Hasil untuk "Medical philosophy. Medical ethics"

Junrong LIU

Compared with traditional medical technologies, medical new-quality technologies demonstrate stronger autonomy, such as self-generation, replication, amplification, variation and reproduction, and can interact deeply with the intrinsic mechanisms of life systems, adapt to environmental changes dynamically, and intervene in life processes autonomously at different scales. Its intervention in natural life has led to the blurring of life boundaries and have brought more profound and ethical challenges to biosecurity, life dignity, and personal identity. Interpreting and responding to these challenges through the lens of body theory not only helps to clarify the definition of life and return to the embodied life of human beings, but also facilitates upstream governance. This approach advances ethics as a guiding principle, strengthens ethical awareness, reinforces ethical boundaries, enforces rigorous review mechanisms, and promotes global ethical co-governance.

Joshua Hatherley

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.

Jing Ai, Xiao-ling Yu, Manhui He et al.

Abstract Modern medical technology is advancing at an unstoppable pace. The scope of medical research has expanded from organs and tissues to the cellular and genetic levels, touching the essence of human life. The modern medical ethics review systems and theories, predominantly rooted in Western frameworks, have not been fully integrated with Chinese cultural contexts, leading to challenges in resolving increasingly complex ethical disputes. By reflecting on the limitations of modern medical ethics review systems and exploring the potential of integrating traditional Chinese philosophy with Western theories, this article aims to develop a framework tailored to Chinese cultural and academic contexts. This approach includes integrating conventional Chinese philosophy into the academic foundation of medical ethics, recruiting ethics committee members with expertise in Chinese culture and philosophy.

Melissa Mccradden, Oluwadara Odusi, Shalmali Joshi et al.

The problem of algorithmic bias represents an ethical threat to the fair treatment of patients when their care involves machine learning (ML) models informing clinical decision-making. The design, development, testing, and integration of ML models therefore require a lifecycle approach to bias identification and mitigation efforts. Presently, most work focuses on the ML tool alone, neglecting the larger sociotechnical context in which these models operate. Moreover, the narrow focus on technical definitions of fairness must be integrated within the larger context of medical ethics in order to facilitate equitable care with ML. Drawing from principles of medical ethics, research ethics, feminist philosophy of science, and justice-based theories, we describe the Justice, Equity, Fairness, and Anti-Bias (JustEFAB) guideline intended to support the design, testing, validation, and clinical evaluation of ML models with respect to algorithmic fairness. This paper describes JustEFAB's development and vetting through multiple advisory groups and the lifecycle approach to addressing fairness in clinical ML tools. We present an ethical decision-making framework to support design and development, adjudication between ethical values as design choices, silent trial evaluation, and prospective clinical evaluation guided by medical ethics and social justice principles. We provide some preliminary considerations for oversight and safety to support ongoing attention to fairness issues. We envision this guideline as useful to many stakeholders, including ML developers, healthcare decision-makers, research ethics committees, regulators, and other parties who have interest in the fair and judicious use of clinical ML tools.

Yu. Ganzhula, Zh. Borykbay, V. Tkachev et al.

Introduction. The health of centenarians is a major focus in global studies. Dyslipidemia is directly linked to the risk of cardiovascular diseases, which pose a growing burden on healthcare due to the increasing elderly population. Studying the lipid profiles of centenarians is important for preventing circulatory system diseases and promoting healthy aging. This research aims to compare the prevalence of dyslipidemia in centenarians (median age 96 [95-97]) with elderly individuals (median age was 69 [64 – 74]) in the Republic of Kazakhstan and examine potential predictors of dyslipidemia in the centenarian group. Methods. The study involved 46 centenarians (study group) and 82 elderly individuals (control group). Statistical analysis was used to process the data, including blood markers and demographic variables, to identify factors contributing to dyslipidemia. Results and conclusion. The prevalence of hypercholesterolemia in centenarians was 32.6% (15 people - 3 men; 12 women), with elevated LDL levels in 4.3% (2 women). In the control group, hypercholesterolemia prevalence was 29.3% (24 people - 6 men; 18 women) and elevated triglycerides in 6.1% (3 women; 2 men). The study and control groups were compared based on their lipid profile characteristics, which showed similarities as indicated by all p-values being above 0.05: Cholesterol (p=0.348), HDL (p=0.975), LDL (p=0.161), and Triglycerides (p=0.159). Decreased physical activity was a predictor of dyslipidemia in centenarians. Excessive cholesterol levels were significantly higher among women than men in both groups. The primary factor for dyslipidemia was low physical activity, with other predictors having no significant impact on the lipid profiles of centenarians. This factor should be considered when assessing cardiovascular disease risks and all-cause mortality.

