Hasil untuk "Medical philosophy. Medical ethics"

Karine Dubé, John Kanazawa, Jeff Taylor et al.

Shima Shirozhan

Abstract Background Rehabilitation research involves unique ethical complexities due to the heterogeneous nature of disability and participant vulnerability. Despite established international ethical principles, there appears to be a significant gap in context-sensitive ethical frameworks specifically tailored for rehabilitation settings. This integrative review aims to analyze the ethical challenges and principles reported in rehabilitation research. Methods Following the integrative review methodology by Whittemore and Knafl (2005), a comprehensive literature search was conducted across PubMed/MEDLINE, Embase, Cochrane, and Web of Science from their inception to December 2024. The search strategy used controlled vocabulary and keywords related to ethics, rehabilitation, and disability. Additional hand searching of reference lists and key journals was conducted to ensure comprehensive coverage. The review included all studies published in English, focusing on ethical issues in rehabilitation research. Data were analyzed using the constant comparative method. Results From 150 initially identified records, nine studies met full inclusion criteria. The analysis and subsequent interpretation of the evidence identified seven interconnected ethical domains: (1) systemic neglect of specialized ethical guidelines; (2) methodological challenges jeopardizing ethical rigor; (2) complexities in decision-making capacity and informed consent; (4) therapeutic misconception and inflated hope for a cure; (5) communication barriers; (6) ethical ambiguities in participant incentives; and (7) institutional and systemic barriers within ethics review boards. Conclusion The synthesized evidence suggests that ethical challenges in rehabilitation research are multifaceted and deeply interconnected. The findings highlight the need to shift from reactive compliance toward a more proactive, integrated system of ethical governance. Future efforts can focus on developing context-sensitive ethical guidance, strengthening stakeholder engagement, and promoting adaptive research methodologies to ensure rehabilitation research remains ethically grounded and socially relevant.

Zainab Ghafoor, Md Shafiqul Islam, Koushik Howlader et al.

Large Language Models (LLMs) are increasingly applied in healthcare, yet ensuring their ethical integrity and safety compliance remains a major barrier to clinical deployment. This work introduces a multi-agent refinement framework designed to enhance the safety and reliability of medical LLMs through structured, iterative alignment. Our system combines two generative models - DeepSeek R1 and Med-PaLM - with two evaluation agents, LLaMA 3.1 and Phi-4, which assess responses using the American Medical Association's (AMA) Principles of Medical Ethics and a five-tier Safety Risk Assessment (SRA-5) protocol. We evaluate performance across 900 clinically diverse queries spanning nine ethical domains, measuring convergence efficiency, ethical violation reduction, and domain-specific risk behavior. Results demonstrate that DeepSeek R1 achieves faster convergence (mean 2.34 vs. 2.67 iterations), while Med-PaLM shows superior handling of privacy-sensitive scenarios. The iterative multi-agent loop achieved an 89% reduction in ethical violations and a 92% risk downgrade rate, underscoring the effectiveness of our approach. This study presents a scalable, regulator-aligned, and cost-efficient paradigm for governing medical AI safety.

B.V.E. Hyde

In this review of The Ethics of Animal Shelters, I explore the book’s practical and philosophical approach to ethical dilemmas in animal shelters, such as euthanasia and triage. I highlight the book’s relevance to bioethics, despite the limited explicit engagement with the field, and critique its justification of killing healthy animals under nonideal conditions. I call for broader bioethical involvement in shelter ethics and the creation of ethical review boards to support decision-making.

Junjie Yang

Abstract People who have experienced traumatic events often suffer from the burden of painful memories. Recent advances in neuropharmaceuticals and neurotechnologies have enabled the modification and even erasure of traumatic memories, raising both therapeutic hopes and ethical concerns. One view argues that individuals have a moral obligation to preserve traumatic memories; therefore, erasing such memories amounts to an evasion of moral obligations and is therefore unacceptable. However, neither deontological ethics nor rule consequentialism can justify the claim that patients have an obligation to preserve their traumatic memories. In fact, memory erasure, as a transformative experience, situates individuals within a context of decision-making under uncertainty, thereby highlighting their moral obligations to themselves. Trauma survivors may seek memory erasure technologies as a way of honoring their moral obligations to their past, present, and future selves. In this sense, such interventions may be regarded as morally permissible.

