ethnicity, skin tone, and body habitus. Discrepancies in responses were resolved by third-observer consensus. RESULTS: Collectively for DALL-E 3, 69.7% of pharmacists were depicted as men, 29.7% as women, 93.5% as a light skin tone, 6.5% as mid skin tone, and 0% as dark skin tone. The gender distribution was a statistically significant variation from that of actual Australian pharmacists (P < .001). Among the images of individual pharmacists, DALL-E 3 generated 100% as men and 100% were light skin tone. CONCLUSIONS: This evaluation reveals the gender and ethnicity bias associated with generative AI text-to-image generation using DALL-E 3 among Australian pharmacists. Generated images have a disproportionately high representation of white men as pharmacists which is not representative of the diversity of pharmacists in Australia today.

Systematic review of MRI safety literature in relation to radiofrequency thermal injury prevention.

INTRODUCTION: Magnetic resonance imaging (MRI) is a rapidly evolving modality, generally considered safe due to lack of ionising radiation. While MRI technology and techniques are improving, many of the safety concerns remain the same as when first established. Patient thermal injuries are the most frequently reported adverse event, accounting for 59% of MRI incidents to the Food and Drug Administration (FDA). Surveys indicate many incidents remain unreported. Patient thermal injuries are preventable and various methods for their mitigation have been published. However, recommendations can be variable, fragmented and confusing. The aim of this systematic review was to synthesise the evidence on MRI safety and associated skin injuries and offer comprehensive recommendations for radiographers to prevent skin thermal injuries. METHODS: Four journal databases were searched for sources published January 2010-May 2023, presenting information on MRI safety and thermal injuries. RESULTS: Of 26,801 articles returned, after careful screening and based on the eligibility criteria, only 79 articles and an additional 19 grey literature sources were included (n = 98). Included studies were examined using thematic analysis to determine if holistic recommendations can be provided to assist in preventing skin burns. This resulted in three simplified recommendations: Remove any electrically conductive items Insulate the patient to prevent any conductive loops or contact with objects Communicate regularly CONCLUSION: By implementing the above recommendations, it is estimated that 97% of skin burns could be prevented. With thermal injuries continuing to impact MRI safety, strategies to prevent skin burns and heating are essential. Assessing individual risks, rather than blanket policies, will help prevent skin thermal injuries occurring, improving patient care.

'Scanxiety': Content analysis of pre-MRI patient experience on Instagram.

INTRODUCTION: Distress and anxiety are commonly reported during the Magnetic Resonance Imaging (MRI) experience with prior studies suggesting the pre-MRI period is a time of heightened distress. There is a paucity of literature exploring preprocedural distress and anxiety, in particular qualitative research analysing patient experience. Instagram is rapidly becoming an important social media platform though which to conduct health research. A gradually increasing number of studies have examined social media to gain insight into patient experience within medical radiation science (MRS). This study is considered as the first to explore patient experience of MRI using Instagram as a data source. METHODS: This study investigated the patient experience during the pre-MRI period by performing a content analysis on open-source Instagram posts. Ethical approval for the study was sought and approved by the Charles Sturt University, Human Research Ethics Committee. RESULTS: Six themes emerged from the extracted data; Journey to the MRI, Waiting, Anticipating the MRI procedure, Preparing for the MRI procedure, Negative interaction, and Fear of the results. CONCLUSION: The findings of this study provide novel self-reported and unsolicited insight into the diverse, multifactorial, and often concomitant nature of preprocedural MRI anxiety and distress. IMPLICATIONS FOR PRACTICE: This study adds to a growing body of literature advocating for a compassionate, holistic, and person-centered approach when caring for patients in MRI that also considers their emotional and psychological wellbeing.

Australian perspectives on artificial intelligence in medical imaging.

