Hasil untuk "Medical legislation"

Emma Duchesne, Alison N. Ross, Jane Lewis et al.

Abstract Background Intimate partner violence (IPV) is prevalent among patients visiting the emergency department (ED). Studies show that patients experiencing IPV continue to have negative care experiences in the ED, leading to an increased risk of adverse physical, mental, legal, and economic outcomes. However, few studies explore ED care metrics and gaps in knowledge on how providers can improve. Methods We sought to fill these gaps by collecting quantitative and qualitative electronic data on ED care parameters for patients experiencing IPV. A retrospective chart review was conducted for patients seen in our tertiary care center’s ED and by our Sexual Assault and Domestic Violence Program between December 17, 2018 and June 16, 2021. Quantitative data, including sociodemographics, were summarized using medians/interquartile ranges and frequencies/proportions as appropriate using SPSS. This paper describes IPV care metrics across three domains: (1) ED mandatory reporting, (2) medical management of strangulation, and (3) discharge diagnosis containing IPV. Additionally, when documenting IPV encounters in charts, the use of trauma- and violence-informed care (TVIC) principles was evaluated as a secondary exploratory outcome. Results A total of 124 clinical encounters were analyzed. Among these, 54 involved children in the home, and documentation of mandatory reporting was absent in 43% (23/54) of such cases. Twenty-five patients experienced strangulation; however, 88% (22/25) of these cases were inadequately investigated. Furthermore, IPV was omitted as a discharge diagnosis in 38% (47/124) of encounters. Overall, 64% (79/124) of charts demonstrated a lack of trauma- and violence-informed care (TVIC) principles in the documentation of IPV-related encounters. Conclusions These findings highlight that gaps exist for ED patients experiencing IPV and illuminate areas for improvement of clinical care. We provide evidence-based recommendations for ED providers to improve their management of IPV, including review of mandatory reporting legislation, overview of clinical criteria requiring contrast imaging for strangulation, and discussion around the significance of including IPV in ED discharge diagnosis. Clinical trial number Not applicable.

C. Charles, T. Whelan, A. Gafni

Erica Simone Barbosa Dantas, Yansy Aurora Delgado Orrillo, Romário Correia dos Santos et al.

O direito à saúde no Brasil, definido na Constituição de 1988, é garantido e fortalecido por meio de uma ampla estrutura de participação e controle social. Mudanças estruturais transcendentes, como a transformação digital de muitos aspectos do Sistema Único de Saúde, podem ser poderosas ferramentas de ampliação do acesso e da equidade nos serviços, porém, ao mesmo tempo, podem criar novas exclusões por barreiras geográficas, tecnológicas e de letramento digital. Esta pesquisa buscou explorar, a partir de entrevistas, a visão de representantes do campo do controle social no Sistema Único de Saúde a respeito do processo de transformação digital da saúde. Os resultados mostram que o Programa SUS Digital vem mudando positivamente o acesso aos serviços, mas que a participação social ainda não foi completamente incluída e que a transformação digital é percebida como um processo da gestão com ausência de controle social.

Manuela Cortez da Cunha Cruz, Ana Maria Malik

Este artigo explora as implicações da digitalização da saúde para o acesso das mulheres a serviços de saúde, com foco na exclusão digital, equidade de gênero e interseccionalidade. Por meio de uma revisão integrativa da literatura, realizada em quatro bases bibliográficas, foram selecionados e analisados 22 artigos que abordam os temas de saúde digital, gênero e exclusão digital. Os resultados mostraram disparidades significativas entre o Norte e o Sul Global. O artigo também destaca lacunas importantes na literatura, como a ausência de pesquisas focadas no Sul Global, a relação entre saúde digital e o trabalho de cuidado não remunerado e as questões de proteção de dados, especialmente para mulheres em situações de vulnerabilidade. A interseccionalidade é apresentada como uma ferramenta crítica para entender como opressões interligadas agravam as desigualdades no acesso à saúde digital. A pesquisa traz contribuições ao campo ao propor uma agenda que privilegie contextos marginalizados e promova soluções digitais mais inclusivas. A conclusão reforça a necessidade de políticas públicas que enfrentem barreiras estruturais e promovam a literacia digital, proteção de dados e equidade de gênero, para que os benefícios da digitalização sejam amplamente distribuídos.