R. Jean Cadigan, Margaret Waltz, John M. Conley et al.

Heritable human genome editing has garnered significant attention in scholarly and lay media, yet questions remain about whether, when, and how heritable genome editing ought to proceed. Drawing on interviews with scientists who use genome editing in their research and professionals engaged in human genome editing governance efforts, we examine their views on the permissibility of heritable genome editing and the governance strategies they see as necessary and realistic. For both issues, we found divergent views from respondents. We place the views of these scientists and governance professionals within the context of the larger bioethical discussion of heritable genome editing governance, along a continuum of hard to soft approaches. These respondents’ views highlight the challenges of various hard forms of governance and the potential virtues of soft governance approaches.

Massimiliano Esposito, Konrad Szocik, Emanuele Capasso et al.

Abstract Background Respect for human rights and bioethical principles in prisons is a crucial aspect of society and is proportional to the well-being of the general population. To date, these ethical principles have been lacking in prisons and prisoners are victims of abuse with strong repercussions on their physical and mental health. Methods A systematic review was performed, through a MESH of the following words (bioethics) AND (prison), (ethics) AND (prison), (bioethics) AND (jail), (ethics) AND (jail), (bioethics) AND (penitentiary), (ethics) AND (penitentiary), (prison) AND (human rights). Inclusion and exclusion criteria were defined and after PRISMA, 17 articles were included in the systematic review. Results Of the 17 articles, most were prevalence studies (n.5) or surveys (n.4), followed by cross-sectional studies (n.3), qualitative studies (n.1), retrospective (n.1) and an explanatory sequential mixed-methods study design (n.1). In most cases, the studies associated bioethics with prisoners’ access to treatment for various pathologies such as vaccinations, tuberculosis, hepatitis, HIV, it was also found that bioethics in prisons was related to the mental health of prisoners, disability, ageing, the condition of women, the risk of suicide or with the request for end-of-life by prisoners. The results showed shortcomings in the system of maintaining bioethical principles and respect for human rights. Conclusions Prisoners, in fact, find it difficult to access care, and have an increased risk of suicide and disability. Furthermore, they are often used as improper organ donors and have constrained autonomy that also compromises their willingness to have end-of-life treatments. In conclusion, prison staff (doctors, nurses, warders, managers) must undergo continuous refresher courses to ensure compliance with ethical principles and human rights in prisons.

Nicholas Konz, Richard Osuala, Preeti Verma et al.

Determining whether two sets of images belong to the same or different distributions or domains is a crucial task in modern medical image analysis and deep learning; for example, to evaluate the output quality of image generative models. Currently, metrics used for this task either rely on the (potentially biased) choice of some downstream task, such as segmentation, or adopt task-independent perceptual metrics (e.g., Fréchet Inception Distance/FID) from natural imaging, which we show insufficiently capture anatomical features. To this end, we introduce a new perceptual metric tailored for medical images, FRD (Fréchet Radiomic Distance), which utilizes standardized, clinically meaningful, and interpretable image features. We show that FRD is superior to other image distribution metrics for a range of medical imaging applications, including out-of-domain (OOD) detection, the evaluation of image-to-image translation (by correlating more with downstream task performance as well as anatomical consistency and realism), and the evaluation of unconditional image generation. Moreover, FRD offers additional benefits such as stability and computational efficiency at low sample sizes, sensitivity to image corruptions and adversarial attacks, feature interpretability, and correlation with radiologist-perceived image quality. Additionally, we address key gaps in the literature by presenting an extensive framework for the multifaceted evaluation of image similarity metrics in medical imaging -- including the first large-scale comparative study of generative models for medical image translation -- and release an accessible codebase to facilitate future research. Our results are supported by thorough experiments spanning a variety of datasets, modalities, and downstream tasks, highlighting the broad potential of FRD for medical image analysis.

Tao Chen, Chenhui Wang, Zhihao Chen et al.

