Hasil untuk "Medical philosophy. Medical ethics"

Ting ZHANG, Yajun CHENG

This paper aims to transcend traditional semantic interpretations of the phrase "medicine is perception" by engaging in a philosophical dialogue with Heidegger's ontology. It re-examines the essence of perception through the lens of existential thought. The emergence of "medicine is perception" is rooted in "Dao" of medicine, mediated through the manifestations of illness, and materialized in therapeutic expressions. The reflection on perception in medicine and the poetic meditation on being share a common pursuit of fundamental questioning. While poetic thinking seeks being through a process of subtraction, the emergence of medical perception, as a form of spiritual thinking, requires both sensory and rational cognition. Ultimately, both perception in medicine and poetic thinking on being presuppose the dissolution of the subject-object dichotomy, challenging the positivist logic of current evidence-based medicine.

Michael Klenk

This chapter discusses the ethics of generative AI. It provides a technical primer to show how generative AI affords experiencing technology as if it were human, and this affordance provides a fruitful focus for the philosophical ethics of generative AI. It then shows how generative AI can both aggravate and alleviate familiar ethical concerns in AI ethics, including responsibility, privacy, bias and fairness, and forms of alienation and exploitation. Finally, the chapter examines ethical questions that arise specifically from generative AI's mimetic generativity, such as debates about authorship and credit, the emergence of as-if social relationships with machines, and new forms of influence, persuasion, and manipulation.

Anand Kumar, Kavinder Roghit Kanthen, Josna John

We can achieve fast and consistent early skin cancer detection with recent developments in computer vision and deep learning techniques. However, the existing skin lesion segmentation and classification prediction models run independently, thus missing potential efficiencies from their integrated execution. To unify skin lesion analysis, our paper presents the Gaussian Splatting - Transformer UNet (GS-TransUNet), a novel approach that synergistically combines 2D Gaussian splatting with the Transformer UNet architecture for automated skin cancer diagnosis. Our unified deep learning model efficiently delivers dual-function skin lesion classification and segmentation for clinical diagnosis. Evaluated on ISIC-2017 and PH2 datasets, our network demonstrates superior performance compared to existing state-of-the-art models across multiple metrics through 5-fold cross-validation. Our findings illustrate significant advancements in the precision of segmentation and classification. This integration sets new benchmarks in the field and highlights the potential for further research into multi-task medical image analysis methodologies, promising enhancements in automated diagnostic systems.

Jeffrey H. Siewerdsen

The science and clinical practice of medical physics has been integral to the advancement of radiology and radiation therapy for over a century. In parallel, advances in surgery - including intraoperative imaging, registration, and other technologies within the expertise of medical physicists - have advanced primarily in connection to other disciplines, such as biomedical engineering and computer science, and via somewhat distinct translational paths. This review article briefly traces the parallel and convergent evolution of such scientific, engineering, and clinical domains with an eye to a potentially broader, more impactful role of medical physics in research and clinical practice of surgery. A review of image-guided surgery technologies is offered, including intraoperative imaging, tracking / navigation, image registration, visualization, and surgical robotics across a spectrum of surgical applications. Trends and drivers for research and innovation are traced, including federal funding and academic-industry partnership, and some of the major challenges to achieving major clinical impact are described. Opportunities for medical physicists to expand expertise and contribute to the advancement of surgery in the decade ahead are outlined, including research and innovation, data science approaches, improving efficiency through operations research and optimization, improving patient safety, and bringing rigorous quality assurance to technologies and processes in the circle of care for surgery. Challenges abound but appear tractable, including domain knowledge, professional qualifications, and the need for investment and clinical partnership.

Praveenbalaji Rajendran, Mojtaba Safari, Wenfeng He et al.

