Daniel J. Hurst, Krisha Darji, Christopher A. Bobier
Abstract Background In response to the 2022 Russian invasion of Ukraine, some scholars have advocated for pharmaceutical companies to voluntarily cease all operations and sales in Russia, including essential medicines. This proposal has been criticized on ethical and practical grounds. This study aims to understand the perspectives of global pharmaceutical companies regarding the provision of medicines following the Russian invasion of Ukraine. Methods We analyzed press releases and similar statements from the public websites of 19 global pharmaceutical companies following Russia’s invasion of Ukraine. Companies were selected based on third-party reports of top 20 global pharmaceutical companies by revenue. Data were analyzed using conventional content analysis. Two researchers with ethics expertise independently identified key concepts, assessed ethical justifications (consequentialist, deontological, or values-based), and categorized findings. Results Analysis of the press releases revealed two major themes: “scale-back” of non-essential operations in Russia (58% of companies) and a commitment to continuing to sell “essential products or medicines” (79% of companies). Companies scaling back operations often hinted at ethical justifications, including consequentialist, deontological, and values-based reasoning. The commitment to essential medicines was justified using consequentialist, deontological, and values-based reasoning, although values-based reasons were most common. Conclusion Global pharmaceutical companies have responded to the Russian invasion of Ukraine with a nuanced approach, characterized by scaling back non-essential operations while maintaining the supply of essential medicines. This approach reflects a complex interplay of ethical, practical, and geopolitical considerations. The findings highlight a shared understanding of the humanitarian imperative to ensure access to life-saving treatments, but they also raise questions about the variable articulation of ethical justifications and the potential influence of economic factors.
Mohammad Reza Rezaei, Reza Saadati Fard, Jayson L. Parker
et al.
Large Language Models (LLMs) have significantly advanced medical question-answering by leveraging extensive clinical data and medical literature. However, the rapid evolution of medical knowledge and the labor-intensive process of manually updating domain-specific resources pose challenges to the reliability of these systems. To address this, we introduce Agentic Medical Graph-RAG (AMG-RAG), a comprehensive framework that automates the construction and continuous updating of medical knowledge graphs, integrates reasoning, and retrieves current external evidence, such as PubMed and WikiSearch. By dynamically linking new findings and complex medical concepts, AMG-RAG not only improves accuracy but also enhances interpretability in medical queries. Evaluations on the MEDQA and MEDMCQA benchmarks demonstrate the effectiveness of AMG-RAG, achieving an F1 score of 74.1 percent on MEDQA and an accuracy of 66.34 percent on MEDMCQA, outperforming both comparable models and those 10 to 100 times larger. Notably, these improvements are achieved without increasing computational overhead, highlighting the critical role of automated knowledge graph generation and external evidence retrieval in delivering up-to-date, trustworthy medical insights.
Recent advances in video generation have shown remarkable progress in open-domain settings, yet medical video generation remains largely underexplored. Medical videos are critical for applications such as clinical training, education, and simulation, requiring not only high visual fidelity but also strict medical accuracy. However, current models often produce unrealistic or erroneous content when applied to medical prompts, largely due to the lack of large-scale, high-quality datasets tailored to the medical domain. To address this gap, we introduce MedVideoCap-55K, the first large-scale, diverse, and caption-rich dataset for medical video generation. It comprises over 55,000 curated clips spanning real-world medical scenarios, providing a strong foundation for training generalist medical video generation models. Built upon this dataset, we develop MedGen, which achieves leading performance among open-source models and rivals commercial systems across multiple benchmarks in both visual quality and medical accuracy. We hope our dataset and model can serve as a valuable resource and help catalyze further research in medical video generation. Our code and data is available at https://github.com/FreedomIntelligence/MedGen
Ahmad Fayaz-Bakhsh, Janice Tania, Syaheerah Lebai Lutfi
et al.
