Renata Bittar Britto Arantes, Alanna Gomes da Silva, Mónica Maria Bessa Correia
et al.
Resumo Este estudo analisou 1.363 processos éticos de médicos em Minas Gerais, ocorridos entre 2012 e 2022. Predominaram homens com mais de 20 anos de experiência, envolvidos em processos relacionados a responsabilidade profissional em áreas cirúrgicas. A absolvição ocorreu em 50,2% dos casos, com censura pública como penalidade mais frequente. Identificou-se a necessidade de atualizações contínuas: em documentação para médicos jovens e experientes, em publicidade para generalistas de faixa intermediária, em interação entre médicos especialistas. As denúncias, conforme o denunciante, indicaram a necessidade de estratégias fiscalizatórias mais precisas, como reforço em instituições públicas e orientação da importância da relação médico-paciente. A gravidade das sanções nos casos relativos a direitos humanos evidenciou a importância ética desse capítulo do Código de Ética Médica. Concluiu-se que, para que a regulação ética seja eficaz e aderente à realidade profissional, intervenções educativas e fiscalizatórias devem ser pautadas pelo profissional sancionado mais vulnerável e pelas infrações mais prevalentes.
Abstract Introduction This article explores the clinical encounter not merely as a site for technical intervention or diagnostic reasoning, but as a complex event where epistemology and ethics converge. Challenging the reduction of medicine to scientific protocols, it argues for a conceptual reorientation grounded in language and human relationality. The encounter between clinician and patient is framed as both an epistemic inquiry and a moral covenant, where understanding a patient’s condition requires access not only to biological data but to their social, cultural, and linguistic lifeworld. While the sciences offer truth about the body, they do not suffice to grasp the full existential dimension of illness. Language thus becomes central—not only as a medium of communication, but as the very space where knowledge and care are shaped and shared. It is in and through language that ethical responsibility toward the patient is enacted. Method This article synthesizes a philosophical investigation into the ethical and linguistic foundations of medical practice. Drawing on the work of Wilhelm von Humboldt, Émile Benveniste, Emmanuel Levinas, and decolonial thinkers such as Frantz Fanon and Édouard Glissant, it develops a theoretical framework that helps to clarify how subjectivity, vulnerability, and responsibility emerge in and through language during the clinical encounter. The approach is conceptual and interpretive, grounded in close textual analysis and oriented toward the ethical implications of these philosophical insights within the medical practice. Findings From this analysis emerges a critique of dominant ontological assumptions within Western medicine, particularly its tendency to assimilate the Other into pre-existing categories, thereby enacting a form of epistemic violence. Levinas’s distinction between the Said (le Dit) and the Saying (le Dire) becomes central to this critique. The Said corresponds to propositional knowledge and thematic discourse—typical of clinical reasoning—while the Saying signals a more primordial ethical relation: an act of exposure, vulnerability, and responsibility toward the Other. Proximity, as defined by Levinas, is not a spatial or cognitive closeness but an ethical immediacy—a face-to-face relation where the Other appears as irreducibly singular. Humboldt’s and Benveniste’s linguistic theories reinforce this view by emphasizing that subjectivity is dialogical and relational rather than autonomous and pregiven. In contrast to Habermas’s emphasis on validity claims and rational consensus, Levinas privileges the irreducible alterity of the Other as the foundation of ethical life, a move that reframes the conditions under which medical knowledge and care become possible. Discussion These philosophical insights have profound implications for the medical practice. When in a clinical encounter, the patients do not merely present symptoms to be categorized—they bring a world that demands ethical attention. Medical language, far from being neutral, reconfigures how illness is understood and treated. The difference between saying a patient “has diabetes,” “suffers from diabetes,” or “is diabetic” reflects deeper assumptions about identity and embodiment. The ethical quality of care hinges on such linguistic choices. Through the lens of Levinas’s Saying, the patient’s voice is heard not simply as information but as a call to responsibility. Moreover, when placed in conversation with decolonial thinkers, this analysis reveals the extent to which colonial and racialized logics continue to shape medical practice. Fanon’s critique of the objectification of Black bodies, and Glissant’s defense of “opacity” against totalizing knowledge systems, highlight how patients are often forced into identities that obscure their singularity. The ethical demand of the clinical encounter thus resists any framework—biological, social, or racial—that seeks to fully determine the patient in advance of relation. Conclusion The article concludes that ethical responsibility in medicine arises not from what is known about the patient, but from a willingness to engage with what remains unknown and unknowable—their singularity, vulnerability, and alterity. The clinical encounter is reimagined as a moral space where language becomes the medium through which care is not only delivered but ethically constituted. In this reconfiguration, the practice of medicine moves beyond procedural norms and toward a relational ethics rooted in proximity and attentiveness. By bringing together Levinas’s philosophy of language and responsibility with decolonial critiques of medical rationality, the article calls for a fundamental transformation in how healing is conceived: not as mastery over the body, but as a dialogical and ethical relation between singular beings.