Recent advancements in artificial intelligence (AI), particularly foundation models (FMs), have revolutionized medical image analysis, demonstrating strong zero- and few-shot performance across diverse medical imaging tasks, from segmentation to report generation. Unlike traditional task-specific AI models, FMs leverage large corpora of labeled and unlabeled multimodal datasets to learn generalized representations that can be adapted to various downstream clinical applications with minimal fine-tuning. However, despite the rapid proliferation of FM research in medical imaging, the field remains fragmented, lacking a unified synthesis that systematically maps the evolution of architectures, training paradigms, and clinical applications across modalities. To address this gap, this review article provides a comprehensive and structured analysis of FMs in medical image analysis. We systematically categorize studies into vision-only and vision-language FMs based on their architectural foundations, training strategies, and downstream clinical tasks. Additionally, a quantitative meta-analysis of the studies was conducted to characterize temporal trends in dataset utilization and application domains. We also critically discuss persistent challenges, including domain adaptation, efficient fine-tuning, computational constraints, and interpretability along with emerging solutions such as federated learning, knowledge distillation, and advanced prompting. Finally, we identify key future research directions aimed at enhancing the robustness, explainability, and clinical integration of FMs, thereby accelerating their translation into real-world medical practice.

Jingyun Chen, Yading Yuan

Federated Learning (FL) presents a promising avenue for collaborative model training among medical centers, facilitating knowledge exchange without compromising data privacy. However, vanilla FL is prone to server failures and rarely achieves optimal performance on all participating sites due to heterogeneous data distributions among them. To overcome these challenges, we propose Gossip Contrastive Mutual Learning (GCML), a unified framework to optimize personalized models in a decentralized environment, where Gossip Protocol is employed for flexible and robust peer-to-peer communication. To make efficient and reliable knowledge exchange in each communication without the global knowledge across all the sites, we introduce deep contrast mutual learning (DCML), a simple yet effective scheme to encourage knowledge transfer between the incoming and local models through collaborative training on local data. By integrating DCML with other efforts to optimize site-specific models by leveraging useful information from peers, we evaluated the performance and efficiency of the proposed method on three publicly available datasets with different segmentation tasks. Our extensive experimental results show that the proposed GCML framework outperformed both centralized and decentralized FL methods with significantly reduced communication overhead, indicating its potential for real-world deployment. Upon the acceptance of manuscript, the code will be available at: https://github.com/CUMC-Yuan-Lab/GCML.

C. McGovern

Moghaddameh Mirzaee, Firoozeh Mirzaee

Abstract Background The existence of a valid instrument to evaluate the attitude of mothers towards compliance with medical ethics during childbirth can lead to appropriate interventions to create a positive attitude. The purpose of this study is to determine the construct validity of the MEAVDQ (Medical Ethics Attitude in Vaginal Delivery Questionnaire). Methods The study was carried out with 350 women. The main research instrument was MEAVDQ. This 59-item questionnaire comprises three parts A, B, J. Part A is concerned with the first principles. Part B deals with the second and third principles and part J addresses the fourth principle of medical ethics. Structural Equations Modeling (SEM) was used to determine the construct validity of MEAVDQ. Results The results of SEM revealed that there was a positive correlation between structures A and B. The relationship between structures B and J was also positive and significant. On the other hand, there was a direct and indirect relationship between structures A and J. One-unit increase in structure A led to 0.16 (95% CI: 0.01, 0.33) direct increase in structure J. Also, one-unit increase score increases in structure A caused 0.39 indirect rise (95% CI: 0.26, 0.53) in structure J with the mediating role of the structure B. Conclusions It can be suggested to midwifery policy maker and midwives that respect for the first principle of medical ethics and autonomy is the most important principle of medical ethics in childbirth. By respecting the autonomy of mothers, a positive birth experience can be created for them.

