Hasil untuk "Medical legislation"

Filippo Pesapane, Caterina Volonté, M. Codari et al.

Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place. The European Union (EU) is reforming these fields with new legislation (General Data Protection Regulation [GDPR], Cybersecurity Directive, Medical Devices Regulation, In Vitro Diagnostic Medical Device Regulation). This reform is gradual, but it has now made its first impact, with the GDPR and the Cybersecurity Directive having taken effect in May, 2018. As regards the United States (U.S.), the regulatory scene is predominantly controlled by the Food and Drug Administration. This paper considers issues of accountability, both legal and ethical. The processes of medical device decision-making are largely unpredictable, therefore holding the creators accountable for it clearly raises concerns. There is a lot that can be done in order to regulate AI applications. If this is done properly and timely, the potentiality of AI based technology, in radiology as well as in other fields, will be invaluable.Teaching Points• AI applications are medical devices supporting detection/diagnosis, work-flow, cost-effectiveness.• Regulations for safety, privacy protection, and ethical use of sensitive information are needed.• EU and U.S. have different approaches for approving and regulating new medical devices.• EU laws consider cyberattacks, incidents (notification and minimisation), and service continuity.• U.S. laws ask for opt-in data processing and use as well as for clear consumer consent.

K. Harmon, J. Clugston, K. Dec et al.

Sport-related concussion (SRC) is a common injury in recreational and organised sport. Over the past 30 years, there has been significant progress in our scientific understanding of SRC, which in turn has driven the development of clinical guidelines for diagnosis, assessment and management of SRC. In addition to a growing need for knowledgeable healthcare professionals to provide evidence-based care for athletes with SRC, media attention and legislation have created awareness and, in some cases, fear about many issues and unknowns surrounding SRC. The American Medical Society for Sports Medicine (AMSSM) formed a writing group to review the existing literature on SRC, update its previous position statement, and to address current evidence and knowledge gaps regarding SRC. The absence of definitive outcomes-based data is challenging and requires relying on the best available evidence integrated with clinical experience and patient values. This statement reviews the definition, pathophysiology and epidemiology of SRC, the diagnosis and management of both acute and persistent concussion symptoms, the short-term and long-term risks of SRC and repetitive head impact exposure, SRC prevention strategies, and potential future directions for SRC research. The AMSSM is committed to best clinical practices, evidence-based research and educational initiatives that positively impact the health and safety of athletes.

Cecilia Stapff, Lucía Gómez Garbero, Rodolfo Gómez Ponce de León et al.

Summary: Problem: In the 1990s, almost 40% of maternal deaths in Uruguay were caused by unsafe abortions. Approach: A harm reduction model implemented in Uruguay, which addressed the risks associated with unsafe abortion practices by promoting and supporting the self-management of medical abortions by women in their homes, encouraged women’s autonomy. Local setting: Since 2005, an accelerated decrease in maternal mortality has been recorded in Uruguay, coinciding with the implementation of two major actions: a harm reduction approach with active promotion of self-care through self-management of medical abortions; and in 2012, a change in legislation, which made abortion legal within sexual and reproductive health facilities when requested by women up to 12 weeks of pregnancy or later for specific indications. Relevant changes: This example demonstrates that progress in public policies is possible through the combined efforts of civil society, healthcare professionals and policy makers. The initiative expanded the entry points to the healthcare system while strengthening women’s autonomy. Lessons learned: Increased access to self-care interventions for SRH contributed to advancing achievement of universal health coverage and the highest, most attainable standards of health. Funding: The authors have no financial relationships relevant to this article to disclose.

Ante B. V. Pettersson, R. Ballardini, M. Mimler et al.

