Hasil untuk "Medical philosophy. Medical ethics"

Woohyeon Park, Jaeik Kim, Sunghwan Steve Cho et al.

Lesion detection, symptom tracking, and visual explainability are central to real-world medical image analysis, yet current medical Vision-Language Models (VLMs) still lack mechanisms that translate their broad knowledge into clinically actionable outputs. To bridge this gap, we present MEDIC-AD, a clinically oriented VLM that strengthens these three capabilities through a stage-wise framework. First, learnable anomaly-aware tokens (<Ano>) encourage the model to focus on abnormal regions and build more discriminative lesion centered representations. Second, inter image difference tokens (<Diff>) explicitly encode temporal changes between studies, allowing the model to distinguish worsening, improvement, and stability in disease burden. Finally, a dedicated explainability stage trains the model to generate heatmaps that highlight lesion-related regions, offering clear visual evidence that is consistent with the model's reasoning. Through our staged design, MEDIC-AD steadily boosts performance across anomaly detection, symptom tracking, and anomaly segmentation, achieving state-of-the-art results compared with both closed source and medical-specialized baselines. Evaluations on real longitudinal clinical data collected from real hospital workflows further show that MEDIC-AD delivers stable predictions and clinically faithful explanations in practical patient-monitoring and decision-support workflows

Yuhang GUO

In end-of-life care, quality of life (QoL) assessment serves as a critical basis for nursing and medical decision-making. However, the concept faces challenges such as high subjectivity, difficulties in quantification, and irreducible individual differences. By integrating patients' self-assessments with intersubjective rational standards, QoL evaluation can better reflect individual realities and support more personalized judgments. Practically, QoL assessments primarily influence three types of decisions. First, the use of continuous sedation, which requires balancing pain relief against potential side effects. Second, when patients refuse treatment, their autonomy must be respected while evaluating the practical consequences. Third, when treatment is deemed futile, ethical justification for intervention should be ensured through a comprehensive consideration of patient interests, risks, allocation of medical resources, and the needs of family members.

Israel González Marino, Monserrat Argandoña Barraza, Carlo Bascuñán Bravo et al.

Este artículo presenta los resultados de un estudio exploratorio-descriptivo que indaga en las percepciones del cuerpo docente de la carrera de Derecho de una universidad privada chilena respecto de la incorporación del Derecho Animal en la formación jurídica. A través de un cuestionario aplicado a 45 docentes, se indagó en seis dimensiones: conocimiento, experiencia profesional, valoración, inclusión curricular, apertura institucional y disposición personal. Los hallazgos muestran una valoración positiva general del Derecho Animal y una alta disposición a integrarlo en la docencia, aunque persisten brechas significativas en formación específica, reconocimiento institucional y difusión del curso existente. Además, se identificaron diferencias en las respuestas según variables como género, sede, disciplina académica y estilo de alimentación. Estos resultados permiten visibilizar tanto las oportunidades como los desafíos para consolidar esta área como parte del currículo jurídico, y abren nuevas preguntas para futuras investigaciones. Considerando el potencial formativo identificado, y desde una perspectiva largoplacista, estos hallazgos respaldan la inclusión sostenida del Derecho Animal en la educación jurídica y su contribución para sentar las bases éticas e institucionales de una justicia interespecie.

Michael Klenk

Generative AI enables automated, effective manipulation at scale. Despite the growing general ethical discussion around generative AI, the specific manipulation risks remain inadequately investigated. This article outlines essential inquiries encompassing conceptual, empirical, and design dimensions of manipulation, pivotal for comprehending and curbing manipulation risks. By highlighting these questions, the article underscores the necessity of an appropriate conceptualisation of manipulation to ensure the responsible development of Generative AI technologies.

Tianwei Lin, Wenqiao Zhang, Sijing Li et al.