Xiaojie Li, Yali Cong

Abstract Background The impetus for policies promoting medical data sharing in China has gained significant traction. Nonetheless, the present legal and ethical framework governing the research use of medical data in China, is characterized by a more restrictive rather than permissive approach. The proportion of Chinese medical data being leveraged for scientific research still has room for improvement at present, indicating a significant untapped potential for advancing medical knowledge and improving healthcare outcomes. Building upon this research, we aim to delve deeper into the challenges researchers encounter in the sharing of medical data through focus group interviews. Methods We conducted two focus group interviews study with researchers representing diverse disciplines to explore their perspectives on 21 June 2021 and 28 July 2021. A total of seventeen researchers willingly participated in this study, representing various professional backgrounds. Similar codes were merged. Research team discussions were also utilized to select interviewees’ statements that were regarded as typical or representative. Results The respondents demonstrated a strong understanding that medical data should not be disseminated arbitrarily, recognizing the importance of sharing data in compliance with laws. Through the interview, we found that although respondents stressed the importance of careful consideration regarding if and when this information can be responsibly released, none of the respondents raised the issue of necessitating consent from data subjects for the research use of medical data. This observation sharply contrasts with the stringent separate consent provisions for secondary data use outlined in the PIPL. Conclusions The findings from the focus group studies shed light on researchers’ barriers and ethical challenges towards medical data sharing for scientific research, highlighting their deep concern for data security and cautious approach to sharing. The key objectives aimed at facilitating and enabling the reuse of medical data encompass enhancing interoperability, harmonizing data standards, improving data quality, safeguarding privacy, ensuring informed consent, incentivizing patients, and establishing explicit regulations pertaining to data access and utilization.

Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil et al.

Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotations to be accurately solved. However, there exist alternative, potentially weaker, forms of supervision, such as accompanying free-text reports, which are readily available. The task of performing localization with textual guidance is commonly referred to as phrase grounding. In this work, we use a publicly available Foundation Model, namely the Latent Diffusion Model, to perform this challenging task. This choice is supported by the fact that the Latent Diffusion Model, despite being generative in nature, contains cross-attention mechanisms that implicitly align visual and textual features, thus leading to intermediate representations that are suitable for the task at hand. In addition, we aim to perform this task in a zero-shot manner, i.e., without any training on the target task, meaning that the model's weights remain frozen. To this end, we devise strategies to select features and also refine them via post-processing without extra learnable parameters. We compare our proposed method with state-of-the-art approaches which explicitly enforce image-text alignment in a joint embedding space via contrastive learning. Results on a popular chest X-ray benchmark indicate that our method is competitive with SOTA on different types of pathology, and even outperforms them on average in terms of two metrics (mean IoU and AUC-ROC). Source code will be released upon acceptance at https://github.com/vios-s.

Jianfei Liu, Christopher Parnell, Ronald M. Summers

Accurate training labels are a key component for multi-class medical image segmentation. Their annotation is costly and time-consuming because it requires domain expertise. This work aims to develop a dual-branch network and automatically improve training labels for multi-class image segmentation. Transfer learning is used to train the network and improve inaccurate weak labels sequentially. The dual-branch network is first trained by weak labels alone to initialize model parameters. After the network is stabilized, the shared encoder is frozen, and strong and weak decoders are fine-tuned by strong and weak labels together. The accuracy of weak labels is iteratively improved in the fine-tuning process. The proposed method was applied to a three-class segmentation of muscle, subcutaneous and visceral adipose tissue on abdominal CT scans. Validation results on 11 patients showed that the accuracy of training labels was statistically significantly improved, with the Dice similarity coefficient of muscle, subcutaneous and visceral adipose tissue increased from 74.2% to 91.5%, 91.2% to 95.6%, and 77.6% to 88.5%, respectively (p<0.05). In comparison with our earlier method, the label accuracy was also significantly improved (p<0.05). These experimental results suggested that the combination of the dual-branch network and transfer learning is an efficient means to improve training labels for multi-class segmentation.

Krishan Agyakari Raja Babu, Rachana Sathish, Mrunal Pattanaik et al.