The transformative potential of artificial intelligence (AI) in medical Imaging (MI) is well recognized. Yet despite promising reports in research settings, many AI tools fail to achieve clinical adoption in practice. In fact, more generally, there is a documented 17-year average delay between evidence generation and implementation of a technology. Implementation science (IS) may provide a practical, evidence-based framework to bridge the gap between AI development and real-world clinical imaging use, to shorten this lag through systematic frameworks, strategies, and hybrid research designs. We outline challenges specific to AI adoption in MI workflows, including infrastructural, educational, and cultural barriers. We highlight the complementary roles of effectiveness research and implementation research, emphasizing hybrid study designs and the role of integrated KT (iKT), stakeholder engagement, and equity-focused co-creation in designing sustainable and generalizable solutions. We discuss integration of Human-Computer Interaction (HCI) frameworks in MI towards usable AI. Adopting IS is not only a methodological advancement; it is a strategic imperative for accelerating translation of innovation into improved patient outcomes.
Modern physics proposals present deep tensions between seemingly contradictory descriptions of reality. Views of wave-particle duality, black hole complementarity, and the Unruh effect demand explanations that shift depending on how a system is observed. However, traditional models of scientific explanation impose a fixed structure that fails to account for varying observational contexts. This paper introduces context-dependent mapping, a framework that reorganizes physical laws into self-consistent subsets structured around what can actually be observed in a given context. By doing so, it provides a principled way to integrate complementarity into the philosophy of explanation.
Es esta una obra presentada en castellano coordinada por Justo Aznar, jefe del departamento de Biopatología Clínica del Hospital Universitario La Fe, Ex director del Observatorio de Bioética y del Instituto de Ciencias de la Vida (Universidad Católica de Valencia) desarrollado en la metodología de doscientas preguntas y respuestas con la presencia de diversos especialistas que dan al trabajo la perspectiva interdisciplinar que requiere.
Medical image segmentation is a critical task in healthcare applications, and U-Nets have demonstrated promising results. This work delves into the understudied aspect of receptive field (RF) size and its impact on the U-Net and Attention U-Net architectures. This work explores several critical elements including the relationship between RF size, characteristics of the region of interest, and model performance, as well as the balance between RF size and computational costs for U-Net and Attention U-Net methods for different datasets. This work also proposes a mathematical notation for representing the theoretical receptive field (TRF) of a given layer in a network and proposes two new metrics - effective receptive field (ERF) rate and the Object rate to quantify the fraction of significantly contributing pixels within the ERF against the TRF area and assessing the relative size of the segmentation object compared to the TRF size respectively. The results demonstrate that there exists an optimal TRF size that successfully strikes a balance between capturing a wider global context and maintaining computational efficiency, thereby optimizing model performance. Interestingly, a distinct correlation is observed between the data complexity and the required TRF size; segmentation based solely on contrast achieved peak performance even with smaller TRF sizes, whereas more complex segmentation tasks necessitated larger TRFs. Attention U-Net models consistently outperformed their U-Net counterparts, highlighting the value of attention mechanisms regardless of TRF size. These novel insights present an invaluable resource for developing more efficient U-Net-based architectures for medical imaging and pave the way for future exploration. A tool is also developed that calculates the TRF for a U-Net (and Attention U-Net) model, and also suggest an appropriate TRF size for a given model and dataset.
Medical text simplification is crucial for making complex biomedical literature more accessible to non-experts. Traditional methods struggle with the specialized terms and jargon of medical texts, lacking the flexibility to adapt the simplification process dynamically. In contrast, recent advancements in large language models (LLMs) present unique opportunities by offering enhanced control over text simplification through iterative refinement and collaboration between specialized agents. In this work, we introduce the Society of Medical Simplifiers, a novel LLM-based framework inspired by the "Society of Mind" (SOM) philosophy. Our approach leverages the strengths of LLMs by assigning five distinct roles, i.e., Layperson, Simplifier, Medical Expert, Language Clarifier, and Redundancy Checker, organized into interaction loops. This structure allows the agents to progressively improve text simplification while maintaining the complexity and accuracy of the original content. Evaluations on the Cochrane text simplification dataset demonstrate that our framework is on par with or outperforms state-of-the-art methods, achieving superior readability and content preservation through controlled simplification processes.