3D printing has been adopted into routine use for certain medical applications, but more widespread usage has been hindered by, among other things, unclear legislation. We performed an analysis, using legal doctrinal study and legal informatics, of relevant EU legislation and case law in four issues relevant to medical 3D printing (excluding bioprinting or pharmacoprinting): pre-market approval, post-market liability, intellectual property rights, and data protection. Several gaps and uncertainties in the current legislation and interpretations were identified. In particular, we regard the current EU regulatory framework to be quite limiting and inflexible, exemplifying a cautionary approach common in EU law. Though the need to establish high safety standards in order to protect patients as a disadvantaged population is understood, both legal uncertainties and overregulation are seen as harmful to innovation. Hence, more adaptive legislation is called for to ensure continuous innovation efforts and enhanced patient outcomes.

D. Pullman

Abstract Canada and California each introduced legislation to permit medical assistance in dying in June, 2016. Each jurisdiction publishes annual reports on the number of deaths that occurred under their respective legislations in the previous years. The numbers are disturbingly different. In 2021, 486 individuals died under California’s End of Life Option. In the same year 10,064 Canadians died under that country’s Medical Assistance in Dying (MAiD) legislation. California has a slightly larger population than Canada, and while medically assisted deaths as a percentage of total deaths remained virtually unchanged in California from 2020-2021, Canada saw a 30% increase from 2020 to 2021. This essay examines some of the factors propelling Canada down the slippery slope of medically assisted suicide, as well as those that may be keeping California and other US jurisdictions from taking the slide. At a time of increasing pressure in many jurisdictions (both nationally and internationally) to liberalize access to medical assistance in dying, some lessons from this comparative analysis are offered.

Izabela Rojek, D. Mikołajewski, E. Dostatni et al.

Three-dimensional-printed medical devices are a separate group of medical devices necessary for the development of personalized medicine. The present article discusses a modern and specific group of medical devices and exoskeletons, which aims to present our own experiences in the selection of materials, design, artificial-intelligence optimization, production, and testing of several generations of various upper limb exoskeletons when considering the Medical Devices Regulation (MDR) and the ISO 13485 and ISO 10993 standards. Work is underway to maintain the methodological rigor inherent in medical devices and to develop new business models to achieve cost-effectiveness so that inadequate legislation does not stop the development of this group of technologies (3D scanning, 3D printing, and reverse engineering) in the healthcare system. The gap between research and engineering practice and clinical 3D printing should be bridged as quickly and as carefully as possible. This measure will ensure the transfer of proven solutions into clinical practice. The growing maturity of 3D printing technology will increasingly impact everyday clinical practice, so it is necessary to prepare medical specialists and strategic and organizational changes to realize the correct implementation based on the needs of patients and clinicians.

Agnieszka Byś, Tomasz Berus

Amendment to the Regulation of Minister of Labour and Social Policy of 1 December 1998 on safety and occupational hygiene at positions equipped with display monitors, adopted on October 18th, 2023, entered the long-awaited by employees possibility of reimbursement for corrective contact lenses, adjusting the Polish legislation to European directive regulating working conditions with display screen equipment. Contact lenses in many cases of refractive errors can be an alternative to prescription glasses method of correcting the visual impairment. There are however many clinical conditions, in which contact lenses can provide a better corrective effect on visual acuity. The information contained in medical databases of articles and scientific journals (PubMed, Biblioteka Nauki), online publications (Lippincott Journals), books, applicable legal regulations (available in Internetowy System Aktów Prawnych) and guidelines published by organizations and associations (Nofer Institute of Occupational Medicine in Łódź, Occupational Safety and Health Administration, Tear Film & Ocular Surface Society) were analyzed, covering the discussed issues over the years 2000–2023. Non-correction or suboptimal correction of a refractive error can cause a wide variety of troublesome symptoms, such as eye pain, headache, double vision, balance disorders, nausea, disturbances in the perception of the surroundings, contributing to poorer work efficiency, faster fatigue or an increased risk of error. This article, which is a narrative review, aims to present these conditions, as well as provide a brief overview of the types of contact lenses used, complications that may result from their use and contraindications to the use of this type of correction. Med Pr Work Health Saf. 2024;75(4):383–390