We present HealthGPT, a powerful Medical Large Vision-Language Model (Med-LVLM) that integrates medical visual comprehension and generation capabilities within a unified autoregressive paradigm. Our bootstrapping philosophy is to progressively adapt heterogeneous comprehension and generation knowledge to pre-trained large language models (LLMs). This is achieved through a novel heterogeneous low-rank adaptation (H-LoRA) technique, which is complemented by a tailored hierarchical visual perception approach and a three-stage learning strategy. To effectively learn the HealthGPT, we devise a comprehensive medical domain-specific comprehension and generation dataset called VL-Health. Experimental results demonstrate exceptional performance and scalability of HealthGPT in medical visual unified tasks. Our project can be accessed at https://github.com/DCDmllm/HealthGPT.

Zenan Huang, Mingwei Li, Zheng Zhou et al.

Large Language Models (LLMs) have been widely adopted across various domains, yet their application in the medical field poses unique challenges, particularly concerning the generation of hallucinations. Hallucinations in open-ended long medical text manifest as misleading critical claims, which are difficult to verify due to two reasons. First, critical claims are often deeply entangled within the text and cannot be extracted based solely on surface-level presentation. Second, verifying these claims is challenging because surface-level token-based retrieval often lacks precise or specific evidence, leaving the claims unverifiable without deeper mechanism-based analysis. In this paper, we introduce a novel method termed Iterative Tree Analysis (ITA) for medical critics. ITA is designed to extract implicit claims from long medical texts and verify each claim through an iterative and adaptive tree-like reasoning process. This process involves a combination of top-down task decomposition and bottom-up evidence consolidation, enabling precise verification of complex medical claims through detailed mechanism-level reasoning. Our extensive experiments demonstrate that ITA significantly outperforms previous methods in detecting factual inaccuracies in complex medical text verification tasks by 10%. Additionally, we will release a comprehensive test set to the public, aiming to foster further advancements in research within this domain.

Hazem Zohny

This paper introduces ADEPT, a system using Large Language Model (LLM) personas to simulate multi-perspective ethical debates. ADEPT assembles panels of 'AI personas', each embodying a distinct ethical framework or stakeholder perspective (like a deontologist, consequentialist, or disability rights advocate), to deliberate on complex moral issues. Its application is demonstrated through a scenario about prioritizing patients for a limited number of ventilators inspired by real-world challenges in allocating scarce medical resources. Two debates, each with six LLM personas, were conducted; they only differed in the moral viewpoints represented: one included a Catholic bioethicist and a care theorist, the other substituted a rule-based Kantian philosopher and a legal adviser. Both panels ultimately favoured the same policy -- a lottery system weighted for clinical need and fairness, crucially avoiding the withdrawal of ventilators for reallocation. However, each panel reached that conclusion through different lines of argument, and their voting coalitions shifted once duty- and rights-based voices were present. Examination of the debate transcripts shows that the altered membership redirected attention toward moral injury, legal risk and public trust, which in turn changed four continuing personas' final positions. The work offers three contributions: (i) a transparent, replicable workflow for running and analysing multi-agent AI debates in bioethics; (ii) evidence that the moral perspectives included in such panels can materially change the outcome even when the factual inputs remain constant; and (iii) an analysis of the implications and future directions for such AI-mediated approaches to ethical deliberation and policy.

Artem Vazhentsev, Ivan Sviridov, Alvard Barseghyan et al.

This study addresses the critical issue of reliability for AI-assisted medical diagnosis. We focus on the selection prediction approach that allows the diagnosis system to abstain from providing the decision if it is not confident in the diagnosis. Such selective prediction (or abstention) approaches are usually based on the modeling predictive uncertainty of machine learning models involved. This study explores uncertainty quantification in machine learning models for medical text analysis, addressing diverse tasks across multiple datasets. We focus on binary mortality prediction from textual data in MIMIC-III, multi-label medical code prediction using ICD-10 codes from MIMIC-IV, and multi-class classification with a private outpatient visits dataset. Additionally, we analyze mental health datasets targeting depression and anxiety detection, utilizing various text-based sources, such as essays, social media posts, and clinical descriptions. In addition to comparing uncertainty methods, we introduce HUQ-2, a new state-of-the-art method for enhancing reliability in selective prediction tasks. Our results provide a detailed comparison of uncertainty quantification methods. They demonstrate the effectiveness of HUQ-2 in capturing and evaluating uncertainty, paving the way for more reliable and interpretable applications in medical text analysis.

