Hasil untuk "Medical legislation"

A. Sen

J. Prosser, Lewis S. Nelson

R. Weinstein, A. M. López, Bellal Joseph et al.

L. Coventry, D. Branley

Electronic healthcare technology is prevalent around the world and creates huge potential to improve clinical outcomes and transform care delivery. However, there are increasing concerns relating to the security of healthcare data and devices. Increased connectivity to existing computer networks has exposed medical devices to new cybersecurity vulnerabilities. Healthcare is an attractive target for cybercrime for two fundamental reasons: it is a rich source of valuable data and its defences are weak. Cybersecurity breaches include stealing health information and ransomware attacks on hospitals, and could include attacks on implanted medical devices. Breaches can reduce patient trust, cripple health systems and threaten human life. Ultimately, cybersecurity is critical to patient safety, yet has historically been lax. New legislation and regulations are in place to facilitate change. This requires cybersecurity to become an integral part of patient safety. Changes are required to human behaviour, technology and processes as part of a holistic solution.

S. O’Sullivan, Nathalie Nevejans, Colin Allen et al.

This paper aims to move the debate forward regarding the potential for artificial intelligence (AI) and autonomous robotic surgery with a particular focus on ethics, regulation and legal aspects (such as civil law, international law, tort law, liability, medical malpractice, privacy and product/device legislation, among other aspects).

Chandra Thapa, Seyit Ahmet Camtepe

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Besides, the public, who is the data source, always expects the security, privacy, and trust of their data. Otherwise, they can avoid contributing their data to the precision health system. Consequently, as the public is the targeted beneficiary of the system, the effectiveness of precision health diminishes. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.

K. Lasater, L. Aiken, D. Sloane et al.

Introduction Efforts to enact nurse staffing legislation often lack timely, local evidence about how specific policies could directly impact the public’s health. Despite numerous studies indicating better staffing is associated with more favourable patient outcomes, only one US state (California) sets patient-to-nurse staffing standards. To inform staffing legislation actively under consideration in two other US states (New York, Illinois), we sought to determine whether staffing varies across hospitals and the consequences for patient outcomes. Coincidentally, data collection occurred just prior to the COVID-19 outbreak; thus, these data also provide a real-time example of the public health implications of chronic hospital nurse understaffing. Methods Survey data from nurses and patients in 254 hospitals in New York and Illinois between December 2019 and February 2020 document associations of nurse staffing with care quality, patient experiences and nurse burnout. Results Mean staffing in medical-surgical units varied from 3.3 to 9.7 patients per nurse, with the worst mean staffing in New York City. Over half the nurses in both states experienced high burnout. Half gave their hospitals unfavourable safety grades and two-thirds would not definitely recommend their hospitals. One-third of patients rated their hospitals less than excellent and would not definitely recommend it to others. After adjusting for confounding factors, each additional patient per nurse increased odds of nurses and per cent of patients giving unfavourable reports; ORs ranged from 1.15 to 1.52 for nurses on medical-surgical units and from 1.32 to 3.63 for nurses on intensive care units. Conclusions Hospital nurses were burned out and working in understaffed conditions in the weeks prior to the first wave of COVID-19 cases, posing risks to the public’s health. Such risks could be addressed by safe nurse staffing policies currently under consideration.

Felix Busch, J. Kather, Christian Johner et al.

The European Union’s recently adopted Artificial Intelligence (AI) Act is the first comprehensive legal framework specifically on AI. This is particularly important for the healthcare domain, as other existing harmonisation legislation, such as the Medical Device Regulation, do not explicitly cover medical AI applications. Given the far-reaching impact of this regulation on the medical AI sector, this commentary provides an overview of the key elements of the AI Act, with easy-to-follow references to the relevant chapters.

Mariami Kvirkvia

This article examines the allocation of the burden of proof in medical liability. The paper is oriented toward a comparative law analysis, drawing on examples from various countries. It discusses The Concept of Proof and its Content, the Grounds for Allocating the Burden of Proof, and The Allocation of the Burden of Proof in American, German, and English Law, with particular attention to The Rule for Distributing the Burden of Proof in Medical Law, Reversing the Burden of Proof in Cases of Gross Medical Negligence, and Fully Controllable Risk as a Basis for Reversing the Burden of Proof. In this context, the legislation and judicial practice of both Continental and Anglo-American law countries are analyzed. The paper provides a detailed discussion of both statutory provisions and case law, as well as doctrinal debates, reflecting the specific challenges faced by plaintiffs in medical disputes. The study is enriched with examples from judicial practice, which give a practical dimension to the theoretical discussion and highlight the significance of judicial interpretations in shaping the doctrine of medical liability

Alismail A, Xu Y, Craddock K et al.

