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Current Radiology workforce perspective on the integration of artificial intelligence in clinical practice: A systematic review
Get PDFIntroduction: Artificial Intelligence (AI) represents the application of computer systems to tasks traditionally performed by humans. The medical imaging profession has experienced a transformative shift through the integration of AI. While there have been several independent primary studies describing various aspects of AI, the current review employs a systematic approach towards describing the perspectives of radiologists and radiographers about the integration of AI in clinical practice. This review provides a holistic view from a professional standpoint towards understanding how the broad spectrum of AI tools are perceived as a unit in medical imaging practice. Methods: The study utilised a systematic review approach to collect data from quantitative, qualitative, and mixed-methods studies. Inclusion criteria encompassed articles concentrating on the viewpoints of either radiographers or radiologists regarding the incorporation of AI in medical imaging practice. A stepwise approach was employed in the systematic search across various databases. The included studies underwent quality assessment using the Quality Assessment Tool for Studies with Diverse Designs (QATSSD) checklist. A parallel-result convergent synthesis approach was employed to independently synthesise qualitative and quantitative evidence and to integrate the findings during the discussion phase. Results: Forty-one articles were included, all of which employed a cross-sectional study design. The main findings were themed around considerations and perspectives relating to AI education, impact on image quality and radiation dose, ethical and medico-legal implications for the use of AI, patient considerations and their perceived significance of AI for their care, and factors that influence development, implementation and job security. Despite varying emphasis, these themes collectively provide a global perspective on AI in medical imaging practice. Conclusion: While expertise levels are varied and different, both radiographers and radiologists were generally optimistic about incorporation of AI in medical imaging practice. However, low levels of AI education and knowledge remain a critical barrier. Furthermore, equipment errors, cost, data security and operational difficulties, ethical constraints, job displacement concerns and insufficient implementation efforts are integration challenges that should merit the attention of stakeholders
Effect of short and long-term cadmium exposure on behaviour and cardiac function in Drosophila.
Get PDFCadmium is a non-essential heavy metal and environmental pollutant that causes a range of pathologies across different species. In humans, cadmium exposure has recently been directly linked to heart disease. Understanding how long-term cadmium exposure affects cardiac physiology is therefore important. In this work we employed a tractable Drosophila melanogaster model to study the effects of cadmium exposure on behaviour, lifespan and cardiac physiology. Dietary experiments established that cadmium at 10 μM and 100 μM was tolerated for several weeks, whereas doses in the mM range caused lethality within days. It was estimated that 10 μM dietary exposure represented an approximately 60-fold excess of the maximum exposure recommended for humans. Although 10 μM cadmium had no impact on lifespan compared to the control diet, it did cause significant daytime hyperactivity. Direct exposure of the heart to cadmium caused reversible cardiac arrest and disrupted calcium signalling. Compared to controls, 10 μM dietary cadmium had no impact on the rate of cardiac ageing over a six-week period. The higher dose of 100 μM shortened the flies' lifespan but it slowed the rate of cardiac ageing. The findings indicate that Drosophila can be used to model the direct effects of cadmium on cardiomyocyte function and also demonstrate the existence of cardioprotective pathways triggered by dietary cadmium exposure. The data also indicate that cadmium at doses that do not affect lifespan or heart function, do cause daytime hyperactivity. Identifying and the cardioprotective mechanisms and understanding the hyperactivity phenotype in Drosophila may yield important findings of applied relevance to insects in general, as well as humans exposed to cadmium
Conclusions and Predictions for the Future of Online Safety Policy
Get PDFThis concluding chapter reflects on the systemic flaws and persistent challenges within online safety policy, particularly regarding its alignment with young people’s needs and experiences. It critiques the policy path dependency and regulatory isomorphism that favour punitive and platform-centric approaches over proactive, evidence-based, and youth-centred strategies. The chapter highlights the inefficacy of current frameworks, such as the UK’s Online Safety Act 2023, that emphasize stringent platform liability while failing to address the complex causes of online harm. Drawing from youth voices and historical insights, the chapter advocates for a shift towards a holistic model based on ecological systems theory, engaging stakeholders across the child’s microsystem to macrosystem in collaborative, educational, and supportive roles. Ultimately, it calls for a progressive paradigm in online safety policy that prioritizes harm reduction, digital literacy, and the active participation of young people, positioning their welfare and voices at the centre of future interventions
Using a range of recruitment strategies to recruit those who use anabolic androgenic steroids