Srinagesh Mannekote Thippaiah, Jayasudha G. Gude

In recent years, cannabis has sparked both curiosity and controversy due to its purported benefits and risks. Cannabis remains the most widely used drug globally, and its use is increasing, possibly due to greater access, changing cultural perceptions, and evolving legislation. The National Household Survey of Drugs in India reveals that approximately 3% of the Indian population are current cannabis users. Across 16 Asian countries, cannabis use prevalence ranges from 0.30% to 19.10%. Cannabis continues to be a widely used recreational drug in India and across Asia. Notably, Thailand became the first country in Asia to legalize the use and purchase of cannabis leaves in February 2021, followed by the legalization of the whole plant in June 2022. While India’s Narcotic Drugs and Psychotropic Substances Act of 1985 permits cannabis use for medical and scientific research, recreational use remains prohibited. There are concerns about the potential gateway effect of cannabis use, which could lead to the consumption of more harmful substances. With cannabis legalization spreading globally and its influence extending to Asian countries, it is crucial to evaluate whether the perceived benefits outweigh the risks. In fact, multiple petitions have been filed in High Courts advocating for the decriminalization of cannabis use, highlighting its medicinal, industrial, ecological, and economic potential. However, it is important to assess with longitudinal research the potential benefits and risks of recreational cannabis use in Indian and Asian countries. The current evidence does not strongly support significant health benefits. However, factors such as social perception, economic considerations, religious beliefs, and cannabis’s cultural role make it difficult to clearly determine whether the benefits outweigh the risks.

Inga Abed, Vanessa Bennett, Brendan Cuddy et al.

Shortages of medicines are a global public health challenge with a significant impact on patient care. While the issue is at the top of the agenda of EU policymakers, regulators and healthcare providers, shortages are a complex problem with many contributing factors requiring multifaceted solutions. This article is a reflection of the work EMA and network of EU regulatory agencies for medicines carried out since 2012, in particular its taskforce, set up in 2016 to provide strategic and structural solutions to tackle shortages in the EU. Since its inception, the taskforce played a key role in spearheading activities related to medicines’ shortages. Members of the taskforce supported important initiatives from leading the work on critical medicines to laying the foundation for Regulation (EU) 2022/123, which reinforces EMA’s role in crisis preparedness and management of medicinal products and medical devices. On 18 December 2024 the taskforce reached the end of its mandate. Its work is now integrated into EMA and HMA’s core activities. Future activities such as the implementation of shortage prevention plans will be further defined by measures in the revised pharmaceutical legislation, and the proposed Critical Medicines Act.

Dawei Li, Zijian Gu, Peng Wang et al.

Multimodal large language models (MLLMs) have shown strong potential for medical image reasoning, yet fairness across demographic groups remains a major concern. Existing debiasing methods often rely on large labeled datasets or fine-tuning, which are impractical for foundation-scale models. We explore In-Context Learning (ICL) as a lightweight, tuning-free alternative for improving fairness. Through systematic analysis, we find that conventional demonstration selection (DS) strategies fail to ensure fairness due to demographic imbalance in selected exemplars. To address this, we propose Fairness-Aware Demonstration Selection (FADS), which builds demographically balanced and semantically relevant demonstrations via clustering-based sampling. Experiments on multiple medical imaging benchmarks show that FADS consistently reduces gender-, race-, and ethnicity-related disparities while maintaining strong accuracy, offering an efficient and scalable path toward fair medical image reasoning. These results highlight the potential of fairness-aware in-context learning as a scalable and data-efficient solution for equitable medical image reasoning.

Paul Zaha, Lars Böcking, Simeon Allmendinger et al.