Abdullah Alismail,1– 3,* Yiqing Xu,1,* Krystal Craddock,4 David Lopez,1 Michael Terry,1,5 Laren Tan1,2 1Department of Cardiopulmonary Sciences, School of Allied Health Professions, Loma Linda University Health, Loma Linda, CA, USA; 2Department of Medicine, Loma Linda University Health, Loma Linda, CA, USA; 3Department of Cardiopulmonary Sciences, College of Health Science, Rush University, Chicago, IL, USA; 4Department of Respiratory Care, University of California Davis Medical Center, Sacramento, CA, USA; 5Department of Respiratory Care, Loma Linda University Medicine Center, Loma Linda, CA, USA*These authors contributed equally to this workCorrespondence: Abdullah Alismail, Department of Cardiopulmonary Sciences, Loma Linda University, Loma Linda, CA, USA, Email aalismail@llu.eduIntroduction: As the respiratory therapy (RT) profession in the United States discusses the establishment of the Advanced Practice in Respiratory Therapy (APRT) profession, the purpose of this study was to investigate the perception of the respiratory therapy community in the state of California (CA) on the need to start the APRT profession within the state.Methods: This was a descriptive pilot cross-sectional anonymous study that was approved by the institutional review board at Loma Linda University. Survey was sent via Email to program directors, faculty, students, bedside RTs, and hospital managers/directors by The Respiratory Care Board of CA, and the California Society in Respiratory Care to be sent out to their constituents.Results: A total of 1030 responded to the survey. Of the respondents, 50.6% were males and 48.1% females, with mean age of 45.7 ± 13.1 years. Most were practicing RTs (74.2%). Majority of the respondents held at least a bachelor’s degree and worked in a mid-size hospital. An overwhelming majority of the respondents supported the establishment of APRT in CA (91.9%). When asked about APRT educational level, 56% recommended a graduate degree. Nearly 56% of the respondents had knowledge of APRT prior to the survey, with 68.1% of them showing interest in applying for an APRT program once established. The main identified barriers to implementation were acceptance among other advanced practice providers, acceptance among physicians, legislation, scope of practice, and reimbursement. A majority believed that APRT should require a separate license, 71.7%. Chi-Square results showed that those with higher education were more supportive of APRT than those with high school, p = 0.015.Conclusion: The results of this pilot study show the strong support of the respiratory therapy workforce in California for establishing APRT. In addition, respondents believed that APRT should have its own separate license and those holding higher education were more supportive to establish APRT within the state. Further research is needed by surveying physicians, nurse practitioners, and physician assistants on the need for APRT within the state.Keywords: APRT, respiratory care, advanced practice respiratory therapy, advanced practice provider, California

Gijs Jan Brandsma, Jens Blom-Hansen, Christiaan Meijer et al.

Identifying regulatory statements in legislation is useful for developing metrics to measure the regulatory density and strictness of legislation. A computational method is valuable for scaling the identification of such statements from a growing body of EU legislation, constituting approximately 180,000 published legal acts between 1952 and 2023. Past work on extraction of these statements varies in the permissiveness of their definitions for what constitutes a regulatory statement. In this work, we provide a specific definition for our purposes based on the institutional grammar tool. We develop and compare two contrasting approaches for automatically identifying such statements in EU legislation, one based on dependency parsing, and the other on a transformer-based machine learning model. We found both approaches performed similarly well with accuracies of 80% and 84% respectively and a K alpha of 0.58. The high accuracies and not exceedingly high agreement suggests potential for combining strengths of both approaches.

Minjae Chung, Jong Bum Won, Ganghyun Kim et al.

Although Vision Transformers (ViTs) have recently demonstrated superior performance in medical imaging problems, they face explainability issues similar to previous architectures such as convolutional neural networks. Recent research efforts suggest that attention maps, which are part of decision-making process of ViTs can potentially address the explainability issue by identifying regions influencing predictions, especially in models pretrained with self-supervised learning. In this work, we compare the visual explanations of attention maps to other commonly used methods for medical imaging problems. To do so, we employ four distinct medical imaging datasets that involve the identification of (1) colonic polyps, (2) breast tumors, (3) esophageal inflammation, and (4) bone fractures and hardware implants. Through large-scale experiments on the aforementioned datasets using various supervised and self-supervised pretrained ViTs, we find that although attention maps show promise under certain conditions and generally surpass GradCAM in explainability, they are outperformed by transformer-specific interpretability methods. Our findings indicate that the efficacy of attention maps as a method of interpretability is context-dependent and may be limited as they do not consistently provide the comprehensive insights required for robust medical decision-making.