Synthetic data is becoming increasingly integral in data-scarce fields such as medical imaging, serving as a substitute for real data. However, its inherent statistical characteristics can significantly impact downstream tasks, potentially compromising deployment performance. In this study, we empirically investigate this issue and uncover a critical phenomenon: downstream neural networks often exploit spurious distinctions between real and synthetic data when there is a strong correlation between the data source and the task label. This exploitation manifests as \textit{simplicity bias}, where models overly rely on superficial features rather than genuine task-related complexities. Through principled experiments, we demonstrate that the source of data (real vs.\ synthetic) can introduce spurious correlating factors leading to poor performance during deployment when the correlation is absent. We first demonstrate this vulnerability on a digit classification task, where the model spuriously utilizes the source of data instead of the digit to provide an inference. We provide further evidence of this phenomenon in a medical imaging problem related to cardiac view classification in echocardiograms, particularly distinguishing between 2-chamber and 4-chamber views. Given the increasing role of utilizing synthetic datasets, we hope that our experiments serve as effective guidelines for the utilization of synthetic datasets in model training.

Islam G. Ali

Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) represent versatile tools with diverse applications spanning physics, chemistry, geology, and medical science. This comprehensive review explores the foundational principles of NMR and MRI technologies, elucidating their evolution from fundamental quantum mechanical concepts to widespread applications in medical science. Commencing within a quantum mechanical framework, the concise review emphasizes the significant role played by NMR and MRI in clinical research. Furthermore, it provides a succinct survey of various NMR system types. Conclusively, the review delves into key applications of MRI techniques, presenting valuable methodologies for visualizing internal anatomical structures and soft tissues.

Dennis Hein, Afshin Bozorgpour, Dorit Merhof et al.

Physics-inspired Generative Models (GMs), in particular Diffusion Models (DMs) and Poisson Flow Models (PFMs), enhance Bayesian methods and promise great utility in medical imaging. This review examines the transformative role of such generative methods. First, a variety of physics-inspired GMs, including Denoising Diffusion Probabilistic Models (DDPMs), Score-based Diffusion Models (SDMs), and Poisson Flow Generative Models (PFGMs and PFGM++), are revisited, with an emphasis on their accuracy, robustness as well as acceleration. Then, major applications of physics-inspired GMs in medical imaging are presented, comprising image reconstruction, image generation, and image analysis. Finally, future research directions are brainstormed, including unification of physics-inspired GMs, integration with Vision-Language Models (VLMs), and potential novel applications of GMs. Since the development of generative methods has been rapid, this review will hopefully give peers and learners a timely snapshot of this new family of physics-driven generative models and help capitalize their enormous potential for medical imaging.