From the cross-temporal perspective of Hans Jonas's ethics of responsibility, the application of human genetic enhancement technology faces three ethical dilemmas: technical intervention undermines the genetic stability of human gene pool and challenges formal responsibility; intergenerational imbalance in the distribution of technological benefits disrupts non-reciprocal responsibility; and the technology commercialization and power alienation deviate from public welfare, violating substantive responsibility. The root cause lies in the mismatch between technological power and responsibility ethics, the spatiotemporal imbalance of the intergenerational responsibility relationship, and the lack of responsibility for technological consequences. Therefore, it is necessary to reconstruct the ethical framework of technological responsibility: safeguarding the human gene pool as the foundation for the survival of civilization, clarifying intergenerational responsibility with justice between generations as the core, and strengthening self-regulation in technological application with public welfare as the guiding principle. This reconstruction aims to achieve the harmonious coexistence of technological rationality and humanistic values.
Various indications show that contemporary medical technology has entered a new stage. Unlike traditional high technologies, which focused on addressing discomfort and defects of the human body based on the natural essence of human life, the new advanced medical technologies emphasize the molecular and submolecular levels for understanding and treating diseases. These technologies utilize innovative methods to transform or create human tissues, organs, or even life itself, resulting in advanced medical technologies with novel qualities, diverse characteristics, and varied forms. The strategic approach to developing advanced medical technologies includes adhering to and serving the fundamental purposes of medicine, differentiating the values and significance of various high technologies for tailored applications, balancing the dual objectives of tackling severe and complex diseases while promoting public health, selecting the best options, and maintaining respect for the sanctity and dignity of life as well as natural laws. Additionally, it advocates for rational innovation and embraces the principle of technological humility.
High-performing medical Large Language Models (LLMs) typically require extensive fine-tuning with substantial computational resources, limiting accessibility for resource-constrained healthcare institutions. This study introduces a confidence-driven multi-model framework that leverages model diversity to enhance medical question answering without fine-tuning. Our framework employs a two-stage architecture: a confidence detection module assesses the primary model's certainty, and an adaptive routing mechanism directs low-confidence queries to Helper models with complementary knowledge for collaborative reasoning. We evaluate our approach using Qwen3-30B-A3B-Instruct, Phi-4 14B, and Gemma 2 12B across three medical benchmarks; MedQA, MedMCQA, and PubMedQA. Result demonstrate that our framework achieves competitive performance, with particularly strong results in PubMedQA (95.0\%) and MedMCQA (78.0\%). Ablation studies confirm that confidence-aware routing combined with multi-model collaboration substantially outperforms single-model approaches and uniform reasoning strategies. This work establishes that strategic model collaboration offers a practical, computationally efficient pathway to improve medical AI systems, with significant implications for democratizing access to advanced medical AI in resource-limited settings.