Medical image segmentation is crucial for disease diagnosis and treatment planning, yet developing robust segmentation models often requires substantial computational resources and large datasets. Existing research shows that pre-trained and finetuned foundation models can boost segmentation performance. However, questions remain about how particular image preprocessing steps may influence segmentation performance across different medical imaging modalities. In particular, edges-abrupt transitions in pixel intensity-are widely acknowledged as vital cues for object boundaries but have not been systematically examined in the pre-training of foundation models. We address this gap by investigating to which extend pre-training with data processed using computationally efficient edge kernels, such as kirsch, can improve cross-modality segmentation capabilities of a foundation model. Two versions of a foundation model are first trained on either raw or edge-enhanced data across multiple medical imaging modalities, then finetuned on selected raw subsets tailored to specific medical modalities. After systematic investigation using the medical domains Dermoscopy, Fundus, Mammography, Microscopy, OCT, US, and XRay, we discover both increased and reduced segmentation performance across modalities using edge-focused pre-training, indicating the need for a selective application of this approach. To guide such selective applications, we propose a meta-learning strategy. It uses standard deviation and image entropy of the raw image to choose between a model pre-trained on edge-enhanced or on raw data for optimal performance. Our experiments show that integrating this meta-learning layer yields an overall segmentation performance improvement across diverse medical imaging tasks by 16.42% compared to models pre-trained on edge-enhanced data only and 19.30% compared to models pre-trained on raw data only.

Xin Wang, Yin Guo, Jiamin Xia et al.

Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by source-target alignment, or the source-free setting, which typically resorts to implicit adaptation mechanisms such as pseudo-labeling and network distillation. This substantial divergence in methodological designs between the two settings reveals an inherent flaw: the lack of an explicit, structured construction of anatomical knowledge that naturally generalizes across domains and settings. To bridge this longstanding divide, we introduce a unified, semantically grounded framework that supports both source-accessible and source-free adaptation. Fundamentally distinct from all prior works, our framework's adaptability emerges naturally as a direct consequence of the model architecture, without relying on explicit cross-domain alignment strategies. Specifically, our model learns a domain-agnostic probabilistic manifold as a global space of anatomical regularities, mirroring how humans establish visual understanding. Thus, the structural content in each image can be interpreted as a canonical anatomy retrieved from the manifold and a spatial transformation capturing individual-specific geometry. This disentangled, interpretable formulation enables semantically meaningful prediction with intrinsic adaptability. Extensive experiments on challenging cardiac and abdominal datasets show that our framework achieves state-of-the-art results in both settings, with source-free performance closely approaching its source-accessible counterpart, a level of consistency rarely observed in prior works. The results provide a principled foundation for anatomically informed, interpretable, and unified solutions for domain adaptation in medical imaging. The code is available at https://github.com/wxdrizzle/remind

Pak-Hei Yeung, Jayroop Ramesh, Pengfei Lyu et al.

This paper explores the transfer of knowledge from general vision models pretrained on 2D natural images to improve 3D medical image segmentation. We focus on the semi-supervised setting, where only a few labeled 3D medical images are available, along with a large set of unlabeled images. To tackle this, we propose a model-agnostic framework that progressively distills knowledge from a 2D pretrained model to a 3D segmentation model trained from scratch. Our approach, M&N, involves iterative co-training of the two models using pseudo-masks generated by each other, along with our proposed learning rate guided sampling that adaptively adjusts the proportion of labeled and unlabeled data in each training batch to align with the models' prediction accuracy and stability, minimizing the adverse effect caused by inaccurate pseudo-masks. Extensive experiments on multiple publicly available datasets demonstrate that M&N achieves state-of-the-art performance, outperforming thirteen existing semi-supervised segmentation approaches under all different settings. Importantly, ablation studies show that M&N remains model-agnostic, allowing seamless integration with different architectures. This ensures its adaptability as more advanced models emerge. The code is available at https://github.com/pakheiyeung/M-N.

Hao Xu, Tengfei Xue, Jianan Fan et al.

Medical image registration is a fundamental task in medical image analysis, aiming to establish spatial correspondences between paired images. However, existing unsupervised deformable registration methods rely solely on intensity-based similarity metrics, lacking explicit anatomical knowledge, which limits their accuracy and robustness. Vision foundation models, such as the Segment Anything Model (SAM), can generate high-quality segmentation masks that provide explicit anatomical structure knowledge, addressing the limitations of traditional methods that depend only on intensity similarity. Based on this, we propose a novel SAM-assisted registration framework incorporating prototype learning and contour awareness. The framework includes: (1) Explicit anatomical information injection, where SAM-generated segmentation masks are used as auxiliary inputs throughout training and testing to ensure the consistency of anatomical information; (2) Prototype learning, which leverages segmentation masks to extract prototype features and aligns prototypes to optimize semantic correspondences between images; and (3) Contour-aware loss, a contour-aware loss is designed that leverages the edges of segmentation masks to improve the model's performance in fine-grained deformation fields. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods across multiple datasets, particularly in challenging scenarios with complex anatomical structures and ambiguous boundaries. Our code is available at https://github.com/HaoXu0507/IPMI25-SAM-Assisted-Registration.