Samuel Dale

Photo 29553598 / Aristotle © Eleftherios Damianidis | Dreamstime.com INTRODUCTION Bioethics does not have an explicitly stated and agreed upon means of resolving conflicts between normative theories. As such, bioethics lacks an essential feature – action guidance ― an effective translation from theory to practice. While the normative approaches and historical precedents of bioethics may discourage overtly egregious acts, the bioethical discipline does not offer decisive guidance in situations with multiple competing normative approaches. For example, Utilitarians and Kantians offer diametrically opposed guidance in emblematic cases like the trolley problem in which saving a greater number of people conflicts with the imperative to treat persons as ends-in-themselves rather than a means to an end. The predominant framework in bioethics, principlism, also suffers from a lack of action guidance.[1] The consequences of a ‘toothless’ bioethics impeded by misaligned principles and conflicting normative theories are disastrous – not only in death count but also in moral injury and societal fracture. This paper argues that while there is no ‘one theory to rule them all,’ a virtue-based approach to bioethics can ameliorate the adjudication problem. Bioethics ought to embody moral strength but has often provided indecisive guidance due to its awkward theoretical architecture. In defence of bioethics, many actors control societal level decision making. Thus, the onus does not rest entirely on bioethicists but also leaders in government and healthcare. This paper critiques principlism as internally incongruous, as it is composed of elements from multiple ethical theories. Understanding this, it is seen that the entirety of theoretical bioethics, as composed of conflicting normative approaches, also suffers from this action-guidance problem.[2] l.     The Birth of Bioethics Amid Tragedy Bioethics was born out of tragedy. During the Nuremberg Trials of 1946-47, a cohort of French, American, British, and Soviet judges forced the Nazi doctors and architects of the Holocaust to stand trial for their egregious actions and feel the firm hand of justice. In an example of ex post facto law, the global community identified unethical action and indicted Germans for breaking natural law.[3] As a result, the Nuremberg Code arose to prevent crimes against human research subjects. It outlines the parameters of ethical research and is a foundational document of modern bioethics.[4] Early bioethics pronounced immorality and offered decisive guidance, laying the groundwork for an internationally unified theory of negative morality – that which is never permissible. Tuskegee was another foundational tragedy in the history of bioethical discipline. In 1932, the US Public Health Service recruited six hundred African American men from Macon County, Alabama for a study on the effects of untreated syphilis.[5] The researchers failed to obtain informed consent and intentionally withheld information regarding the disease or the nature of the study. The researchers did not offer any men the cure, penicillin, which was discovered midway through the experiment. Many men died during the study. The perpetrators evaded justice until 1972. Tuskegee sparked a new paradigm of bioethics, including the US federal policies, the establishment of ethics review boards, and informed consent as a core tenet of biomedical practice.[6] The National Research Act of 1974 and the Belmont Report of 1978 laid new ground for research ethics and set the tone for the contemporary practice of bioethics. ll.     The Rise of Principlism These two cases demonstrate the nature of the early days of bioethics. It largely lacked high-level theory and appealed more to generally agreed upon moral facts and common-sense morality. However, as medicine advanced, increasingly complex biomedical issues created problems that required greater appeals to theory.[7] The “heroic” phase of bioethics saw “theorists aspire to construct symmetrical cathedrals of normative thought.”[8] In the wake of the Tuskegee Syphilis Study, Tom Beauchamp and James Childress helped draft the Belmont Report, a bulwark intended to prevent future atrocities in human research trials. The document aimed to curtail the utilitarianism implicit in medical research and add essential considerations of the subjects themselves, including respect for persons, beneficence, and justice.[9] It also served as the bedrock of the theoretical architecture of principlism. In 1979, Beauchamp and Childress’ published Principles of Biomedical Ethics, which is arguably the most influential text in bioethics scholarship. It attempts to incorporate some main theoretical approaches to ethics in a unified moral theory: autonomy reflects the work of Kant; beneficence aligns with utilitarianism; non-maleficence is reminiscent of Hippocrates; and justice borrows heavily from Rawls.[10] These four principles have become canonical in academic bioethics. However, doubts remain as to their effectiveness in guiding action toward ethical aims given how scholars contend that “ethical expertise cannot be codified in principles.”[11]  lll.     