Zhiyi YUAN, Zhiyi PEI, Jiayi LIN et al.

This meta-synthesis systematically reviews qualitative and mixed-methods studies on medical students' experiences, perceptions, and attitudes toward the use of generative artificial intelligence (GAI) as a learning aid, aiming to provide a foundation for the future development of GAI in medical education. According to the literature screening and quality evaluation, a total of 12 articles were included, from which 69 research results were extracted and categorized into 12 categories, culminating in four overarching results: clarified intention, contextualized application scenarios, enhanced intelligent-teaching collaborative strategy, and risk management. The findings reveal a general consensus among medical students recognizing the potential value of GAI in medical education. Future research should focus on achieving a balance between technological advancement, ethical considerations, and educational strategies. It is essential to develop teaching approaches aligned with the practical needs of medical education, verify their effectiveness, and to explore new models of medical education empowered by generative artificial intelligence.

Mengjie Fan, Liang Zhou

We introduce a design study process model for medical visualization based on the analysis of existing medical visualization and visual analysis works, and our own interdisciplinary research experience. With a literature review of related works covering various data types and applications, we identify features of medical visualization and visual analysis research and formulate our model thereafter. Compared to previous design study process models, our new model emphasizes: distinguishing between different stakeholders and target users before initiating specific designs, distinguishing design stages according to analytic logic or cognitive habits, and classifying task types as inferential or descriptive, and further hypothesis-based or hypothesis-free based on whether they involve multiple subgroups. In addition, our model refines previous models according to the characteristics of medical problems and provides referable guidance for each step. These improvements make the visualization design targeted, generalizable, and operational, which can adapt to the complexity and diversity of medical problems. We apply this model to guide the design of a visual analysis method and reanalyze three medical visualization-related works. These examples suggest that the new process model can provide a systematic theoretical framework and practical guidance for interdisciplinary medical visualization research. We give recommendations that future researchers can refer to, report on reflections on the model, and delineate it from existing models.

Hao Li, Bowen Deng, Chang Xu et al.

A unified foundation model for medical time series -- pretrained on open access and ethics board-approved medical corpora -- offers the potential to reduce annotation burdens, minimize model customization, and enable robust transfer across clinical institutions, modalities, and tasks, particularly in data-scarce or privacy-constrained environments. However, existing generalist time series foundation models struggle to handle medical time series data due to their inherent challenges, including irregular intervals, heterogeneous sampling rates, and frequent missing values. To address these challenges, we introduce MIRA, a unified foundation model specifically designed for medical time series forecasting. MIRA incorporates a Continuous-Time Rotary Positional Encoding that enables fine-grained modeling of variable time intervals, a frequency-specific mixture-of-experts layer that routes computation across latent frequency regimes to further promote temporal specialization, and a Continuous Dynamics Extrapolation Block based on Neural ODE that models the continuous trajectory of latent states, enabling accurate forecasting at arbitrary target timestamps. Pretrained on a large-scale and diverse medical corpus comprising over 454 billion time points collect from publicly available datasets, MIRA achieves reductions in forecasting errors by an average of 10% and 7% in out-of-distribution and in-distribution scenarios, respectively, when compared to other zero-shot and fine-tuned baselines. We also introduce a comprehensive benchmark spanning multiple downstream clinical tasks, establishing a foundation for future research in medical time series modeling.

Ibtissam El Harch, Soumaya Benmaamar, Nabil Tachfouti et al.