As large language models (LLMs) develop increasingly sophisticated capabilities and find applications in medical settings, it becomes important to assess their medical safety due to their far-reaching implications for personal and public health, patient safety, and human rights. However, there is little to no understanding of the notion of medical safety in the context of LLMs, let alone how to evaluate and improve it. To address this gap, we first define the notion of medical safety in LLMs based on the Principles of Medical Ethics set forth by the American Medical Association. We then leverage this understanding to introduce MedSafetyBench, the first benchmark dataset designed to measure the medical safety of LLMs. We demonstrate the utility of MedSafetyBench by using it to evaluate and improve the medical safety of LLMs. Our results show that publicly-available medical LLMs do not meet standards of medical safety and that fine-tuning them using MedSafetyBench improves their medical safety while preserving their medical performance. By introducing this new benchmark dataset, our work enables a systematic study of the state of medical safety in LLMs and motivates future work in this area, paving the way to mitigate the safety risks of LLMs in medicine. The benchmark dataset and code are available at https://github.com/AI4LIFE-GROUP/med-safety-bench.
Ethics based auditing (EBA) is a structured process whereby an entitys past or present behaviour is assessed for consistency with moral principles or norms. Recently, EBA has attracted much attention as a governance mechanism that may bridge the gap between principles and practice in AI ethics. However, important aspects of EBA (such as the feasibility and effectiveness of different auditing procedures) have yet to be substantiated by empirical research. In this article, we address this knowledge gap by providing insights from a longitudinal industry case study. Over 12 months, we observed and analysed the internal activities of AstraZeneca, a biopharmaceutical company, as it prepared for and underwent an ethics-based AI audit. While previous literature concerning EBA has focused on proposing evaluation metrics or visualisation techniques, our findings suggest that the main difficulties large multinational organisations face when conducting EBA mirror classical governance challenges. These include ensuring harmonised standards across decentralised organisations, demarcating the scope of the audit, driving internal communication and change management, and measuring actual outcomes. The case study presented in this article contributes to the existing literature by providing a detailed description of the organisational context in which EBA procedures must be integrated to be feasible and effective.
Silvia Rodrigues dos Santos, Mirelle Finkler, Marta Verdi
A pandemia de covid-19 no Brasil contou com um número expressivo de mortes. Os marcadores sociais foram elementos indispensáveis na compreensão de seus desdobramentos. Esta pesquisa voltou seu olhar à população indígena, sabendo que ela enfrenta problemas significativos de cunho estrutural, tendo como objetivo compreender como se deu a garantia de acesso e proteção à saúde desta população em um período atravessado por evidente política anti-indígena. Para tanto, foram analisados documentos de março de 2020 a março de 2022, localizados em plataformas virtuais, incluindo as normativas publicadas pelo Poder Executivo. Com auxílio do software Atlas.Ti® realizamos Análise Temática de Conteúdo dos documentos selecionados. Os resultados evidenciaram as articulações de base dos povos originários como ponto fundamental para a proteção de suas vidas e identidades. Sob o escopo de uma Bioética Latino-americana, cunhamos possibilidades de leitura dos achados, possibilitando sua compreensão por um viés crítico-social.