A Critique of Principlism Clouser & Gert say: At best, ‘principles’ operate primarily as checklists naming issues worth remembering when considering a biomedical moral issue. At worst ‘principles’ obscure and confuse moral reasoning by their failure to be guidelines and by their eclectic and unsystematic use of moral theory.[12] To this point, principlism is no more than a flashlight – a tool to illuminate the ethical landscape. Viewing cases through the lens of moral principles can reveal the salient moral features, but it ultimately provides no guidance for adjudication, hereby referred to as the adjudication problem. Consequently, the doctor’s moral intuition has de facto weight, and the principles are merely a post hoc justification for any given action they choose. Using the four principles to decide the right course of moral action is “tantamount to using two, three, or four conflicting moral theories to decide a case.”[13] Principlism attempts to reap the benefit of multiple ethical theories, each with unambiguous goals. When blended, the result is discordant directives. These conflicting principles “provide no systematic guidance” for real world dilemmas.[14] Other ethical theories have faults too. Kantians leave no room for exceptions for exigency, and utilitarianism ‘crosses the line’ far too often. At least these theories decisively guide action and provide unambiguous justification for doing so. Utilitarianism is quite measurable: “Provide the greatest good for the greatest number” – sure! Done. Kant’s ethical imperative has a clear rule: “Never treat humans as a mere means to an end” – certainly, will do. Principlism merely provides “a check list of considerations” that doctors can cross off one by one before going about their originally intended course of action.[15] Worse, the internally disharmonious nature of principlism allows doctors to justify ethically dubious decisions. An important goal of bioethics is avoiding the following scenario: a doctor faces with a moral dilemma. He can choose Option A or Option B. Let’s say B is morally preferrable on a consensus view. However, his moral intuition guides him toward Option A. Having completed his required course on biomedical ethics in medical school, he recalls a few theories which are relevant to his case. He considers the four principles but autonomy conflicts with beneficence, which does not yield a straightforward, practical directive, so he disregards principlism for the case at hand. Kantian ethics disagrees with his intuition, but utilitarianism may support it. He goes ahead with Option A, claiming utilitarianism supported his actions. He, therefore, provides post hoc justification for Option A, using whichever theory agrees with his judgment.  Reliance on intuition when the principles conflict is an intractable problem “unless one is willing to grant privileged epistemological status to the moral judgments (calling them "intuitions") or to the moral principles (calling them "self-evident" or otherwise a priori”).[16] Neither deserves a privileged epistemological status. Moral intuitions can possess prejudice or ignorance, and moral principles can demonstrably conflict, offering no guidance. Realistically, most people “pay little attention to theories when they make moral decisions,” and when they do, post hoc rationalization often follows. When discipline is used as an afterthought, it provides justifications for potentially unethical actions. lV.     Virtue Ethics: A Provisional Solution Virtue ethics may provide a workaround. It emphasizes the disposition and character of the moral agent instead of abstract theories, making it a practical choice. As Jacobson writes, “ethical dictates cannot be codified in general rules applicable to particular situations by someone who lacks virtue.”[17] Ethical theories can still highlight moral lapses and dilemmas, but since they do not decisively guide action, bioethics must focus on moral agents’ decision-making abilities. Aristotelian virtue as a provisional solution to the adjudication problem also accounts for the “multiple and heterogeneous” particularities which other theories often neglect.[18] Aristotle said that "phronesis [practical wisdom] deals with the ultimate particular and this is done by perception (aisthesis) rather than science (episteme).”[19] Scientific knowledge in the case of bioethics may appropriately refer to medical facts. Perception refers to the moral intuition of an individual agent as applied to a given scenario. Jonsen goes further, however, interpreting this perception as “the appreciative sight of a constellation of ideas, arguments, and facts about the case, seen as a whole.”[20] Phronesis, or practical wisdom, is the cardinal virtue of Aristotelian virtue ethics. It enables the agent to consider the relevant facts and act in the most prudent, courageous, or tempered manner. This paper proposes that in the face of intractable theoretical disagreements, the only way forward for bioethics is to educate bioethics practitioners and students in this tradition. V.     