Background: Deep learning has significantly advanced medical image analysis, with Vision Transformers (ViTs) offering a powerful alternative to convolutional models by modeling long-range dependencies through self-attention. However, ViTs are inherently data-intensive and lack domain-specific inductive biases, limiting their applicability in medical imaging. In contrast, radiomics provides interpretable, handcrafted descriptors of tissue heterogeneity but suffers from limited scalability and integration into end-to-end learning frameworks. In this work, we propose the Radiomics-Embedded Vision Transformer (RE-ViT) that combines radiomic features with data-driven visual embeddings within a ViT backbone. Purpose: To develop a hybrid RE-ViT framework that integrates radiomics and patch-wise ViT embeddings through early fusion, enhancing robustness and performance in medical image classification. Methods: Following the standard ViT pipeline, images were divided into patches. For each patch, handcrafted radiomic features were extracted and fused with linearly projected pixel embeddings. The fused representations were normalized, positionally encoded, and passed to the ViT encoder. A learnable [CLS] token aggregated patch-level information for classification. We evaluated RE-ViT on three public datasets (including BUSI, ChestXray2017, and Retinal OCT) using accuracy, macro AUC, sensitivity, and specificity. RE-ViT was benchmarked against CNN-based (VGG-16, ResNet) and hybrid (TransMed) models. Results: RE-ViT achieved state-of-the-art results: on BUSI, AUC=0.950+/-0.011; on ChestXray2017, AUC=0.989+/-0.004; on Retinal OCT, AUC=0.986+/-0.001, which outperforms other comparison models. Conclusions: The RE-ViT framework effectively integrates radiomics with ViT architectures, demonstrating improved performance and generalizability across multimodal medical image classification tasks.
Medical Physics education is delivered through accredited programs with admissions and funding for students determined by individual institutions providing the educational experiences. Public data from accredited graduate programs, along with funding data, were used to analyze institutional trends in this educational market. Temporal trends from 2017 to 2023 show robust growth in MS graduates, increasing at an average of 17.7 per year, as compared to steady but modest growth in PhDs, increasing by 3.6 per year. The current status is there are nearly two MS graduates for every PhD graduate. Trends in funding show self-funding of students is a dominant pathway in domestic programs. Those programs dominated by accredited MS education have their largest fraction of faculty in radiation oncology departments, whereas those dominated by PhD education have their largest fraction of faculty in radiology departments. Overall NIH funding in the space of radiation diagnostics and therapeutics has been largely static over this timeframe, but with a notable 5 year rise in NCI funding. This can be contrasted to a substantial 5X-6X rise in NIH funding for engineering research in this same period, with significant increases in trainee funding there. Taken as a whole, this survey shows that growth in the field of medical physics education is dominated by MS graduates, presumably servicing the expanded growth needs for well-trained clinical physicists. However, the research infrastructure that supports PhD training in medical physics seems likely to be growing modestly and missing the growth trend of NIH funding that appears to show substantially more growth in non-accredited programs such as biomedical engineering. This data is useful to informing accreditation guidance on numbers of graduates to match the workforce needs or for inter-institutional planning around education goals.
Lee J. Evitts, Philip W. Miller, Chiara Da Pieve
et al.
Background: The emergence and growth of fusion technology enables investigative studies into its applications beyond typical power production facilities. This study seeks to determine the viability of medical isotope production with the neutrons produced in an example large fusion device. Using FISPACT-II (a nuclear inventory code) and a simulated fusion spectrum, the production yields of a significant number of potentially clinically relevant (both in use and novel) medical isotopes were calculated. Comparative calculations were also conducted against existing production routes. Results: Depending on the neutron flux of the fusion device, it could be an ideal technology to produce alpha-emitters such as 212Bi/212Pb, it may be able to contribute to the production of 99mTc/99Mo, and could offer an alternative route in the production a few Auger-emitting candidates. There is also a long list of beta-emitting nuclides where fusion technology may be best placed to produce over existing technologies including 67Cu, 90Y and 47Sc. Conclusions: It is theoretically viable to produce existing and novel medical isotopes with fusion technology. However, a significant number of assumptions form the basis of this study which would need to be studied further for any particular nuclide of interest.