He Huang

Through the use of a survey and statistical methods, this study explores the effects and interventions of handheld Tai Chi water resistance fitness balls on the elderly with Parkinson's disease. Firstly, a questionnaire on exercise compliance for patients with Parkinson's disease was developed, and its reliability and validity were tested. Then, a survey was conducted to investigate the current status of exercise compliance among Parkinson's disease patients, including general information, scoring status, and single and multiple factor analyses of influencing factors. The results of the study show that through qualitative research, the dimensions and item pools of the questionnaire were initially constructed, and the reliability analysis of the questionnaire was conducted through Delphi expert consultation, with favorable results in terms of its reliability and validity. Regarding the current status of exercise compliance among Parkinson's disease patients, the study found that the level of exercise compliance needs improvement, and there are significant differences in exercise compliance levels among patients under different circumstances. Finally, the research results were discussed and conclusions were drawn. The innovation of this study lies in the development of a questionnaire on exercise compliance for patients with Parkinson's disease and the preliminary qualitative research and Delphi expert consultation conducted on it, providing new ideas and methods for the study of exercise compliance. However, the study also has limitations as it did not examine the effects of other interventions on Parkinson's disease, therefore further research should be conducted.

Rasoul Ramazani, Samira Beiranvand, Sogand Daei et al.

Abstract Background The concept of medical futility has exposed the medical staff to many complicated conflicts. Through identifying some of these conflicts, it will be possible to have control over such situations and make plans for managing them better. The present study was conducted to determine the perception of futile care and the reasons behind it among the patients at end-of-life stages from care providers’ perspective. Methods This research is an analytical descriptive study which was conducted in Dezful in Iran on 308 care providers including physicians, nurses, and medical and nursing interns, in 2022. The data collection tools included 3 areas: demographic variables, investigating the perception of futile care, and investigating the reasons behind futile care. Results The mean score of perception of futile care was 103.20 ± 32.89 and the mean scores of the reasons behind providing futile care, 118.03 ± 26.09. A significant correlation was observed between the mean scores of the questionnaire for perception of futile care and the reasons behind providing futile care among end-of-life patients (P-value = 0.000, r = 0.465). Conclusions Based on the findings, almost half of the care providers had a moderate perception of futile care and the reasons behind providing it. The reasons behind providing futile care mentioned by the participants, as well as the positive relationship between the level of perception and the level of education, point out the need for training courses to become more familiar with the concept of futile care and change care providers’ perspectives and attitudes towards end-of-life care.

Chenlu Zhan, Yu Lin, Gaoang Wang et al.

Medical generative models, acknowledged for their high-quality sample generation ability, have accelerated the fast growth of medical applications. However, recent works concentrate on separate medical generation models for distinct medical tasks and are restricted to inadequate medical multi-modal knowledge, constraining medical comprehensive diagnosis. In this paper, we propose MedM2G, a Medical Multi-Modal Generative framework, with the key innovation to align, extract, and generate medical multi-modal within a unified model. Extending beyond single or two medical modalities, we efficiently align medical multi-modal through the central alignment approach in the unified space. Significantly, our framework extracts valuable clinical knowledge by preserving the medical visual invariant of each imaging modal, thereby enhancing specific medical information for multi-modal generation. By conditioning the adaptive cross-guided parameters into the multi-flow diffusion framework, our model promotes flexible interactions among medical multi-modal for generation. MedM2G is the first medical generative model that unifies medical generation tasks of text-to-image, image-to-text, and unified generation of medical modalities (CT, MRI, X-ray). It performs 5 medical generation tasks across 10 datasets, consistently outperforming various state-of-the-art works.