Medical image segmentation has been significantly advanced with the rapid development of deep learning (DL) techniques. Existing DL-based segmentation models are typically discriminative; i.e., they aim to learn a mapping from the input image to segmentation masks. However, these discriminative methods neglect the underlying data distribution and intrinsic class characteristics, suffering from unstable feature space. In this work, we propose to complement discriminative segmentation methods with the knowledge of underlying data distribution from generative models. To that end, we propose a novel hybrid diffusion framework for medical image segmentation, termed HiDiff, which can synergize the strengths of existing discriminative segmentation models and new generative diffusion models. HiDiff comprises two key components: discriminative segmentor and diffusion refiner. First, we utilize any conventional trained segmentation models as discriminative segmentor, which can provide a segmentation mask prior for diffusion refiner. Second, we propose a novel binary Bernoulli diffusion model (BBDM) as the diffusion refiner, which can effectively, efficiently, and interactively refine the segmentation mask by modeling the underlying data distribution. Third, we train the segmentor and BBDM in an alternate-collaborative manner to mutually boost each other. Extensive experimental results on abdomen organ, brain tumor, polyps, and retinal vessels segmentation datasets, covering four widely-used modalities, demonstrate the superior performance of HiDiff over existing medical segmentation algorithms, including the state-of-the-art transformer- and diffusion-based ones. In addition, HiDiff excels at segmenting small objects and generalizing to new datasets. Source codes are made available at https://github.com/takimailto/HiDiff.

Xiao Ma, Yuhui Tao, Zetian Zhang et al.

Medical image segmentation is critical for clinical diagnosis, treatment planning, and monitoring, yet segmentation models often struggle with uncertainties stemming from occlusions, ambiguous boundaries, and variations in imaging devices. Traditional test-time augmentation (TTA) techniques typically rely on predefined geometric and photometric transformations, limiting their adaptability and effectiveness in complex medical scenarios. In this study, we introduced Test-Time Generative Augmentation (TTGA), a novel augmentation strategy specifically tailored for medical image segmentation at inference time. Different from conventional augmentation strategies that suffer from excessive randomness or limited flexibility, TTGA leverages a domain-fine-tuned generative model to produce contextually relevant and diverse augmentations tailored to the characteristics of each test image. Built upon diffusion model inversion, a masked null-text inversion method is proposed to enable region-specific augmentations during sampling. Furthermore, a dual denoising pathway is designed to balance precise identity preservation with controlled variability. We demonstrate the efficacy of our TTGA through extensive experiments across three distinct segmentation tasks spanning nine datasets. Our results consistently demonstrate that TTGA not only improves segmentation accuracy (with DSC gains ranging from 0.1% to 2.3% over the baseline) but also offers pixel-wise error estimation (with DSC gains ranging from 1.1% to 29.0% over the baseline). The source code and demonstration are available at: https://github.com/maxiao0234/TTGA.

Fenghe Tang, Bingkun Nian, Yingtai Li et al.

Recently, the state space model Mamba has demonstrated efficient long-sequence modeling capabilities, particularly for addressing long-sequence visual tasks in 3D medical imaging. However, existing generative self-supervised learning methods have not yet fully unleashed Mamba's potential for handling long-range dependencies because they overlook the inherent causal properties of state space sequences in masked modeling. To address this challenge, we propose a general-purpose pre-training framework called MambaMIM, a masked image modeling method based on a novel TOKen-Interpolation strategy (TOKI) for the selective structure state space sequence, which learns causal relationships of state space within the masked sequence. Further, MambaMIM introduces a bottom-up 3D hybrid masking strategy to maintain a masking consistency across different architectures and can be used on any single or hybrid Mamba architecture to enhance its multi-scale and long-range representation capability. We pre-train MambaMIM on a large-scale dataset of 6.8K CT scans and evaluate its performance across eight public medical segmentation benchmarks. Extensive downstream experiments reveal the feasibility and advancement of using Mamba for medical image pre-training. In particular, when we apply the MambaMIM to a customized architecture that hybridizes MedNeXt and Vision Mamba, we consistently obtain the state-of-the-art segmentation performance. The code is available at: https://github.com/FengheTan9/MambaMIM.