Health disparities (differences in non-genetic conditions that influence health) can be associated with differences in burden of disease by groups within a population. Social determinants of health (SDOH) are domains such as health care access, dietary access, and economics frequently studied for potential association with health disparities. Evaluating SDOH-related phenotypes using routine medical images as data sources may enhance health disparities research. We developed a pipeline for using quantitative measures automatically extracted from medical images as inputs into health disparities index calculations. Our study focused on the use case of two SDOH demographic correlates (sex and race) and data extracted from chest radiographs of 1,571 unique patients. The likelihood of severe disease within the lung parenchyma from each image type, measured using an established deep learning model, was merged into a single numerical image-based phenotype for each patient. Patients were then separated into phenogroups by unsupervised clustering of the image-based phenotypes. The health rate for each phenogroup was defined as the median image-based phenotype for each SDOH used as inputs to four imaging-derived health disparities indices (iHDIs): one absolute measure (between-group variance) and three relative measures (index of disparity, Theil index, and mean log deviation). The iHDI measures demonstrated feasible values for each SDOH demographic correlate, showing potential for medical images to serve as a novel probe for health disparities. Large-scale AI analysis of medical images can serve as a probe for a novel data source for health disparities research.
More than a century ago, Karl Bernhard Zoeppritz derived the equations that determine the reflected and transmitted coefficients at a planar interface for an incident seismic wave. The coefficients so obtained are a function of the elastic parameters of the media on each side of the interface and the angle of incidence. Approximations of the equations have been proposed and used in geophysical exploration, however, full use of the equations and their generalization to multiple layers could offer richer information about the properties of the media and be helpful in medical diagnosis via ultrasound. In this work, we investigate how to extract information from the angle-dependent reflection coefficients, including critical angles and the wave distortion at the interface between two and three media. It is shown that it is possible to separate the effect of density from speed of sound mismatch by measuring amplitudes as a function of angle of incidence (AVA). And examining the critical angle and waveform distortion of the reflected waves can reveal the thickness of an intermediate layer, even with subwavelength resolution. These studies could be integrated into medical imaging and also into the training of artificial intelligence systems that assist in diagnosis. In particular, they could help prevent cerebrovascular accidents by early detection of the formation and hardening of plaque in the arteries that irrigate the brain.
In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal technique, can provide a more comprehensive view of the lesions, aiding physicians in evaluating the disease's shape, location, and biological activity. However, due to the limitations of imaging equipment and considerations for patient safety, the quality of medical images is usually limited, leading to sub-optimal fusion performance, and affecting the depth of image analysis by the physician. Thus, there is an urgent need for a technology that can both enhance image resolution and integrate multi-modal information. Although current image processing methods can effectively address image fusion and super-resolution individually, solving both problems synchronously remains extremely challenging. In this paper, we propose TFS-Diff, a simultaneously realize tri-modal medical image fusion and super-resolution model. Specially, TFS-Diff is based on the diffusion model generation of a random iterative denoising process. We also develop a simple objective function and the proposed fusion super-resolution loss, effectively evaluates the uncertainty in the fusion and ensures the stability of the optimization process. And the channel attention module is proposed to effectively integrate key information from different modalities for clinical diagnosis, avoiding information loss caused by multiple image processing. Extensive experiments on public Harvard datasets show that TFS-Diff significantly surpass the existing state-of-the-art methods in both quantitative and visual evaluations. Code is available at https://github.com/XylonXu01/TFS-Diff.
Foundational models (FMs) have tremendous potential to revolutionize medical imaging. However, their deployment in real-world clinical settings demands extensive ethical considerations. This paper aims to highlight the ethical concerns related to FMs and propose a framework to guide their responsible development and implementation within medicine. We meticulously examine ethical issues such as privacy of patient data, bias mitigation, algorithmic transparency, explainability and accountability. The proposed framework is designed to prioritize patient welfare, mitigate potential risks, and foster trust in AI-assisted healthcare.
Fransa Kralı IX. Louis, 1244 tarihinde malaria (sıtma) hastalığına yakalanmış, bu hastalık sebebiyle de bilincini kaybetmiş, bilinci kapalıyken hizmetçileri onun öldüğünü zannetmişti. IX. Louis, hastalıktan kurutulup iyileşmesi halinde bir Haçlı Seferi düzenleyeceğine yemin etmişti.