Laura Schütz, Sasan Matinfar, Gideon Schafroth et al.

Medical doctors rely on images of the human anatomy, such as magnetic resonance imaging (MRI), to localize regions of interest in the patient during diagnosis and treatment. Despite advances in medical imaging technology, the information conveyance remains unimodal. This visual representation fails to capture the complexity of the real, multisensory interaction with human tissue. However, perceiving multimodal information about the patient's anatomy and disease in real-time is critical for the success of medical procedures and patient outcome. We introduce a Multimodal Medical Image Interaction (MMII) framework to allow medical experts a dynamic, audiovisual interaction with human tissue in three-dimensional space. In a virtual reality environment, the user receives physically informed audiovisual feedback to improve the spatial perception of anatomical structures. MMII uses a model-based sonification approach to generate sounds derived from the geometry and physical properties of tissue, thereby eliminating the need for hand-crafted sound design. Two user studies involving 34 general and nine clinical experts were conducted to evaluate the proposed interaction framework's learnability, usability, and accuracy. Our results showed excellent learnability of audiovisual correspondence as the rate of correct associations significantly improved (p < 0.001) over the course of the study. MMII resulted in superior brain tumor localization accuracy (p < 0.05) compared to conventional medical image interaction. Our findings substantiate the potential of this novel framework to enhance interaction with medical images, for example, during surgical procedures where immediate and precise feedback is needed.

Ximiao Zhang, Min Xu, Dehui Qiu et al.

In the field of medical decision-making, precise anomaly detection in medical imaging plays a pivotal role in aiding clinicians. However, previous work is reliant on large-scale datasets for training anomaly detection models, which increases the development cost. This paper first focuses on the task of medical image anomaly detection in the few-shot setting, which is critically significant for the medical field where data collection and annotation are both very expensive. We propose an innovative approach, MediCLIP, which adapts the CLIP model to few-shot medical image anomaly detection through self-supervised fine-tuning. Although CLIP, as a vision-language model, demonstrates outstanding zero-/fewshot performance on various downstream tasks, it still falls short in the anomaly detection of medical images. To address this, we design a series of medical image anomaly synthesis tasks to simulate common disease patterns in medical imaging, transferring the powerful generalization capabilities of CLIP to the task of medical image anomaly detection. When only few-shot normal medical images are provided, MediCLIP achieves state-of-the-art performance in anomaly detection and location compared to other methods. Extensive experiments on three distinct medical anomaly detection tasks have demonstrated the superiority of our approach. The code is available at https://github.com/cnulab/MediCLIP.

Ben Fei, Yixuan Li, Weidong Yang et al.

The development of medical imaging techniques has made a significant contribution to clinical decision-making. However, the existence of suboptimal imaging quality, as indicated by irregular illumination or imbalanced intensity, presents significant obstacles in automating disease screening, analysis, and diagnosis. Existing approaches for natural image enhancement are mostly trained with numerous paired images, presenting challenges in data collection and training costs, all while lacking the ability to generalize effectively. Here, we introduce a pioneering training-free Diffusion Model for Universal Medical Image Enhancement, named UniMIE. UniMIE demonstrates its unsupervised enhancement capabilities across various medical image modalities without the need for any fine-tuning. It accomplishes this by relying solely on a single pre-trained model from ImageNet. We conduct a comprehensive evaluation on 13 imaging modalities and over 15 medical types, demonstrating better qualities, robustness, and accuracy than other modality-specific and data-inefficient models. By delivering high-quality enhancement and corresponding accuracy downstream tasks across a wide range of tasks, UniMIE exhibits considerable potential to accelerate the advancement of diagnostic tools and customized treatment plans.

