Dylan Powell

Drone technologies offer significant promise for enhancing medical logistics, particularly in rural and remote areas. However, despite several pilot trials across the UK, there remains a lack of evidence regarding patient perceptions of drone integration in routine healthcare delivery. This study aimed to explore patient perspectives on the use of drones in medical logistics, with a focus on cost, safety, and impact on quality of life amongst three medical areas (chemotherapy, lab diagnostics and blood products). A qualitative study using semi-structured interviews and small focus groups was conducted with 26 participants from five Scottish health boards. Participants were recruited via NHS engagement leads, community networks, and snowball sampling. Data were collected via Microsoft Teams and analysed thematically using an inductive approach. Eight key themes emerged: Acceptance, Knowledge of drones, Potential use within the NHS, Potential impact, Concerns, Public perception, Support networks, and Future innovation. Participants expressed cautious optimism, highlighting potential benefits including improved access in rural areas, reduced travel burdens, and environmental gains. Concerns included technical reliability, regulatory complexity, data privacy, and the risk of increasing health inequities. Drones were viewed by patients as a promising complement to existing medical logistics, especially for emergency and rural applications. However, successful integration demands transparent communication, co-design with stakeholders, robust regulatory planning, and sustainable funding models to ensure equitable and safe implementation.

We propose a novel framework and measurement model that frames post-traumatic stress disorder (PTSD) in the context of brain health capital, utilising advances in smart devices (wearables, phones) and AI to better capture the multimodal physiological, behavioural, and cognitive features of PTSD. Patients would be provided with complex information in a meaningful way that could provide a unique opportunity to identify personal change mechanisms and support empowered, patient-led decision-making.

Virtual interviews have now become standard practice in health professions training admissions following the COVID-19 pandemic, but the advent of generative AI technologies has raised concerns about the fairness and integrity of such practices. Admissions programs are responding with AI detection software, increased proctoring, and outright bans, all of which are difficult to enforce. A recent randomized controlled trial by Eva and colleagues examining Generative AI tool use among applicants to a medical school during virtual Multiple Mini‑Interviews (MMIs) suggests a different solution: good interview structure may be more resistant to AI advantage, and minor modifications can limit AI use without compromising reliability,authenticity or acceptability. Reframing generative AI as a design problem rather than a detection problem may also help align integrity, equity, and learning values.

Atrial fibrillation (AF) affects over 50 million people worldwide and carries substantial downstream morbidity, mortality, and cost. Yet many contemporary screening programs rely primarily on age thresholds—an approach that is operationally simple but can be imprecise for identifying near-term risk. AI applied to handheld single-lead ECGs can predict incident AF with accuracy similar to established clinical risk scores, but real-world deployment remains limited by signal noise, workflow complexity, and unclear risk thresholds.

Clinical artificial intelligence (AI) promises to enhance patient care, streamline workflows, and improve health outcomes. AI tools have increasingly been integrated into a variety of healthcare settings. Despite rapid adoption, there is limited understanding of which leadership and organizational paradigms can best support effective AI implementation in clinical settings. This article examines frameworks of organizational culture and leadership structures that can help guide the advancement of clinical AI.

Cancer care generates vast quantities of data including clinical records, pathology images, radiology scans, and molecular profiles, yet these modalities are rarely integrated in a systematic, automated manner within routine clinical workflows, remaining largely siloed across separate departmental and technical systems. Foundation model-driven embeddings—or numerical representations (vectors) that summarize complex data such as text, images, and molecular profiles —offer a framework to integrate these data streams into unified patient representations. Here we examine the HONeYBEE platform’s approach to multimodal integration in oncology, situate it within broader developments in representation learning, and clinical and technical challenges that may shape its path to implementation1.

Emerging evidence suggests generative artificial intelligence (AI) may offer potential for autoimmune and rheumatic disease care, moving beyond traditional narrow AI applications to produce contextualized clinical content to support a wide spectrum of medical tasks. This article explores generative AI applications across autoimmune and rheumatologic clinical care, research, and administrative domains. However, significant implementation challenges remain, including clinical validation, model interpretability, data integration complexities, and evolving regulatory frameworks.

Healthcare communication faces unprecedented challenges as the healthcare workforce is increasingly faced with increased administrative burdens and reduced time with patients. Conversational agents powered by generative AI may offer a potential solution by collecting information, answering questions, documenting encounters, and supporting clinical decision-making through fluid, contextual dialogue. However, realizing their potential requires rigorous validation, careful implementation, and a strong commitment to safety, equity, and preserving human-centered care.

Cognitive bias accounts for a significant portion of preventable errors in healthcare, contributing to significant patient morbidity and mortality each year. As large language models (LLMs) are introduced into healthcare and clinical decision-making, these systems are at risk of inheriting – and even amplifying – these existing biases. This article explores both the cognitive biases impacting LLM-assisted medicine and the countervailing strengths these technologies bring to addressing these limitations.