Parvina Fazail Ismayilova

The scientific article examines the relevant regulatory framework for the development of legal, ethical and moral regulation in the fight against HIV/AIDS, and provides recommendations for improving medical treatment and control mechanisms. In the research work, a comparative analysis of domestic legal acts with international legal norms as the current legal basis for the fight against the AIDS epidemic is considered, and the directions for the adoption of the legislation and judicial practice of model countries in the field of medical law for the Republic of Azerbaijan are stated. Based on case studies and empirical research, the article provides an assessment of the challenges and opportunities for strengthening legal protection, especially for people living with HIV/AIDS, and legal mechanisms for increasing access to care and treatment. Also, innovative methods and strategies are explored to protect the rights of people suffering from HIV/AIDS, to protect against discrimination and stigmatization through the application of directly mentioned coping strategies, as well as to create more sensitive and effective treatment mechanisms of the health care system. It is proposed to refine the legislation of the Republic of Azerbaijan and eliminate the loopholes in the law. Overall, the article emphasizes the importance of a strong legal framework for the development of the fight against AIDS and suggests practical steps to bring it to the agenda in today’s world. In the context of the ongoing global struggle against HIV/AIDS, our research emphasized the crucial role of legal regulation in the development of a comprehensive care system for individuals affected by this disease. The article underscored the significance of legal frameworks in ensuring unhindered access to care, treatment, and support for individuals living with HIV/AIDS. By establishing guidelines and standards for healthcare providers, legal regulation enables the delivery of high-quality care while safeguarding the rights of individuals affected by HIV/AIDS. Furthermore, the rule of law plays a vital role in combating stigma and discrimination, which remain significant obstacles to effective efforts to prevent and treat HIV/AIDS. It is also highlighted various tools and strategies for improving care for individuals living with HIV/ AIDS, including the integration of HIV services into existing healthcare systems, community-based care models, and the promotion of holistic and patient-centered approaches to care. In conclusion, our research demonstrates that legal regulation is a vital component of the global response to HIV/AIDS, as it enables the provision of comprehensive care and support to individuals affected by this disease. Moreover, the implementation of innovative care models and strategies outlined in international legal instruments can further enhance the quality and effectiveness of HIV/AIDS treatment services.

Fan Bai, Yuxin Du, Tiejun Huang et al.

Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research has primarily focused on 2D medical images, leaving 3D images under-explored, despite their richer spatial information. This paper aims to advance 3D medical image analysis with MLLMs. To this end, we present a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs specifically tailored for various 3D medical tasks, such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis. Furthermore, we introduce a new 3D multi-modal medical benchmark, M3D-Bench, which facilitates automatic evaluation across eight tasks. Through comprehensive evaluation, our method proves to be a robust model for 3D medical image analysis, outperforming existing solutions. All code, data, and models are publicly available at: https://github.com/BAAI-DCAI/M3D.

Yu Cai, Weiwen Zhang, Hao Chen et al.

Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays an important role in rare disease recognition and health screening in the medical domain. Despite the emergence of numerous methods for medical AD, the lack of a fair and comprehensive evaluation causes ambiguous conclusions and hinders the development of this field. To address this problem, this paper builds a benchmark with unified comparison. Seven medical datasets with five image modalities, including chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and histopathology images, are curated for extensive evaluation. Thirty typical AD methods, including reconstruction and self-supervised learning-based methods, are involved in comparison of image-level anomaly classification and pixel-level anomaly segmentation. Furthermore, for the first time, we systematically investigate the effect of key components in existing methods, revealing unresolved challenges and potential future directions. The datasets and code are available at https://github.com/caiyu6666/MedIAnomaly.

Wentao Chen, Xichen Xu, Jie Luo et al.

Image-to-image translation is a common task in computer vision and has been rapidly increasing the impact on the field of medical imaging. Deep learning-based methods that employ conditional generative adversarial networks (cGANs), such as Pix2PixGAN, have been extensively explored to perform image-to-image translation tasks. However, when noisy medical image data are considered, such methods cannot be directly applied to produce clean images. Recently, an augmented GAN architecture named AmbientGAN has been proposed that can be trained on noisy measurement data to synthesize high-quality clean medical images. Inspired by AmbientGAN, in this work, we propose a new cGAN architecture, Ambient-Pix2PixGAN, for performing medical image-to-image translation tasks by use of noisy measurement data. Numerical studies that consider MRI-to-PET translation are conducted. Both traditional image quality metrics and task-based image quality metrics are employed to assess the proposed Ambient-Pix2PixGAN. It is demonstrated that our proposed Ambient-Pix2PixGAN can be successfully trained on noisy measurement data to produce high-quality translated images in target imaging modality.