A. Dowie

Abstract Context The toolbox of instructional methods available to medical ethics educators is richly stocked and well-catalogued. However, the history of ideas relating to its contents is relatively under-researched in the medical education literature. History This paper proposes an approach to professional medical ethics education that adapts the ancient maieutic, question-asking method associated with Socratic dialogue, and particularly its uptake in educational theory developed by nineteenth and twentieth century American pragmatic philosophers, who in turn were profoundly influenced by the eighteenth century Common Sense school of philosophy from the Scottish Enlightenment. Theory The ‘ethical sense’ postulated in this article is a distant echo of moral sense in Scottish Enlightenment thought. However, ethical sense as posited here is not the natural faculty variously theorised by Scottish Enlightenment philosophers such as Francis Hutcheson and Thomas Reid, but derives from the pre-understandings of students with respect to professional medical ethics. Conclusions The ethics educator can engage the ethical sense of students through maieutic ‘teaching and learning by asking’ in relation to actual clinical narratives, beginning not with the teacher’s questions but importantly with those of the learners based on what they would need to know in order to determine the professional ethical obligations entailed.

Christopher Lisanti, Sebastian Galante, A. Betts

Medical ethics is increasingly culturally subjective. A clear understanding of medical ethics must be grounded in a clear philosophy of medicine and philosophical anthropology. Philosophically, medicine is a profession dedicated to the patient’s health. A better understanding of health or wholeness will lead to better healing. Health or wholeness is best understood as well-functioning. The philosophical and anthropological biopsychosocial-spiritual model informs the roots of well-functioning. These four interrelated dimensions must be balanced and work in harmony, ultimately aimed at the basic goods (e.g., health, life, and personal integrity among others) and should be strived for but never harmed. This holistic approach helps determine the primary cause of unwellness while also explaining that biologic healing frequently relies as much on the other dimensions as the biologic treatment. Historically, physicians refined their skills ensuring that the best biologic means addressed the most well-defined biological diseases. This dimension-specific philosophical framework formed the basis of a physician’s diagnostic investigation. If the source of unwellness was primarily in another dimension, then appropriate therapeutic referrals to dimension-specific experts were offered. This framework prevented or corrected egregious excesses due to medicalization of social issues. Traditional boundaries of medicine are now challenged by contraceptives, elective abortion, cosmetic procedures, and euthanasia/physician-assisted suicide, steering medicine away from treating biologic problems with biologic solutions towards treating psychologic and/or social problems with biologic solutions. Countries now inconsistently require their physicians to provide biologic means aimed at specific psychologic and/or social goals to advance sexual and/or economic goals, the country’s laws, or patient autonomy. This is conceptually flawed requiring special exemptions from the profession of medicine while also resulting in biologic harm, damage to the basic goods, and negative psychologic and/or social effects, while transforming medicine into a commodity. Physicians have an ethical obligation to provide effective biologic means to treat biologic diseases while promoting healing and wholeness among all four dimensions. On the other hand, physicians have no obligation to use biologic means for non-biologic problems. This multi-dimensional model with dimension-specific therapies is conceptually consistent, socially agnostic, empirically sound, and provides a clearer understanding of medical ethical obligations.

Edip AKYOL

İnsan, yaratıcının özel bir statü verdiği varlıklar arasında mükemmel bir donanıma sahip olmasının yanı sıra, doğuştan veya sonradan gelişen engellilik durumu da insanlık tarihinde yaygın ve yadsınamaz bir gerçekliktir. İnsan, sadece mükemmel ve sağlıklı bir şekilde var olmanın ötesinde, yaşamın karmaşıklığını ve çeşitliliğini engellilikle mücadele eden bireylerin varlığıyla birlikte deneyimlemektedir. Bu durum, insan varlığının zenginlik ve çeşitliliği içinde, her bireyin benzersiz bir hikâyeye sahip olduğu evrensel bir gerçekliği yansıtmaktadır. Aslında dezavantajlı bireylere olan bakış, o toplumdaki insanların ahlaki seviyelerini ve insana verdikleri değeri de göstermektedir. Seri editörlüğünü Adem APAK’ın yaptığı “Tüm İnsanların Peygamberi” konulu bir projede, on başlık altında Hz. Peygamber’in çocuklar, gençler, yaşlılar, kadınlar, devlet görevlileri, varlıklılar, yoksullar, yakınlar, gayri müslimler ve engellilerle ilişkileri serisinin 10’uncu kitabı olarak “Hz. Peygamber (sav) ve Engelliler” adıyla alanın uzmanlarından Cuma KARAN tarafından telif edilen bu eser, toplumumuzun neredeyse yüzde onunu aşan “engelliler” ile ilgili farkındalık oluşturması açısından önemlidir. Eserin değerlendirilmesine yönelik olmak üzere; Engelli insanların horlandığı bir dönemde gerek bu durumu düzeltmeye yönelik inen âyetler ve gerekse Hz. Peygamber’in engelli sahâbîlere gösterdiği ilgi, empati ve anlayışla dolu iletişimi, engelli bireylerin toplum içinde etkin bir şekilde yer almalarını teşvik etmesinin önemi ve bu değerleri güncel yaşantımıza entegre etmek gibi konular üzerinde durulmuştur.