Counterargument So far, this paper has argued that bioethics is relatively toothless and needs to give clear guidance due to theoretical disagreements and the intractable differences between normative approaches. And yet, some may object to the notion that bioethics ought to have these proverbial teeth. In this view, bioethics merely acts as a sounding board for those in executive roles (doctors, lawyers, politicians) to better understand the moral landscape of the problem. To them, bioethics’ failure to decisively guide action is acceptable because it should not. If this is the case, then bioethics need not speak with one voice and should cherish the long-standing, obstinate disagreements between different theoretical camps. But this paper contends the opposite. If bioethics continues to offer conflicting imperatives and fails to demonstrably guide individuals, hospitals, and society toward clear ethical aims and outcomes, it has failed as a discipline. One might argue that virtue theory is not an ideal framework to replace principlism because individuals approach ethical problems in many ways based on features of their character and background. Injecting one’s character into moral decisions can lead to bias. As Carl Elliot writes, “how a moral problem is described will turn on an array of variables: the role and degree of involvement in the case of the person who is describing it, the person’s particular profession or discipline, her religious and cultural inheritance-indeed, with all of the intangibles that have contributed to her character.”[21] Self-awareness may counteract personal biases in moral decision making. Vl.     Limitation Virtue ethics is only a provisional solution to the adjudication problem for two reasons. One, not everyone is inherently virtuous, and two, theoretical differences may be resolved. If deontology and consequentialism can be incorporated into a unified theory for bioethics, then virtue ethics may not be necessary. On a certain view, it would be ideal for ethics to be computational – plug in the relevant variables and receive the morally correct answer. Arguably, principlism was an attempt at such a matrix, but it ultimately failed as a unified theory. Rather than waiting for a perfect unified theory, we must count on the genuine virtue of the moral agents who make ethically important decisions from policy to bedside. If practical wisdom is not a characteristic of these agents, then their decisions will not be as ethical as they ought to be, and no theory is the panacea to such a problem. CONCLUSION Bioethics emerged out of unified responses to clear cases of moral depravity, like the Holocaust and Tuskegee, and perhaps bioethics is most appropriate for such cases which are conducive to moral certitude. At minimum, bioethics offers meaningful guidance in cases where the relevant duties align with beneficent consequences. For example, in both the Nuremberg and Tuskegee cases, abrogating fundamental duties to humanity led to grievous consequences. The principles developed in the wake of such problems led to a conflict between autonomy and beneficence, which perhaps mirror the conflict between Kantian deontology and utilitarianism. Bioethics excels when deontology and utilitarianism are aligned, but most of the time, they are not. In such instances, virtue is needed to adjudicate conflicting normative approaches and resolve theoretical tensions with practical wisdom and courage. - [1] Clouser, K. D., & Gert, B. (1990). A Critique of Principlism, The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine, Volume 15, Issue 2, April 1990, Pages 219–236, https://doi.org/10.1093/jmp/15.2.219 [2] Clouser, K. D., & Gert, B. (1990). [3] Annas, G. J. (2010). The legacy of the Nuremberg Doctors’ Trial to American bioethics and human rights. In Medicine After the Holocaust (pp. 93-105). Palgrave Macmillan, New York. https://scholarship.law.umn.edu/mjlst/vol10/iss1/4 [4] Annas, G. J. (2010). The legacy of the Nuremberg Doctors’ Trial to American bioethics and human rights. In Medicine After the Holocaust (pp. 93-105). Palgrave Macmillan, New York. https://scholarship.law.umn.edu/mjlst/vol10/iss1/4 [5] Barrett, L. A. (2019). Tuskegee Syphilis Study of 1932-1973 and the Rise of Bioethics as Shown through Government Documents and Actions. DttP, 47, 11. https://heinonline.org/HOL/LandingPage?handle=hein.journals/dttp47&div=36&id=&page= [6] Barrett, L. A. (2019). [7] Annas, G. J. (2010). [8] Annas, G. J. (2010). [9] Adashi, E. Y., Walters, L. B., & Menikoff, J. A. (2018). The Belmont Report at 40: reckoning with time. American Journal of Public Health, 108(10), 1345-1348. https://doi.org/10.2105/AJPH.2018.304580 [10] Beauchamp, T. L., & Childress, J. F. (2001). Principles of Biomedical Ethics. Oxford University Press, USA. [11] Jacobson, D. (2005). Seeing by feeling: virtues, skills, and moral perception. Ethical Theory and Moral Practice, 8(4), 387-409. https://doi.org/10.1007/s10677-005-8837-1 [12] Clouser, K. D., & Gert, B. (1990). [13] Clouser, K. D., & Gert, B. (1990). [14] Clouser, K. D., & Gert, B. (1990). [15] Clouser, K. D., & Gert, B. (1990). [16] Daniels, N. (1979). Wide Reflective Equilibrium and Theory Acceptance in Ethics. The Journal of Philosophy, 76(5), 256-282. https://doi.org/10.2307/2025881 [17] Jacobson, D. (2005). [18] Jonsen, A. R. (1991). Of balloons and bicycles—or—the relationship between ethical theory and practical judgment. Hastings Center Report, 21(5), 14-16. https://doi.org/10.2307/3562885 [19] Jonsen, A. R. (1991), p. 15. [20] Jonsen, A. R. (1991), p. 15. [21] Elliott, C. (1992). Where ethics comes from and what to do about it. Hastings Center Report, 22(4), 28-35. https://onlinelibrary.wiley.com/doi/pdf/10.2307/3563021  