Pedram Hosseini, Jessica M. Sin, Bing Ren et al.

There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world clinical applications where LLMs are being deployed. Furthermore, existing studies on evaluating long-form answer generation in medical QA are primarily closed-source, lacking access to human medical expert annotations, which makes it difficult to reproduce results and enhance existing baselines. In this work, we introduce a new publicly available benchmark featuring real-world consumer medical questions with long-form answer evaluations annotated by medical doctors. We performed pairwise comparisons of responses from various open and closed-source medical and general-purpose LLMs based on criteria such as correctness, helpfulness, harmfulness, and bias. Additionally, we performed a comprehensive LLM-as-a-judge analysis to study the alignment between human judgments and LLMs. Our preliminary results highlight the strong potential of open LLMs in medical QA compared to leading closed models. Code & Data: https://github.com/lavita-ai/medical-eval-sphere

Keith Harrigian, Ayah Zirikly, Brant Chee et al.

Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.

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

Objective To evaluate variation in Illinois hospital nurse staffing ratios and to determine whether higher nurse workloads are associated with mortality and length of stay for patients, and cost outcomes for hospitals. Design Cross-sectional analysis of multiple data sources including a 2020 survey of nurses linked to patient outcomes data. Setting: 87 acute care hospitals in Illinois. Participants 210 493 Medicare patients, 65 years and older, who were hospitalised in a study hospital. 1391 registered nurses employed in direct patient care on a medical–surgical unit in a study hospital. Main outcome measures Primary outcomes were 30-day mortality and length of stay. Deaths avoided and cost savings to hospitals were predicted based on results from regression estimates if hospitals were to have staffed at a 4:1 ratio during the study period. Cost savings were computed from reductions in lengths of stay using cost-to-charge ratios. Results Patient-to-nurse staffing ratios on medical-surgical units ranged from 4.2 to 7.6 (mean=5.4; SD=0.7). After adjusting for hospital and patient characteristics, the odds of 30-day mortality for each patient increased by 16% for each additional patient in the average nurse’s workload (95% CI 1.04 to 1.28; p=0.006). The odds of staying in the hospital a day longer at all intervals increased by 5% for each additional patient in the nurse’s workload (95% CI 1.00 to 1.09, p=0.041). If study hospitals staffed at a 4:1 ratio during the 1-year study period, more than 1595 deaths would have been avoided and hospitals would have collectively saved over $117 million. Conclusions Patient-to-nurse staffing ratios vary considerably across Illinois hospitals. If nurses in Illinois hospital medical–surgical units cared for no more than four patients each, thousands of deaths could be avoided, and patients would experience shorter lengths of stay, resulting in cost-savings for hospitals.

N. Khan, V. Vambol, Sergij Vambol et al.

Tasmia Tahmida Jidney, Angona Biswas, MD Abdullah Al Nasim et al.

The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.

Zheyuan Zhang, Lanhong Yao, Bin Wang et al.

Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called DiffBoost. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints and preserve the underlying structure of imaging data. Due to the random sampling process by the diffusion model, we can generate an arbitrary number of synthetic images with diverse appearances. To validate the effectiveness of our proposed method, we conduct an extensive set of medical image segmentation experiments on multiple datasets, including Ultrasound breast (+13.87%), CT spleen (+0.38%), and MRI prostate (+7.78%), achieving significant improvements over the baseline segmentation methods. The promising results demonstrate the effectiveness of our \textcolor{black}{DiffBoost} for medical image segmentation tasks and show the feasibility of introducing a first-ever text-guided diffusion model for general medical image segmentation tasks. With carefully designed ablation experiments, we investigate the influence of various data augmentations, hyper-parameter settings, patch size for generating random merging mask settings, and combined influence with different network architectures. Source code are available at https://github.com/NUBagciLab/DiffBoost.