Jukka Ruohonen, Jesper Løffler Nielsen, Jakub Skórczynski

The European Union (EU) has long favored a risk-based approach to regulation. Such an approach is also used in recent cyber security legislation enacted in the EU. Risks are also inherently related to compliance with the new legislation. Objective: The paper investigates how risks are framed in the EU's five core cyber security legislative acts, whether the framings indicate convergence or divergence between the acts and their risk concepts, and what qualifying words and terms are used when describing the legal notions of risks. Method : The paper's methodology is based on qualitative legal interpretation and taxonomy-building. Results: The five acts have an encompassing coverage of different cyber security risks, including but not limited to risks related to technical, organizational, and human security as well as those not originating from man-made actions. Both technical aspects and assets are used to frame the legal risk notions in many of the legislative acts. A threat-centric viewpoint is also present in one of the acts. Notable gaps are related to acceptable risks, non-probabilistic risks, and residual risks. Conclusion: The EU's new cyber security legislation has significantly extended the risk-based approach to regulations. At the same time, complexity and compliance burden have increased. With this point in mind, the paper concludes with a few practical takeaways about means to deal with compliance and research it.

Wenjing Lu, Yi Hong, Yang Yang

Vision foundation models have demonstrated strong generalization in medical image segmentation by leveraging large-scale, heterogeneous pretraining. However, they often struggle to generalize to specialized clinical tasks under limited annotations or rare pathological variations, due to a mismatch between general priors and task-specific requirements. To address this, we propose Uncertainty-informed Collaborative Learning (UnCoL), a dual-teacher framework that harmonizes generalization and specialization in semi-supervised medical image segmentation. Specifically, UnCoL distills both visual and semantic representations from a frozen foundation model to transfer general knowledge, while concurrently maintaining a progressively adapting teacher to capture fine-grained and task-specific representations. To balance guidance from both teachers, pseudo-label learning in UnCoL is adaptively regulated by predictive uncertainty, which selectively suppresses unreliable supervision and stabilizes learning in ambiguous regions. Experiments on diverse 2D and 3D segmentation benchmarks show that UnCoL consistently outperforms state-of-the-art semi-supervised methods and foundation model baselines. Moreover, our model delivers near fully supervised performance with markedly reduced annotation requirements.

R. Solimini, F. Busardò, Filippo Gibelli et al.

Background and objective: Telemedicine or telehealth services has been increasingly practiced in the recent years. During the COVID-19 pandemic, telemedicine turned into and indispensable service in order to avoid contagion between healthcare professionals and patients, involving a growing number of medical disciplines. Nevertheless, at present, several ethical and legal issues related to the practice of these services still remain unsolved and need adequate regulation. This narrative review will give a synthesis of the main ethical and legal issues of telemedicine practice during the COVID-19 pandemic. Material and Methods: A literature search was performed on PubMed using MeSH terms: Telemedicine (which includes Mobile Health or Health, Mobile, mHealth, Telehealth, and eHealth), Ethics, Legislation/Jurisprudence, and COVID-19. These terms were combined into a search string to better identify relevant articles published in the English language from March 2019 to September 2021. Results: Overall, 24 out of the initial 85 articles were considered eligible for this review. Legal and ethical issues concerned important aspects such as: informed consent (information about the risks and benefits of remote therapy) and autonomy (87%), patient privacy (78%) and confidentiality (57%), data protection and security (74%), malpractice and professional liability/integrity (70%), equity of access (30%), quality of care (30%), the professional–patient relationship (22%), and the principle of beneficence or being disposed to act for the benefit of others (13%). Conclusions: The ethical and legal issues related to the practice of telehealth or telemedicine services still need standard and specific rules of application in order to guarantee equitable access, quality of care, sustainable costs, professional liability, respect of patient privacy, data protection, and confidentiality. At present, telemedicine services could be only used as complementary or supplementary tools to the traditional healthcare services. Some indications for medical providers are suggested.