IX. Louis sağlığına kavuştuktan hemen sonra Haçlı Seferi hazırlıklarına başlamış ve 11 Eylül 1248 tarihinde Kıbrıs’a gelmişti. 4 Haziran 1249 tarihinde Dimyat önlerine gelerek burayı kısa zamanda almıştı. Daha sonra IX. Louis’in kardeşi Artios Kontu Robert, Mansûra’daki Müslümanların karargâhında bulunan askerlere baskın yapmıştı. Bu baskın sonucunda ordu komutanı Fahreddin Şeyhu’ş-Şuyûh da olmak üzere birçok Müslüman asker şehit edilmişti. Eyyûbî ordusundaki Memlük birliğinin bir kısmı bu kargaşadan kurtulup Mansûra’nın dar sokaklarına pusu kurmuş ve Haçlıların şehrin sokaklarına girmelerini beklemişti. Haçlı birlikleri şehrin sokaklarına girdikten sonra aniden hücum ederek şövalyelerin tamamına yakınını öldürmüşlerdi. Böylece Haçlılar, Mansûra’da istediklerini elde edemeden büyük bir zayiat vererek oradan çekilmek zorunda kalmışlardı.
Bu sırada Mısır Eyyûbî hükümdarı Melikü’s-Sâlih (637-647/1240-1249) yakalanmış olduğu verem hastalığından dolayı vefat etmiş yerine oğlu Turanşah, Eyyûbî tahtına geçmişti. Turanşah, tahta geçer geçmez Haçlılarla etkili bir şekilde mücadele etmek için hafif gemiler yaptırmış, Haçlılara, Nil Nehri yoluyla Dimyat’tan erzak ve savaş teçhizatının getirilmesini engellemişti. Turanşah, daha sonra orduya pek çok asker katarak orduyu da güçlendirmişti. Turanşah, bu hazırlıkları yaparken Haçlı birlikleri açlık ve salgın hastalıklarla mücadele etmekteydi. Neticede IX. Louis, Müslümanlar karşısında kazanma ihtimali olmadığını anlayınca anlaşma yapmak üzere Turanşah’a bir elçi heyeti göndermiş fakat istenilen anlaşma sağlanamamıştı.
IX. Louis, Turanşah ile anlaşma sağlayamayınca orduyu geri çekerek Dimyat’a dönmeye karar verdi. Bu durumdan haberdar olan Turanşah, orduya Haçlılar üzerine saldırı emri verdi. Bu saldırıda birçok Haçlı askeri öldürüldü. Dizanteri hastalığına yakalanan IX. Louis’i adamları bir kulübeye götürdü. Haçlı şövalyeleri arasında erzak eksikliği ve temiz su sıkıntısından dolayı çeşitli salgın hastalıklar yayıldı. Hastalığa yakalanan askerlerin diş etlerinde ölü tabakalar çıktı. Öyle ki hastaların yiyeceklerini çiğneyebilmeleri için cerrahlar bu etleri kesmek zorunda kaldı. Diğer bir salgının belirtileri ise bacaklardaki etlerin kuruması ve derinin üzerinde siyah toprak renginde beneklerin oluşması şeklindeydi. Ayrıca hastalığa yakalananların dişleri çürümeye başlıyordu. Akabinde burunları kanıyor ve daha sonra ölüyorlardı. Bunun neticesinde IX. Louis, ordunun çok fazla zayiat verdiğini görünce Müslümanlara karşı daha fazla direnemeyeceğine kanaat getirdi. Önde gelen komutanlarıyla yaptığı toplantı sonrasında Turanşah’tan emân dileyip teslim olmaya karar verdi.