Junyu Chen, Yihao Liu, Shuwen Wei et al.

Deep learning technologies have dramatically reshaped the field of medical image registration over the past decade. The initial developments, such as regression-based and U-Net-based networks, established the foundation for deep learning in image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, network architectures, and uncertainty estimation. These advancements have not only enriched the field of image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration.

P. Woo, Danise M Au, N. M. W. Ko et al.

Background: Myths and religion are belief systems centered around supernatural entities that attempt to explain the observed world and are of high importance to certain communities. The former is a collection of stories that belong to a cultural tradition and the latter are organized faiths that determine codes of ethics, rituals and philosophy. Deities or monstrous creatures in particular act as archetypes instructing an individual’s conduct. References to them in Greek mythology and Christianity are frequently manifested in the modern neurosurgical vernacular. Methods: A review of the medical literature was performed using the PubMed and MEDLINE bibliographic databases. Publications from 1875 to 2021 related to neurosurgery or neuroanatomy with the medical subject headings (MeSH) terms mythology, religion, Christianity and Catholicism were reviewed. References pertaining to supernatural beings were classified to either a deity or a monstrosity according to their conventional cultural context. Results: Twelve narratives associated with neurosurgery were identified, nine relating to Greek mythology and three associated with the Christian-Catholic faith. Eight accounts concerned deities and the remaining with monstrous creatures. Conclusion: This article explores the etymology of commonly utilized terms in daily neurosurgical practice in the context of mythology and religion. They reveal the ingenuity and creativity of early pioneers who strived to understand the brain.

J. Keune, Erica K Salter

Abstract Teaching healthcare ethics at the doctoral level presents a particular challenge. Ethics is often taught to medical students, but rarely is medicine taught to graduate students in health care ethics. In this paper, Medicine for Ethicists [MfE] — a course taught both didactically and experientially — is described. Eight former MfE students were independently interviewed in a semi-structured, open-ended format regarding their experience in the experiential component of the course. Themes included concrete elements about the course, elements related to the broader PhD student learning experience, and themes related to the students’ past and future career experiences. Findings are related to the educational philosophy of John Dewey and David Kolb’s experiential learning theory. Broader implications of this work are explored.

The emergence of digital platforms and the new application economy are transforming healthcare and creating new opportunities and risks for all stakeholders in the medical ecosystem. Many of these developments rely heavily on data and AI algorithms to prevent, diagnose, treat, and monitor diseases and other health conditions. A broad range of medical, ethical and legal knowledge is now required to navigate this highly complex and fast-changing space. This collection brings together scholars from medicine and law, but also ethics, management, philosophy, and computer science, to examine current and future technological, policy and regulatory issues. In particular, the book addresses the challenge of integrating data protection and privacy concerns into the design of emerging healthcare products and services. With a number of comparative case studies, the book offers a high-level, global, and interdisciplinary perspective on the normative and policy dilemmas raised by the proliferation of information technologies in a healthcare context.

Barbara Prainsack, Maria do Céu Patrão Neves, N. Sahlin et al.

Department of Political Science, University of Vienna, Austria Faculty of Social Sciences and Humanities, University of the Azores, Portugal Department of Clinical Sciences, Faculty of Medicine, Lund University, Sweden Institute of Biomedical Ethics and History of Medicine, University of Zurich, Switzerland Law Department, Pompeu Fabra University, Spain Faculty of Philosophy, University of Warsaw, Poland Centre for Biomedical Ethics and Law, KU Leuven, Belgium Delft University of Technology, The Netherlands Bioethics Research Platform, Faculty of Medicine & Surgery, University of Malta, Malta Faculty of Law, Heidelberg University, Germany School of Law, Queen’s University Belfast, UK Raoul Wallenberg Visiting Chair, Lund University, Sweden Department of Law, Economics, Politics and Modern Languages, Lumsa University, Rome, Italy Department of Ethics and Political Philosophy, Interdisciplinary Hub for Digitalization and Society, Radboud University, The Netherlands Departments of Medical Ethics and Experimental Biology, Masaryk University, Czech Republic Hellenic National Commission for Bioethics and Technoethics, Greece