Medical philosophy. Medical ethics, Business ethics
Iris Parra Jounou, Rosana Triviño-Caballero, Maite Cruz-Piqueras
Abstract Background In 2021, Spain became the first Southern European country to grant and provide the right to euthanasia and medically assisted suicide. According to the law, the State has the obligation to ensure its access through the health services, which means that healthcare professionals’ participation is crucial. Nevertheless, its implementation has been uneven. Our research focuses on understanding possible ethical conflicts that shape different positions towards the practice of Medical Assistance in Dying, on identifying which core ideas may be underlying them, and on suggesting possible reasons for this disparity. The knowledge acquired contributes to understanding its complexity, shedding light into ambivalent profiles and creating strategies to increase their participation. Methods We conducted an exploratory qualitative research study by means of semi-structured interviews (1 h) with 25 physicians and nurses from primary care (12), hospital care (7), and palliative care (6), 17 women and 8 men, recruited from Madrid, Catalonia, and Andalusia between March and May 2023. Interviews were recorded, transcribed, and coded in Atlas.ti software by means of thematic and interpretative methods to develop a conceptual model. Results We identified four approaches to MAiD: Full Support (FS), Conditioned Support (CS), Conditioned Rejection (CR), and Full Rejection (FR). Full Support and Full Rejection fitted the traditional for and against positions on MAiD. Nevertheless, there was a gray area in between represented by conditioned profiles, whose participation cannot be predicted beforehand. The profiles were differentiated considering their different interpretations of four core ideas: end-of-life care, religion, professional duty/deontology, and patient autonomy. These ideas can intersect, which means that participants' positions are multicausal and complex. Divergences between profiles can be explained by different sources of moral authority used in their moral reasoning and their individualistic or relational approach to autonomy. Conclusions There is ultimately no agreement but rather a coexistence of plural moral perspectives regarding MAiD among healthcare professionals. Comprehending which cases are especially difficult to evaluate or which aspects of the law are not easy to interpret will help in developing new strategies, clarifying the legal framework, or guiding moral reasoning and education with the aim of reducing unpredictable non-participations in MAID.
Matthias Zimmer, Daria Magdalena Czarniecki, Stephan Sahm
Abstract Background Teamwork in emergency medical services is a very important factor in efforts to improve patient safety. The potential differences of staff gender on communication, patient safety, and teamwork were omitted. The aim of this study is to evaluate these inadequately examined areas. Methods A descriptive and anonymous study was conducted with an online questionnaire targeting emergency physicians and paramedics. The participants were asked about teamwork, communication, patient safety and handling of errors. Results Seven hundred fourteen prehospital professionals from all over Germany participated. A total of 65.7% of the women harmed a patient (men 72.9%), and 52.6% were ashamed when mistakes were made (men 31.7%). 19.0% of the female participants considered their communication skills to be very good, compared to 81% of the men. More women than men did not want to appear incompetent (28.4%, 15.5%) and therefore did not speak openly about mistakes. Both genders saw the character of their colleagues as a reason for poor team communication (women 89.4%, men 84.9.%). Under high stress, communication decreased (women 35.9%, men 31.0%) and expression became inaccurate (women 18.7%, men 20.1%). Conclusions Team communication problems and teamwork in rescue services are independent of gender. Women seem to have more difficulty with open communication about mistakes because they seem to be subject to higher expectations. Work organization should be adapted to women’s needs to enable more effective error management. We conclude that it is necessary to promote a positive error and communication culture to increase patient safety.
Background: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. Purpose: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties. Methods: Our deep learning-based approaches are based on a variational Bayesian inference framework, which is implemented using two different deep neural networks based on conditional variational auto-encoder (CVAE), CVAE-dual-encoder and CVAE-dual-decoder. The conventional CVAE framework, i.e., CVAE-vanilla, can be regarded as a simplified case of these two neural networks. We applied these approaches to a simulation study of dynamic brain PET imaging using a reference region-based kinetic model. Results: In the simulation study, we estimated posterior distributions of PET kinetic parameters given a measurement of time-activity curve. Our proposed CVAE-dual-encoder and CVAE-dual-decoder yield results that are in good agreement with the asymptotically unbiased posterior distributions sampled by Markov Chain Monte Carlo (MCMC). The CVAE-vanilla can also be used for estimating posterior distributions, although it has an inferior performance to both CVAE-dual-encoder and CVAE-dual-decoder. Conclusions: We have evaluated the performance of our deep learning approaches for estimating posterior distributions in dynamic brain PET. Our deep learning approaches yield posterior distributions, which are in good agreement with unbiased distributions estimated by MCMC. All these neural networks have different characteristics and can be chosen by the user for specific applications. The proposed methods are general and can be adapted to other problems.