Pol Cuadros Aguilera

En el presente trabajo nos ocuparemos de examinar cuáles son los límites que el Derecho impone a la disposición de células reproductoras. Con esto pretendemos clarificar, por ejemplo, respecto a los óvulos, qué limites fija el Derecho a su movilidad; qué tipos de límites son esos; y qué razones justifican su imposición. El resultado de este examen permitirá presentar a las células reproductoras como un objeto que recibe un trato muy especial por parte del Derecho, cuya disposición, en consecuencia, está fuertemente intervenida y limitada.

A. Kuttybaev, A. Kumar, A. Abikulova et al.

Introduction. Healthcare workers (HCWs) should theoretically have the necessary education and environment to adopt a healthy lifestyle, and they supposedly also should have a higher participation rate in WHP programmes. HCWs are, for several reasons, considered to be a key group in health promotion, especially due to the fact that the healthcare system reaches a substantial number of people in need of lifestyle changes such as increased physical activity (PA) [5]. Furthermore, healthcare professionals are considered to be the most credible source of health information [6]. HCWs' lifestyles can play an important role in increasing awareness among patients regarding lifestyle changes, because HCWs' own lifestyle habits and interests in lifestyle behaviour have been shown to positively influence their counselling practices and attitudes [6–7]. The international movement 'Health Promoting Hospitals and Health Services', which was initiated by the World Health Organization (WHO), highlights the importance of also focusing on the health and lifestyle of the employees. Methods. We conducted survey based on a standardized and adapted questionnaire that included socio-demographic data and points related to healthy lifestyle. We adhered to the latest recommendations on designing and reporting survey studies.  Before conducting the study, the questionnaire was pretested among 5 experts and revised twice. The survey was conducted in Kazakh / Russian for respondents who speak two languages fluently at the choice of participants. The filling of the questionnaire took on average 45 minutes. We reported absolute numbers and percentages. Chi-square tests were used to compare responses between groups. Results were considered significant at a P value of < 0.05. Statistical analyses were performed using the application SAS OnDemand for academia (version 3.81, Carry, North Carolina, USA). Results. Our data revealed that nurses were more likely to adhere to healthy eating principles and to have a regular diet at home. GPs were more likely to consume fast food and add salt when food is not salted enough. Nurses were more likely to eat greens regularly. GPs had a stronger belief in the impact of diet on health. More nurses rated their diet as healthy. Media promotion significantly influenced GPs for changing diet towards healthier options. Eating patterns vary according to the risk of stress. Research shows that chronic stress influences the amount and types of consumed food, contributing to both overeating and malnutrition, and that stress hormones can lead to the development of obesity. GPs are more likely to have used tobacco products compared to nurses. GPs are more likely to smoke more than 10 cigarettes per day. GPs have a slightly higher belief in the necessity of a healthy lifestyle compared to nurses. GPs and nurses have different testing frequencies for Hepatitis B and C, with nurses testing more frequently. GPs are more likely to have sexual activity after drinking alcohol. Conclusion. It is known that a healthy lifestyle of doctors affects the attitude of patients and their motivation to change their lifestyle. Thus, the lifestyle patterns of health workers, as well as the understanding of the motivation of these patterns, are more likely to affect public health.