Turanşah, Haçlıların emân talebine olumlu karşılık verdi. Müslümanlarla Haçlılar arasında anlaşma yapılırken Marcel adında biri; IX. Louis’in haberi olmadığı halde “Şövalyeler! Kralımızın kesin emri, Müslümanlara kayıtsız şartsız teslim olunuz.” dedi. Haçlı şövalyeleri bu emri kralın bir isteği zannederek bütün silahlarını Müslümanlara verip teslim oldu. Böylece VII. Haçlı Seferi büyük bir hüsranla sonuçlandı. Daha sonra IX. Louis, 1270 yılında Tunus üzerine VIII. Haçlı Seferini düzenledi. Burada kendisi de dahil olmak üzere, oğlu ve ordusunun büyük bir kısmı veba hastalığına yakalanarak öldü.
The success of deep networks in medical image segmentation relies heavily on massive labeled training data. However, acquiring dense annotations is a time-consuming process. Weakly-supervised methods normally employ less expensive forms of supervision, among which scribbles started to gain popularity lately thanks to its flexibility. However, due to lack of shape and boundary information, it is extremely challenging to train a deep network on scribbles that generalizes on unlabeled pixels. In this paper, we present a straightforward yet effective scribble supervised learning framework. Inspired by recent advances of transformer based segmentation, we create a pluggable spatial self-attention module which could be attached on top of any internal feature layers of arbitrary fully convolutional network (FCN) backbone. The module infuses global interaction while keeping the efficiency of convolutions. Descended from this module, we construct a similarity metric based on normalized and symmetrized attention. This attentive similarity leads to a novel regularization loss that imposes consistency between segmentation prediction and visual affinity. This attentive similarity loss optimizes the alignment of FCN encoders, attention mapping and model prediction. Ultimately, the proposed FCN+Attention architecture can be trained end-to-end guided by a combination of three learning objectives: partial segmentation loss, a customized masked conditional random fields and the proposed attentive similarity loss. Extensive experiments on public datasets (ACDC and CHAOS) showed that our framework not just out-performs existing state-of-the-art, but also delivers close performance to fully-supervised benchmark. Code will be available upon publication.
Despite its clinical utility, medical image segmentation (MIS) remains a daunting task due to images' inherent complexity and variability. Vision transformers (ViTs) have recently emerged as a promising solution to improve MIS; however, they require larger training datasets than convolutional neural networks. To overcome this obstacle, data-efficient ViTs were proposed, but they are typically trained using a single source of data, which overlooks the valuable knowledge that could be leveraged from other available datasets. Naivly combining datasets from different domains can result in negative knowledge transfer (NKT), i.e., a decrease in model performance on some domains with non-negligible inter-domain heterogeneity. In this paper, we propose MDViT, the first multi-domain ViT that includes domain adapters to mitigate data-hunger and combat NKT by adaptively exploiting knowledge in multiple small data resources (domains). Further, to enhance representation learning across domains, we integrate a mutual knowledge distillation paradigm that transfers knowledge between a universal network (spanning all the domains) and auxiliary domain-specific branches. Experiments on 4 skin lesion segmentation datasets show that MDViT outperforms state-of-the-art algorithms, with superior segmentation performance and a fixed model size, at inference time, even as more domains are added. Our code is available at https://github.com/siyi-wind/MDViT.
Background and Objectives: Fear of childbirth is a psychological reaction that depends on various reasons during labor. This study aims to investigate the relationship between spiritual well-being and fear of childbirth in pregnant women referred to the public hospitals of Urmia City, Iran, in 2020.
Methods: This descriptive-correlational study was conducted on 300 pregnant women who referred to public hospitals in Urmia city, Iran, via the available sampling method. Data collection tools included a questionnaire regarding demographic and obstetrics information, the fear of childbirth questionnaire by Wijma and Rose (1988), and the spiritual well-being questionnaire by Paloutzian and Ellison (1983). The data were analyzed via SPSS software, version 16. Meanwhile, the independent t-test, analysis of variance, and the Pearson correlation index were analyzed at a significance level of 0.05.
Results: The results demonstrated a meaningful statistical relationship between education level, occupation, income, owning a house, religious beliefs, number of miscarriages, marital satisfaction, and the lack of previous treatment for sterility with spiritual well-being and fear of childbirth (P<0.05). Additionally, all aspects of spiritual well-being, such as religious well-being and existential well-being showed a statistically significant positive relationship with fear of childbirth (P<0.0001).