Get PDFCollecting data from people who use substances can be challenging. As many now have communities in the digital realm, recruitment via online networks has become a popular method with hard-to-reach participants. This paper seeks to share the pros and cons of different types of recruitment (both online and non-virtually) for those who choose to use androgenic anabolic steroids. Method: This case study highlights positive and negative factors to consider for questionnaire design and dissemination via social media platforms and traditional methods. Findings and Results: Key lessons with regards to online dissemination via social media include the benefits of using specialist hashtags, access to specialist online fora with respite gatekeeper endorsement, awareness of the impact on reputation for those asked to share, and anonymity. Questionnaire design findings emphasise the importance of piloting the questionnaire, giving consideration to how participants from specific populations may react to the way questions are phrased, and the need for awareness of specific sensitivities of topic of the targeted demographic sub-group. This paper will help sociologists and other social and behavioural science researchers who wish to consider and evaluate their own distribution methods for data collection when it comes to seeking out more hard-to-reach groups
Implementation and service evaluation of a Collaborative Learning in Practice (CLiP) placement pilot for Physiotherapy students in an acute hospital setting
Get PDFThis paper presents the implementation and service evaluation of a Collaborative Learning in Practice (CLiP) pilot placement for physiotherapy students on an acute ward in a secondary care district hospital. The pilot involved two second-year physiotherapy students and two practice educators, and ran alongside an established nursing CLiP placement over six weeks. Student feedback was collected via a post-placement questionnaire comprising of open and closed questions, while informal feedback from practice educators was gathered through emails and meetings. Findings indicated that the CLiP model supported the development of key competencies, including autonomy, caseload management, person-centred care, and communication. Challenges were noted around initial preparation and adaptation to the model. This small-scale pilot offered a physiotherapy-specific example of the CLiP model, addressing a gap in the literature. While appropriate setting, training, and preparation appeared critical to success, findings should be interpreted with caution due to the small sample size (n=2). Further research is needed to assess broader effectiveness and scalability
Using decoder-based distillation for enhancing multilingual clinical case report summarization
Get PDFAutomatic summarization of clinical reports represent an important field of studies that contribute to shortening long textual narratives written in various languages. Effective report summarization poses numerous challenges, including density of medical terms mentions, semantic interdependency among mentioned entities. The most recent advances of instruction-tuned models illustrate promising capabilities of models at various scale across numerous fields of Natural Language Processing, including textual summarization. A hybrid teacher-student distillation process leverages the power of knowledge distillation by transferring knowledge from a large model (teacher) to a smaller model (student). To our best knowledge, numerous existing studies broadly exploit Seq2seq models. Despite their effectiveness for dialogues and summarization of short texts, such techniques have not become common for supporting multilingual and long input contexts. To bridge the gap in exploring distillation tuning, this paper proposes an adaptation of the teacher-student framework for decoder based systems. In this paper, we experiment with a teacher-student framework for summarising clinical case reports. We adopt the Qwen2.5 models family and evaluate our setup on the MultiClinSumsmall dataset. We demonstrate that fine-tuning the 0.5B model with the knowledge transferred from the 72B model results in 2.4%-4% performance increment by Rouge metrics compared to the conventional fine-tuning process, highlighting our model’s practical benefits in clinical information processing. Our framework is publicly available: https://github.com/nicolay-r/ distil-tuning-ll
Enhancing medical dialogue summarization: A MediExtract Distillation Framework
Get PDFAutomatic summarization of medical dialogues, which converts colloquial doctor-patient conversations into concise notes, is increasingly important due to the growing complexity of healthcare data. However, the complexity of medical language and the lack of annotated datasets pose challenges for summarization models. In this paper, we propose a MediExtract Distillation Framework (MEDF), a novel hybrid teacher-student distillation process that leverages the power of Large Language Models (LLMs) in information capturing to enhance the performance of a smaller student model. Utilizing medical key information generated by GPT-3.5-Turbo, the model training involves two feedforward branches per iteration: one using ground truth as labels and another using generated structured medical key information as an auxiliary supervision. We validated our method on the MTS-Dialogue dataset, achieving a +2.1% improvement in BLEURT compared to previous methods, demonstrating its effectiveness in summarizing medical dialogues. Additionally, using UMLS-based BERTScore, we observed a +1.8% increase in MedBERTScore for medical term extraction, highlighting our model's practical benefits in clinical information processing. Our framework is publicly available at: https://github.com/Xiaoxiao-Liu/distill-d2n.gi