Pedro Dias Ramos, Maria Strecht Almeida, I. Anna S. Olsson

CRISPR-Cas9 revolutionized the precise editing of mammalian cells genome. The present study explores genome editing (GE) in the context of the Responsible Research and Innovation framework for emerging technologies, through semi-structured interviews with life sciences researchers worldwide. Our study demonstrates that for researchers in the field, GE technology is viewed as promising but also harboring unsolved challenges. These experts call for complementary research to improve the technology and increase knowledge of the genome function. They clearly do not support what they perceive as unsafe, unpredictable and irrelevant applications, and they view the lack of international harmonization of regulation in combination with cultural differences in public attitude as difficult challenges. Interviewees see public misconceptions as a problem while recognizing the need to foster a clear science-society dialogue with informed citizens. This study with scientists provides insight into the science-based priorities for GE to be a technology that can be responsibly applied.

Ahmed Ragib Chowdhury, Sal Sabil Chowdhury, Arif Jamil

Pragmatism is a school of moral philosophy, and of contemporary in origin, comparing to the other schools of moral philosophy. It evaluates the action based on the practical applicability and relevance. Pragmatism can, therefore, be useful to make a law that govern the society contemporary and relevant for the constantly changing world. Biomedical laws in particular are needed to correspond to the changing standards and good practices in tandem with their advancements. This paper will assess from a pragmatic point of view, the efficacy of the biomedical laws of Bangladesh.

Maciej A. Mazurowski, Haoyu Dong, Hanxue Gu et al.

Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. While the performance on natural images is impressive, medical image domains pose their own set of challenges. Here, we perform an extensive evaluation of SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies. We report the following findings: (1) SAM's performance based on single prompts highly varies depending on the dataset and the task, from IoU=0.1135 for spine MRI to IoU=0.8650 for hip X-ray. (2) Segmentation performance appears to be better for well-circumscribed objects with prompts with less ambiguity and poorer in various other scenarios such as the segmentation of brain tumors. (3) SAM performs notably better with box prompts than with point prompts. (4) SAM outperforms similar methods RITM, SimpleClick, and FocalClick in almost all single-point prompt settings. (5) When multiple-point prompts are provided iteratively, SAM's performance generally improves only slightly while other methods' performance improves to the level that surpasses SAM's point-based performance. We also provide several illustrations for SAM's performance on all tested datasets, iterative segmentation, and SAM's behavior given prompt ambiguity. We conclude that SAM shows impressive zero-shot segmentation performance for certain medical imaging datasets, but moderate to poor performance for others. SAM has the potential to make a significant impact in automated medical image segmentation in medical imaging, but appropriate care needs to be applied when using it.

Emma A. M. Stanley, Raissa Souza, Anthony Winder et al.

Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. Moreover, reweighing was identified as the most successful bias mitigation strategy for this setup, and we demonstrated how explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Developing fair AI models is a considerable challenge given that many and often unknown sources of biases can be present in medical imaging datasets. In this work, we present a novel methodology to objectively study the impact of biases and mitigation strategies on deep learning pipelines, which can support the development of clinical AI that is robust and responsible.

Yongsong Huang, Wanqing Xie, Mingzhen Li et al.

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a "free lunch", its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.

S. K. M Shadekul Islam, MD Abdullah Al Nasim, Ismail Hossain et al.

The diagnosis and treatment of various diseases had been expedited with the help of medical imaging. Different medical imaging modalities, including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging, Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies for in vivo imaging modalities is presented in this chapter, in addition to these modalities, some advanced techniques such as contrast-enhanced MRI, MR approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data mining and search. Despite its important role and potential effectiveness as a diagnostic tool, reading and interpreting medical images by radiologists is often tedious and difficult due to the large heterogeneity of diseases and the limitation of image quality or resolution. Besides the introduction and discussion of the basic principles, typical clinical applications, advantages, and limitations of each modality used in current clinical practice, this chapter also highlights the importance of emerging technologies in medical imaging and the role of data mining and search aiming to support translational clinical research, improve patient care, and increase the efficiency of the healthcare system.