A. Natale-Pereira, K. Enard, Lucinda Nevarez et al.

Reijo Salminen, A. Kousa, R. Ottesen et al.

T. Freeman, C. Hindocha, S. Green et al.

### What you need to know Until recently, cannabis and its derivatives were widely restricted under legislation which stated they had no medical value and carried a substantial risk of misuse. Policy is rapidly changing, and cannabis can now be prescribed for medicinal use in many countries, including the UK. This provides important new opportunities for treating patients although these need to be weighed up against potential risks. Several different medicinal products exist, with contrasting mechanisms of action, efficacy, and safety. Use of these products may increase as new evidence arises and policy changes occur. Here we review this emerging field. In the UK, drugs perceived by policy makers to have no medical value and a high risk of misuse, such as MDMA (3,4-methylenedioxy-methamphetamine, common street name “ecstasy”) are placed in Schedule …

Phoebe E. Freer

A. Basalaeva

The article states that the right to health care and medical care is a guarantee of the right to life. To the state in accordance with Part 2 of Art. 3 of the Constitution of Ukraine is entrusted with the duty to affirm and ensure human rights and freedoms, including the right of everyone to health care and medical assistance. Such a duty of the state is defined as the main one among its other duties. For its implementation, Ukraine must create an effective mechanism for ensuring the above-mentioned right. The Covid-19 pandemic, Russia’s aggression against Ukraine and, as a result, Russia’s temporary occupation of part of the territory of Ukraine, the commission of crimes in such territories that fall under the jurisdiction of the International Criminal Court and testify to violations of human rights in the field of health care, have actualized the issue of the specifics of international cooperation of states in the field of health care under conditions of military occupation, pandemics, etc. and its role in ensuring the above-mentioned right of every person. The doctrinal approaches to defining the concepts of «cooperation», «international cooperation», and international treaties on human rights are analyzed. It has been established that diametrically opposite points of view are expressed in the scientific literature: from the declaration of cooperation as a duty of states to the complete denial of the legal force of this principle. The author’s definition of the concept of international cooperation in the field of health care is formulated - it is a complex of legal means, methods and institutions that are the result of joint multilateral or bilateral activities of states and which they use to achieve common goals of sustainable development in the field of health care in accordance with generally recognized principles and norms of international law. Attention is focused on the fact that the result of international cooperation are international standards of human rights in the field of health care, which are adopted by international organizations of a universal and regional nature, and which are implemented in national legislation, but such standards are the result of the cooperation of states, albeit within the framework international intergovernmental organizations, since these organizations are not endowed with special competence by nature, but are endowed by states by defining it in the charter of such organizations.

Wörmann, Bernhard, Kopp, Ina, Nothacker, Monika et al.

Objective: After over 25 years of developing clinical practice guidelines, the Association of the Scientific Medical Societies in Germany (AWMF) held a symposium to discuss the following topics in order to improve the way evidence is implemented in the delivery of care: expansion of the data pool for guideline development, the regulatory policy framework for this expansion, the transfer of clinical practice guideline statements to medical practice, the associated opportunities and risks resulting from the European legislation. Methods: The AWMF held its Berlin Forum on 27 April 2022 where experts from scientific medical societies and national institutions in the healthcare sector reported their experiences and perceptions on the topics mentioned. Three writing groups compiled the key statements from these contributions to and discussions made at the Berlin Forum into a position paper. Results: The AWMF recommends the following:– The creation of a digital infrastructure that serves the quality assurance of clinical practice guidelines and makes their content available at the bedside and during consultations– An increase in the number of industry-independent clinical trials on prevention, diagnostics and therapy with medicinal products, medical devices or other procedures – The funding of registry structures to generate point-of-care healthcare data– The reduction of excessive bureaucratic hurdles at both the national and EU levelConclusions: By making concrete recommendations in this position paper, the AWMF maps out the steps required to improve the translation of evidence to the delivery of clinical care.