INTRODUCTION: While artificial intelligence (AI) and recent developments in deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. The aim of this survey was to understand the attitudes, applications and concerns among nuclear medicine and radiography professionals in Australia with regard to the rapidly emerging applications of AI. METHODS: An anonymous online survey with invitation to participate was circulated to nuclear medicine and radiography members of the Rural Alliance in Nuclear Scintigraphy and the Australian Society of Medical Imaging and Radiation Therapy. The survey invitations were sent to members via email and as a push via social media with the survey open for 10 weeks. All information collected was anonymised and there is no disclosure of personal information as it was de-identified from commencement. RESULTS: Among the 102 respondents, there was a high level of acceptance of lower order tasks (e.g. patient registration, triaging and dispensing) and less acceptance of high order task automation (e.g. surgery and interpretation). There was a low priority perception for the role of AI in higher order tasks (e.g. diagnosis, interpretation and decision making) and high priority for those applications that automate complex tasks (e.g. quantitation, segmentation, reconstruction) or improve image quality (e.g. dose / noise reduction and pseudo CT for attenuation correction). Medico-legal, ethical, diversity and privacy issues posed moderate or high concern while there appeared to be no concern regarding AI being clinically useful and improving efficiency. Mild concerns included redundancy, training bias, transparency and validity. CONCLUSION: Australian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing less complex tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation.

Imaging individuals with obesity.

Medical imaging is integral to the diagnosis and management of the co-morbidities associated with obesity. While individuals with obesity are increasingly imaged within Medical Radiation Science practice, identifying and understanding the challenges of imaging patients with obesity is an essential requirement for all Medical Radiation Practitioners (MRPs). This Continuing Professional Development article introduces key concepts related to imaging this patient group, explores technical considerations and system limitations within planar radiography, computed tomography (CT), nuclear medicine (NM), magnetic resonance imaging (MRI) and ultrasound (US) and explores patient centred care considerations when imaging patients with obesity.

COVID-19 impact on undergraduate teaching: Medical radiation science teaching team experience.

The COVID-19 crisis has caused a number of significant challenges to the higher education sector. Universities worldwide have been forced to rapidly transition to online delivery, working at home, and disruption to research while concurrently facing the longer-term impacts in institution financial reform. Here, the impact of COVID-19 on academic staff in the medical radiation science (MRS) teaching team at Charles Sturt University are explored. While COVID-19 imposes potentially the greatest challenge many of us will experience in our personal and professional lifetimes, it also affords the opportunity to objectively re-evaluate and, where appropriate, re-design learning and teaching in higher education. Technology has allowed rapid assimilation to online learning environments with additional benefits that allow flexible, mobile, agile, sustainable, culturally safe and equitable learning focussed educational environments in the post-COVID-19 "new normal".

Do MRI Patients Tweet? Thematic Analysis of Patient Tweets About Their MRI Experience.

BACKGROUND: Twitter is an online, multimedia microblogging tool used actively by millions across the world. Twitter may provide a unique insight into the magnetic resonance imaging (MRI) patient experience. METHODS: In-depth, qualitative content analysis of MRI patient tweets during one calendar month. RESULTS: Overall, 464 tweets were categorized into three themes: MRI appointment, scan experience, and diagnosis. CONCLUSIONS: This study demonstrates that MRI patients do tweet about their experiences and that Twitter is a viable platform to conduct research into patient experience within the medical radiation sciences.

Tomographic Reconstruction: A Nonmathematical Overview.

Tomographic reconstruction in single-photon-emission computed tomography (SPECT), positron emission tomography (PET), and computed tomography (CT) aim to reconstruct a three-dimensional object from a finite set of projections. Today, there are a number of approaches to tomographic reconstruction. This article aims to provide a refresher on the principles of tomographic reconstruction in SPECT, PET and CT in a nonmathematical manner. The tomographic reconstruction principles are common to SPECT, PET and CT data. The mathematical basis of analytical and iterative approaches to tomographic reconstruction is complex. This complexity may be prohibitive of a working understanding of reconstruction by medical radiation technologists; more so because of mathematical intimidation than lack of capability. Technologists can, however, develop a working knowledge from the principles and processes of reconstruction and use this understanding for decision making and problem solving.