Elise Li Zheng, Sandra Soo-Jin Lee

Anderson, E. 2012. Epistemic justice as a virtue of social institutions. Social Epistemology 26 (2):163–73. doi:10. 1080/02691728.2011.652211. Benatar, D. 2006. Bioethics and health and human rights: A critical view. Journal of Medical Ethics 32 (1):17–20. doi: 10.1136/jme.2005.011775. Broussard, M. 2018. Artificial unintelligence: How computers misunderstood the world. Cambridge, London: MIT Press. Cohen, I. G. 2023. What should ChatGPT mean for bioethics? American Journal of Bioethics 23 (10):8–16. doi: 10.1080/15265161.2023.2233357. Croce, Y. D. 2023. Epistemic injustice and nonmaleficence. Journal of Bioethical Inquiry 1–10. doi:10.1007/s11673023-10273-4. The Lancet Regional Health-Europe. 2023. Embracing generative AI in health care. Lancet Regional Health – Europe 30:100677. doi:10.1016/j.lanepe.2023.100677. Fricker, M. 2007. Epistemic injustice: Power & the ethics of knowing. Oxford, New York: Oxford University Press. Gent, E. 2023. How does ChatGPT work, and does it really “think” like us? New Scientist 3449 (July):33–4. https:// www.newscientist.com/article/2384030-how-does-chatgptwork-and-do-ai-powered-chatbots-think-like-us/ Glicksman, S., C. Goldberg, C. Hamel, R. Shore, A. Wein, D. Wood, and J. Zummo. 2017. Rights-based and personcentered approaches to supporting people with intellectual disability: A dialectical model. Intellectual and Developmental Disabilities 55 (3):181–91. doi:10.1352/ 1934-9556-55.3.181. Global Alliance for Genomics & Health. 2023. Diversity in Datasets. https://www.ga4gh.org/product/diversity-indatasets-best-practices/ Stewart, H., E. Cichicki, and C. McLeod. 2022. A perfect storm for epistemic injustice: Algorithmic targeting and sorting on social media. Feminist Philosophy Quarterly 8 (3/4):1–28.

Sujith K Mandala

As machine learning models become increasingly prevalent in medical diagnostics, the need for interpretability and transparency becomes paramount. The XAI Renaissance signifies a significant shift in the field, aiming to redefine the interpretability of medical diagnostic models. This paper explores the innovative approaches and methodologies within the realm of Explainable AI (XAI) that are revolutionizing the interpretability of medical diagnostic models. By shedding light on the underlying decision-making process, XAI techniques empower healthcare professionals to understand, trust, and effectively utilize these models for accurate and reliable medical diagnoses. This review highlights the key advancements in XAI for medical diagnostics and their potential to transform the healthcare landscape, ultimately improving patient outcomes and fostering trust in AI-driven diagnostic systems.

Yixuan Weng, Bin Li, Fei Xia et al.

The medical conversational question answering (CQA) system aims at providing a series of professional medical services to improve the efficiency of medical care. Despite the success of large language models (LLMs) in complex reasoning tasks in various fields, such as mathematics, logic, and commonsense QA, they still need to improve with the increased complexity and specialization of the medical field. This is because medical CQA tasks require not only strong medical reasoning, but also the ability to think broadly and deeply. In this paper, to address these challenges in medical CQA tasks that need to be considered and understood in many aspects, we propose the Holistically Thought (HoT) method, which is designed to guide the LLMs to perform the diffused and focused thinking for generating high-quality medical responses. The proposed HoT method has been evaluated through automated and manual assessments in three different medical CQA datasets containing the English and Chinese languages. The extensive experimental results show that our method can produce more correctness, professional, and considerate answers than several state-of-the-art (SOTA) methods, manifesting its effectiveness. Our code in https://github.com/WENGSYX/HoT.

Zhihao Chen, Yang Zhou, Anh Tran et al.

Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifying objects in natural images and are not capable of capturing the subtle and specialized features of medical findings, leading to sub-optimal performance in MPG. In this paper, we propose MedRPG, an end-to-end approach for MPG. MedRPG is built on a lightweight vision-language transformer encoder and directly predicts the box coordinates of mentioned medical findings, which can be trained with limited medical data, making it a valuable tool in medical image analysis. To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo). TaCo seeks context alignment to pull both the features and attention outputs of relevant region-phrase pairs close together while pushing those of irrelevant regions far away. This ensures that the final box prediction depends more on its finding-specific regions and phrases. Experimental results on three MPG datasets demonstrate that our MedRPG outperforms state-of-the-art visual grounding approaches by a large margin. Additionally, the proposed TaCo strategy is effective in enhancing finding localization ability and reducing spurious region-phrase correlations.