Yuntian Bo, Tao Zhou, Zechao Li et al.

Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal analysis is required. However, existing methods typically filter out domain-specific information to improve generalization, which inadvertently limits cross-domain performance and degrades source-domain accuracy. To address this, we present Contrastive Graph Modeling (C-Graph), a framework that leverages the structural consistency of medical images as a reliable domain-transferable prior. We represent image features as graphs, with pixels as nodes and semantic affinities as edges. A Structural Prior Graph (SPG) layer is proposed to capture and transfer target-category node dependencies and enable global structure modeling through explicit node interactions. Building upon SPG layers, we introduce a Subgraph Matching Decoding (SMD) mechanism that exploits semantic relations among nodes to guide prediction. Furthermore, we design a Confusion-minimizing Node Contrast (CNC) loss to mitigate node ambiguity and subgraph heterogeneity by contrastively enhancing node discriminability in the graph space. Our method significantly outperforms prior CD-FSMIS approaches across multiple cross-domain benchmarks, achieving state-of-the-art performance while simultaneously preserving strong segmentation accuracy on the source domain.

Caner Özer, Patryk Rygiel, Bram de Wilde et al.

Artifacts pose a significant challenge in medical imaging, impacting diagnostic accuracy and downstream analysis. While image-based approaches for detecting artifacts can be effective, they often rely on preprocessing methods that can lead to information loss and high-memory-demand medical images, thereby limiting the scalability of classification models. In this work, we propose the use of implicit neural representations (INRs) for image quality assessment. INRs provide a compact and continuous representation of medical images, naturally handling variations in resolution and image size while reducing memory overhead. We develop deep weight space networks, graph neural networks, and relational attention transformers that operate on INRs to achieve image quality assessment. Our method is evaluated on the ACDC dataset with synthetically generated artifact patterns, demonstrating its effectiveness in assessing image quality while achieving similar performance with fewer parameters.

Raza Imam, Rufael Marew, Mohammad Yaqub

Medical Vision-Language Models (MVLMs) have achieved par excellence generalization in medical image analysis, yet their performance under noisy, corrupted conditions remains largely untested. Clinical imaging is inherently susceptible to acquisition artifacts and noise; however, existing evaluations predominantly assess generally clean datasets, overlooking robustness -- i.e., the model's ability to perform under real-world distortions. To address this gap, we first introduce MediMeta-C, a corruption benchmark that systematically applies several perturbations across multiple medical imaging datasets. Combined with MedMNIST-C, this establishes a comprehensive robustness evaluation framework for MVLMs. We further propose RobustMedCLIP, a visual encoder adaptation of a pretrained MVLM that incorporates few-shot tuning to enhance resilience against corruptions. Through extensive experiments, we benchmark 5 major MVLMs across 5 medical imaging modalities, revealing that existing models exhibit severe degradation under corruption and struggle with domain-modality tradeoffs. Our findings highlight the necessity of diverse training and robust adaptation strategies, demonstrating that efficient low-rank adaptation when paired with few-shot tuning, improves robustness while preserving generalization across modalities.

Solha Kang, Joris Vankerschaver, Utku Ozbulak

With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural networks (CNNs) and are poised to dominate the field of artificial intelligence (AI)-based medical imaging in the upcoming years. Nevertheless, similar to CNNs, unveiling the decision-making process of transformer-based models remains a challenge. In this work, we take a step towards demystifying the decision-making process of transformer-based medical imaging models and propose Token Insight, a novel method that identifies the critical tokens that contribute to the prediction made by the model. Our method relies on the principled approach of token discarding native to transformer-based models, requires no additional module, and can be applied to any transformer model. Using the proposed approach, we quantify the importance of each token based on its contribution to the prediction and enable a more nuanced understanding of the model's decisions. Our experimental results which are showcased on the problem of colonic polyp identification using both supervised and self-supervised pretrained vision transformers indicate that Token Insight contributes to a more transparent and interpretable transformer-based medical imaging model, fostering trust and facilitating broader adoption in clinical settings.