Conclusion: Based on the results of this study, higher spiritual well-being is accompanied by reduced fear of childbirth. Accordingly, considering programs to improve the spiritual well-being of pregnant women can alleviate one of the stressful and major difficulties of this population.
In the modern world, a human being comes across the double absolute priority given to the values of medical ethics. On the one hand, moral ideals are metaphysical by nature. On the other hand, a human being treats ethical standards of medical ethics pragmatically. In this aspect, the key players of the ancient world who developed the metaphysical basics of medical ethics were especially important. The study is aimed at determining the contribution of ancient thinkers into development of fundamental basics of medical ethics. The works of ancient thinkers were taken as materials for the study. The study methods are represented by system analysis, dialectic method, phenomenological and hermeneutical approaches that enable to interpret the ideas of thinkers in relation to creating the basics of medical ethics. It has been established during the study that thinking based on the integration of rational, empirical and metaphysical principles has been developed in the ancient world. Metaphysical provisions of Plato and Aristotle manifested through the works of Galen make it is possible to conclude on eclectic philosophical views of Claudius Galen. Eclecticism is not just about plain borrowing of ideas, but about new fusion of physics, logics, and metaphysics in relation to understanding human health and disease. It can be stated that the first stage of nature cognition (natural philosophy) is the most important stage of developing sense-making basics of medical ethics. This period turns into a starting point for the emerging basics of fused humanitarian and natural science- based knowledge and formation of medical ethics principles.
Dartel Ferrari Lima, Lohran Anguera Lima, Vilmar Malacarne
et al.
A exigência de os comitês de ética em pesquisa no Brasil ter representantes da comunidade entre os seus membros é explícita e ainda indefinida. Explícita porque a legislação brasileira impõe essa participação; indefinida, porque as normas vigentes não firmam as competências desse participante, somente o constitui como um braço representativo do controle social dentro do sistema que regula as condutas éticas em pesquisas com humanos. Este artigo tem como objetivo refletir os desafios da inclusão do controle social (representantes da comunidade) nos comitês de ética em pesquisa. O estudo procura mostrar quem são os possíveis representantes do controle social e, ao mesmo tempo, abre caminhos para analisar sobre essa participação. Há expectativa, ainda não comprovada, de o envolvimento do público no trabalho dos comitês ser favorecida pela imposição de normas que estabeleçam suas competências. No entanto, somente a imposição pode não ser suficiente para atender as expectativas do legislador. Conclui-se pela necessidade de adicionar capacitação permanente para os representantes de usuários, a fim de mediar a permanência e a participação efetiva do controle social nos comitês de ética.
Jurisprudence. Philosophy and theory of law, Medical philosophy. Medical ethics
Deep learning models usually suffer from domain shift issues, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data is only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. Under this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation tasks: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-center prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.
Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA), i.e. a structured process by which ADMS are assessed for consistency with relevant principles or norms, can (a) help organisations verify claims about their ADMS and (b) provide decision-subjects with justifications for the outputs produced by ADMS. In this article, we outline the conditions under which EBA procedures can be feasible and effective in practice. First, we argue that EBA is best understood as a 'soft' yet 'formal' governance mechanism. This implies that the main responsibility of auditors should be to spark ethical deliberation at key intervention points throughout the software development process and ensure that there is sufficient documentation to respond to potential inquiries. Second, we frame ADMS as parts of larger socio-technical systems to demonstrate that to be feasible and effective, EBA procedures must link to intervention points that span all levels of organisational governance and all phases of the software lifecycle. The main function of EBA should therefore be to inform, formalise, assess, and interlink existing governance structures. Finally, we discuss the policy implications of our findings. To support the emergence of feasible and effective EBA procedures, policymakers and regulators could provide standardised reporting formats, facilitate knowledge exchange, provide guidance on how to resolve normative tensions, and create an independent body to oversee EBA of ADMS.