Impact of Patriarchy on the Evolving Roles of Millennial Chinese Women
No full textThis study investigates the pervasive impact of patriarchy on the lives and roles of millennial Chinese women. Through a qualitative methodology incorporating in-depth, semi-structured online interviews with 16 participants, the research uncovers how rooted patriarchal and Confucian values continue to influence these contemporary women’s educational, career, and personal life choices. Despite some societal changes and a push towards gender equality, findings indicate that traditional norms remain influential, often reinforced through familial pressures rooted in the Confucian philosophy of “filial piety.” Participants expressed a common experience of gender biases and discrimination, both within their families and broader societal structures, such as in educational and workplace settings. These biases manifest as a preference for male offspring and greater societal expectations placed upon women in their familial and marital roles. The study highlights that while millennial Chinese women are increasingly aware of and resist patriarchal norms, they often adopt coping strategies that blend resistance with compliance with societal expectations. The findings underscore a critical need for a broader societal shift towards genuine gender equality. The study advocates for multifaceted approaches that involve educational reforms and cultural negotiations to dismantle entrenched patriarchal structures. It calls for empowering women through increased awareness and education about gender equality as essential steps toward mitigating the impact of patriarchy and advancing women’s rights in contemporary China
Unlocking the data dungeons of higher education
Get PDFHigher Education in the UK operates under a regulatory framework, the Office for Students (OfS). The university sector need to report on a series of data metrics, where education has been quantified into measurable outcomes. These are focused on continuation and completion of studies, as well as readiness and ability to secure professional work. However, recent literature has documented the complexity of computing metrics in an environment in which universities are in constant transition and adaptation, and how these adaptive processes impact student transitions, including from university to graduate work. Thus collecting data with precision and fair statistical assessment of outcomes across the sector remain a challenge for the government and the sector alike.
Learning analytics is a highly contested field which is implemented and used in very different ways across the sector. In some cases, the collection of data places greater emphasis on institutional compliance for reputational protection and as a tool for data driven narrative creation. At its most effective, it places the learner at the centre of the process and as the primary audience for its output. Emerging trends point to how it is increasingly embedded within day-to-day activities that encompass learners, educators and the institution. Our research indicates that it is most impactful when it supports data-informed pedagogic interventions.
Our research suggests three broad responses to data collection, collation and interpretations, where the institutional data gathered is actioned through very different strategic lenses. Best practice seeks to use this data to inform strategic and operational decisions; and to focus on the student experience, with a clear pedagogic rationale underpinning the sharing of data, that genuinely moves the student learning journey forward. The use and role of data can be characterised as a tool to defend the institution from external scrutiny; an intrinsic tool to inform course development or as instigator for dialogue (including self dialogue) by the learner.
The learning design that frames and encompasses learning analytics impacts significantly on the user. It can be cold, dehumanising and context free with the data stored in what we terms as a ‘data dungeon’. It can be interpreted as a ‘data engine room’ driving forward the curricula and learning agenda; it can, we argue, at its cutting edge frame ‘data dialogue’; shining the light into the data dungeon. This paper will draw upon these themes and suggest a maturity model to ensure the data collected has meaning, use and value and contributes to a greater understanding of the measurement and understanding of learning gain
The Role of Social Marketing by the IFPRI in Influencing Sustainable Agriculture Policies in Nigeria
Get PDFThe urgent need to mitigate climate change has prompted international NGOs to utilise social marketing to influence sustainable agricultural policies in developing countries. In Nigeria, the International Food Policy Research Institute (IFPRI) enhances its stakeholders' knowledge about sustainable agriculture and influence their policymaking decisions through research publications, training and workshops, and conferences. However, the research institute struggles to measure the long-term impact of its efforts due to the lack of marketing communication guidelines. Drawing on a qualitative content analysis, this study investigates IFPRI’s utilisation of social marketing and proposes a Social Marketing Planning Guide (SMPG) for the research institute to strengthen its programmes, effectively influence sustainable agriculture policies, and create metrics to measure its influence on policymaking. Ultimately, this study contributes to understanding the interconnectedness between climate change, policymaking, and social marketing, offering insights for industry experts and NGOs to enhance their social intervention strategies in addressing climate change