Zhe Chen, Yusheng Liao, Shuyang Jiang et al.

Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer health inquiry support. However, they often generate hallucinations due to limited medical knowledge. Incorporating external knowledge is therefore critical, which necessitates multi-source knowledge acquisition. We address this challenge by framing it as a source planning problem, which is to formulate context-appropriate queries tailored to the attributes of diverse sources. Existing approaches either overlook source planning or fail to achieve it effectively due to misalignment between the model's expectation of the sources and their actual content. To bridge this gap, we present MedOmniKB, a repository comprising multigenre and multi-structured medical knowledge sources. Leveraging these sources, we propose the Source Planning Optimisation method, which enhances multi-source utilisation. Our approach involves enabling an expert model to explore and evaluate potential plans while training a smaller model to learn source alignment. Experimental results demonstrate that our method substantially improves multi-source planning performance, enabling the optimised small model to achieve state-of-the-art results in leveraging diverse medical knowledge sources.

I.S. Borysova

The Ukrainian system of medical and social expertise needs to revise its conceptual and methodological foundations in accordance with the realities of the current development of social sciences in the world and demand of the state regarding the modern understanding of disability. The purpose of the study was to analyze international experience and legislation on policy towards persons with disabilities and systematic approaches to the criteria of violation of functioning and the possibilities of determining the status of "person with disabilities" using the basic principles of the International Classification of Functioning (ICF), Impairment and Health to create optimal approaches to determining the criteria for disability in Ukraine. The study was based on the analysis of available scientific literature and legislative documents of developed countries on the understanding of the concept of "person with a disability". The scientometric databases used were Scopus, Web of Science, Google Scholar, and MedLine. Data from the official websites of the Political Department of the European Parliament, WHO, the United Nations, UNICEF, the World Bank. According to the results of the study, it is determined that a single state body is responsible for determining the status of a person with a disability in sustainable development countries using a multidisciplinary approach. The author identifies 3 main components that most developed countries assess when determining disability: economic, medical and social. At the same time, the main criterion is decreased performance. It has been established that since 2022, Spain has completely switched to the criteria of the International Classification of Functioning in determining the signs of disability, basing the severity of a person's condition as a degree of disability on a percentage decrease in the person's functioning. Some European countries use selective categories of the ICF.

Qixiang Zhang, Haonan Wang, Xiaomeng Li

Semi-supervised medical image segmentation (SSMIS) has emerged as a promising solution to tackle the challenges of time-consuming manual labeling in the medical field. However, in practical scenarios, there are often domain variations within the datasets, leading to derivative scenarios like semi-supervised medical domain generalization (Semi-MDG) and unsupervised medical domain adaptation (UMDA). In this paper, we aim to develop a generic framework that masters all three tasks. We notice a critical shared challenge across three scenarios: the explicit semantic knowledge for segmentation performance and rich domain knowledge for generalizability exclusively exist in the labeled set and unlabeled set respectively. Such discrepancy hinders existing methods from effectively comprehending both types of knowledge under semi-supervised settings. To tackle this challenge, we develop a Semantic & Domain Knowledge Messenger (S&D Messenger) which facilitates direct knowledge delivery between the labeled and unlabeled set, and thus allowing the model to comprehend both of them in each individual learning flow. Equipped with our S&D Messenger, a naive pseudo-labeling method can achieve huge improvement on six benchmark datasets for SSMIS (+7.5%), UMDA (+5.6%), and Semi-MDG tasks (+1.14%), compared with state-of-the-art methods designed for specific tasks.

Jingyu Guo, Christos Matsoukas, Fredrik Strand et al.

In multi-view medical diagnosis, deep learning-based models often fuse information from different imaging perspectives to improve diagnostic performance. However, existing approaches are prone to overfitting and rely heavily on view-specific features, which can lead to trivial solutions. In this work, we introduce Random Token Fusion (RTF), a novel technique designed to enhance multi-view medical image analysis using vision transformers. By integrating randomness into the feature fusion process during training, RTF addresses the issue of overfitting and enhances the robustness and accuracy of diagnostic models without incurring any additional cost at inference. We validate our approach on standard mammography and chest X-ray benchmark datasets. Through extensive experiments, we demonstrate that RTF consistently improves the performance of existing fusion methods, paving the way for a new generation of multi-view medical foundation models.