Medical artificial general intelligence (AGI) is an emerging field that aims to develop systems specifically designed for medical applications that possess the ability to understand, learn, and apply knowledge across a wide range of tasks and domains. Large language models (LLMs) represent a significant step towards AGI. However, training cross-domain LLMs in the medical field poses significant challenges primarily attributed to the requirement of collecting data from diverse domains. This task becomes particularly difficult due to privacy restrictions and the scarcity of publicly available medical datasets. Here, we propose Medical AGI (MedAGI), a paradigm to unify domain-specific medical LLMs with the lowest cost, and suggest a possible path to achieve medical AGI. With an increasing number of domain-specific professional multimodal LLMs in the medical field being developed, MedAGI is designed to automatically select appropriate medical models by analyzing users' questions with our novel adaptive expert selection algorithm. It offers a unified approach to existing LLMs in the medical field, eliminating the need for retraining regardless of the introduction of new models. This characteristic renders it a future-proof solution in the dynamically advancing medical domain. To showcase the resilience of MedAGI, we conducted an evaluation across three distinct medical domains: dermatology diagnosis, X-ray diagnosis, and analysis of pathology pictures. The results demonstrated that MedAGI exhibited remarkable versatility and scalability, delivering exceptional performance across diverse domains. Our code is publicly available to facilitate further research at https://github.com/JoshuaChou2018/MedAGI.
Artificial Intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions.
En el presente artículo se describirá y analizará, mediante una revisión de la literatura, el transhumanismo, una compleja corriente de pensamiento cuyo proyecto implica la fusión del individuo y la máquina.
Tras un primer breve análisis sobre los orígenes del transhumanismo, se investiga la vinculación cada vez más fuerte de los paradigmas antropocéntrico y tecnocéntrico, con el fin de comprender, a través de la figura del cíborg, cómo el progreso actual de las Tecnologías de la Información y la Comunicación (TIC) y la Inteligencia Artificial (IA) insinúan un alto nivel de penetración en lo humano. Se analizan las cuestiones éticas que plantean los procesos de hibridación en la actualidad. Asimismo, se presta especial interés a la exploración de la polarización existente entre conservadores y progresistas, con referencia a sus respectivas formas de interpretar la relación entre el sujeto y la tecnología.
Abstract Background Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorphic encryption allows computations to be performed on data without this data ever being decrypted. Therefore, homomorphic encryption represents a potential solution for conducting feasibility studies on cohorts of sensitive patient data stored in distributed locations. Distributed ledger technology provides a permanent record on all transfers and processing of patient data, allowing data custodians to audit access. A significant portion of the current literature has examined how these technologies might comply with data protection and research ethics frameworks. In the Swiss context, these instruments include the Federal Act on Data Protection and the Human Research Act. There are also institutional frameworks that govern the processing of health related and genetic data at different universities and hospitals. Given Switzerland’s geographical proximity to European Union (EU) member states, the General Data Protection Regulation (GDPR) may impose additional obligations. Methods To conduct this assessment, we carried out a series of qualitative interviews with key stakeholders at Swiss hospitals and research institutions. These included legal and clinical data management staff, as well as clinical and research ethics experts. These interviews were carried out with two series of vignettes that focused on data discovery using homomorphic encryption and data erasure from a distributed ledger platform. Results For our first set of vignettes, interviewees were prepared to allow data discovery requests if patients had provided general consent or ethics committee approval, depending on the types of data made available. Our interviewees highlighted the importance of protecting against the risk of reidentification given different types of data. For our second set, there was disagreement amongst interviewees on whether they would delete patient data locally, or delete data linked to a ledger with cryptographic hashes. Our interviewees were also willing to delete data locally or on the ledger, subject to local legislation. Conclusion Our findings can help guide the deployment of these technologies, as well as determine ethics and legal requirements for such technologies.