Zihao Zhao, Yuxiao Liu, Han Wu et al.

Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising results across various tasks due to its generalizability and interpretability. The use of CLIP has recently gained increasing interest in the medical imaging domain, serving as a pre-training paradigm for image-text alignment, or a critical component in diverse clinical tasks. With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. In this paper, we (1) first start with a brief introduction to the fundamentals of CLIP methodology; (2) then investigate the adaptation of CLIP pre-training in the medical imaging domain, focusing on how to optimize CLIP given characteristics of medical images and reports; (3) further explore practical utilization of CLIP pre-trained models in various tasks, including classification, dense prediction, and cross-modal tasks; and (4) finally discuss existing limitations of CLIP in the context of medical imaging, and propose forward-looking directions to address the demands of medical imaging domain. Studies featuring technical and practical value are both investigated. We expect this survey will provide researchers with a holistic understanding of the CLIP paradigm and its potential implications. The project page of this survey can also be found on https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging.

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

Supplemental Digital Content is available in the text. Background: The Safe Staffing for Quality Care Act under consideration in the New York (NY) state assembly would require hospitals to staff enough nurses to safely care for patients. The impact of regulated minimum patient-to-nurse staffing ratios in acute care hospitals in NY is unknown. Objectives: To examine variation in patient-to-nurse staffing in NY hospitals and its association with adverse outcomes (ie, mortality and avoidable costs). Research Design: Cross-sectional data on nurse staffing in 116 acute care general hospitals in NY are linked with Medicare claims data. Subjects: A total of 417,861 Medicare medical and surgical patients. Measures: Patient-to-nurse staffing is the primary predictor variable. Outcomes include in-hospital mortality, length of stay, 30-day readmission, and estimated costs using Medicare-specific cost-to-charge ratios. Results: Hospital staffing ranged from 4.3 to 10.5 patients per nurse (P/N), and averaged 6.3 P/N. After adjusting for potential confounders each additional patient per nurse, for surgical and medical patients, respectively, was associated with higher odds of in-hospital mortality [odds ratio (OR)=1.13, P=0.0262; OR=1.13, P=0.0019], longer lengths of stay (incidence rate ratio=1.09, P=0.0008; incidence rate ratio=1.05, P=0.0023), and higher odds of 30-day readmission (OR=1.08, P=0.0002; OR=1.06, P=0.0003). Were hospitals staffed at the 4:1 P/N ratio proposed in the legislation, we conservatively estimated 4370 lives saved and $720 million saved over the 2-year study period in shorter lengths of stay and avoided readmissions. Conclusions: Patient-to-nurse staffing varies substantially across NY hospitals and higher ratios adversely affect patients. Our estimates of potential lives and costs saved substantially underestimate potential benefits of improved hospital nurse staffing.

Romanovsky Georgy, Romanovskaya Olga, Artyomova Darya

The article considers a general concept of reproductive rights, its primary enshrinement into international documents, and manifestation in specific laws adopted in post-Soviet space (including the Russian Federation). Various approaches to legal regulation based on direct enshrinement of specific reproductive rights, and determination of a legal regime of assisted reproductive technologies are shown. Some examples taken from legislation of the Republic of Armenia, the Republic of Belarus, the Republic of Kazakhstan, and the Kyrgyz Republic are given. The Western European experience based on the regulation of technologies rather than on provision of new basic human rights is presented. Effects of inclusion of reproductive technologies into economically evaluated medical industry are analyzed. It is highlighted that regulatory logic of reproductive technologies in Russia is largely based on market approaches, and any medical intervention is considered to be a civil service. Meanwhile, according to 2020 Constitutional amendments, Article 114 of the Constitution of the Russian Federation enshrined preservation of traditional values. Thus, a theological approach to the regulation of most biomedical issues seems to be relevant.