Abstract Background In Morocco, medical research ethics training was integrated into the medical curriculum during the 2015 reform. In the same year, a law on medical research ethics was enacted to protect individuals participating in medical research. These improvements, whether in the reform or in the enactment of the law, could positively impact the knowledge of these researchers and, consequently, their attitudes and practices regarding medical research ethics. The main objective of this work is to assess Moroccan physicians’ knowledge, attitudes, and practices at the beginning of their careers (interns and residents) in medical research ethics. Patients and methods This is a multicenter cross-sectional study conducted in 2021 among Moroccan physicians. Three scores were created and validated to assess physicians’ level of knowledge, attitudes, and practices regarding research ethics. A descriptive analysis was carried out, followed by a univariate analysis and a multivariate analysis using multivariate binary logistic regression to study the factors associated with the different calculated scores. Results A total of 924 physicians were included in the study, with an average age of 27.8 ± 2.2 years. 40.7% had a high medical research ethics knowledge score, and 68.8% had good attitudes. These two scores were positively associated with age and were statistically higher in residents and in physicians who had received training in medical research ethics during their medical curriculum. Only 29,9% of physicians who had participated in research studies had adequate practices with medical research ethics. This score was statistically higher in residents and in physicians who had heard about research ethics. Conclusion A genuine introduction to ethics in the medical curriculum is essential to enhance researchers’ knowledge, attitudes, and practices. This, in turn, can lead to an increase in both the quantity and quality of research conducted in Morocco.

Sungjoo Byun, Jiseung Hong, Sumin Park et al.

Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP). Yet, there has not been an open-source medical NER dataset specifically for the Korean language. To address this, we utilized ChatGPT to assist in constructing the KBMC (Korean Bio-Medical Corpus), which we are now presenting to the public. With the KBMC dataset, we noticed an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets. This research underscores the significant benefits and importance of using specialized tools and datasets, like ChatGPT, to enhance language processing in specialized fields such as healthcare.

Jingyu Xu, Binbin Wu, Jiaxin Huang et al.

The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field. Artificial intelligence technologies represented by computer vision, natural language processing, and machine learning have been widely penetrated into diverse scenarios such as medical imaging, health management, medical information, and drug research and development, and have become an important driving force for improving the level and quality of medical services.The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data, enhance images, aid in anomaly detection, and facilitate image-to-image translation. Despite challenges like model complexity, the applications of generative models in healthcare, including Med-PaLM 2 technology, show promising results. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes. However, ethical considerations and collaboration among stakeholders are essential for responsible implementation. Through experiments leveraging GANs to augment brain tumor MRI datasets, the study demonstrates how generative AI can enhance image quality and diversity, ultimately advancing medical diagnostics and patient care.

Antoine Lizée, Pierre-Auguste Beaucoté, James Whitbeck et al.

The shortage of doctors is creating a critical squeeze in access to medical expertise. While conversational Artificial Intelligence (AI) holds promise in addressing this problem, its safe deployment in patient-facing roles remains largely unexplored in real-world medical settings. We present the first large-scale evaluation of a physician-supervised LLM-based conversational agent in a real-world medical setting. Our agent, Mo, was integrated into an existing medical advice chat service. Over a three-week period, we conducted a randomized controlled experiment with 926 cases to evaluate patient experience and satisfaction. Among these, Mo handled 298 complete patient interactions, for which we report physician-assessed measures of safety and medical accuracy. Patients reported higher clarity of information (3.73 vs 3.62 out of 4, p < 0.05) and overall satisfaction (4.58 vs 4.42 out of 5, p < 0.05) with AI-assisted conversations compared to standard care, while showing equivalent levels of trust and perceived empathy. The high opt-in rate (81% among respondents) exceeded previous benchmarks for AI acceptance in healthcare. Physician oversight ensured safety, with 95% of conversations rated as "good" or "excellent" by general practitioners experienced in operating a medical advice chat service. Our findings demonstrate that carefully implemented AI medical assistants can enhance patient experience while maintaining safety standards through physician supervision. This work provides empirical evidence for the feasibility of AI deployment in healthcare communication and insights into the requirements for successful integration into existing healthcare services.

Yining Huang, Keke Tang, Meilian Chen et al.

Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. These models have shown potential to transform the medical field, highlighting the necessity for specialized evaluation frameworks to ensure their effective and ethical deployment. This comprehensive survey delineates the extensive application and requisite evaluation of LLMs within healthcare, emphasizing the critical need for empirical validation to fully exploit their capabilities in enhancing healthcare outcomes. Our survey is structured to provide an in-depth analysis of LLM applications across clinical settings, medical text data processing, research, education, and public health awareness. We begin by exploring the roles of LLMs in various medical applications, detailing their evaluation based on performance in tasks such as clinical diagnosis, medical text data processing, information retrieval, data analysis, and educational content generation. The subsequent sections offer a comprehensive discussion on the evaluation methods and metrics employed, including models, evaluators, and comparative experiments. We further examine the benchmarks and datasets utilized in these evaluations, providing a categorized description of benchmarks for tasks like question answering, summarization, information extraction, bioinformatics, information retrieval and general comprehensive benchmarks. This structure ensures a thorough understanding of how LLMs are assessed for their effectiveness, accuracy, usability, and ethical alignment in the medical domain. ...

Pavel Blinov, Konstantin Egorov, Ivan Sviridov et al.

Building an intelligent and efficient medical assistant is still a challenging AI problem. The major limitation comes from the data modality scarceness, which reduces comprehensive patient perception. This demo paper presents the GigaPevt, the first multimodal medical assistant that combines the dialog capabilities of large language models with specialized medical models. Such an approach shows immediate advantages in dialog quality and metric performance, with a 1.18% accuracy improvement in the question-answering task.

Yuxuan Zhou, Xien Liu, Chen Ning et al.

Large language models (LLMs) have excelled across domains, also delivering notable performance on the medical evaluation benchmarks, such as MedQA. However, there still exists a significant gap between the reported performance and the practical effectiveness in real-world medical scenarios. In this paper, we aim to explore the causes of this gap by employing a multifaceted examination schema to systematically probe the actual mastery of medical knowledge by current LLMs. Specifically, we develop a novel evaluation framework MultifacetEval to examine the degree and coverage of LLMs in encoding and mastering medical knowledge at multiple facets (comparison, rectification, discrimination, and verification) concurrently. Based on the MultifacetEval framework, we construct two multifaceted evaluation datasets: MultiDiseK (by producing questions from a clinical disease knowledge base) and MultiMedQA (by rephrasing each question from a medical benchmark MedQA into multifaceted questions). The experimental results on these multifaceted datasets demonstrate that the extent of current LLMs in mastering medical knowledge is far below their performance on existing medical benchmarks, suggesting that they lack depth, precision, and comprehensiveness in mastering medical knowledge. Consequently, current LLMs are not yet ready for application in real-world medical tasks. The codes and datasets are available at https://github.com/THUMLP/MultifacetEval.

Wei Zhu, Xiaoling Wang, Huanran Zheng et al.

Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends. However, most current benchmarks: (a) are limited to English which makes it challenging to replicate many of the successes in English for other languages, or (b) focus on knowledge probing of LLMs and neglect to evaluate how LLMs apply these knowledge to perform on a wide range of bio-medical tasks, or (c) have become a publicly available corpus and are leaked to LLMs during pre-training. To facilitate the research in medical LLMs, we re-build the Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark into a large scale prompt-tuning benchmark, PromptCBLUE. Our benchmark is a suitable test-bed and an online platform for evaluating Chinese LLMs' multi-task capabilities on a wide range bio-medical tasks including medical entity recognition, medical text classification, medical natural language inference, medical dialogue understanding and medical content/dialogue generation. To establish evaluation on these tasks, we have experimented and report the results with the current 9 Chinese LLMs fine-tuned with differtent fine-tuning techniques.

Yang Liu, Shi Gu

The registration of pathological images plays an important role in medical applications. Despite its significance, most researchers in this field primarily focus on the registration of normal tissue into normal tissue. The negative impact of focal tissue, such as the loss of spatial correspondence information and the abnormal distortion of tissue, are rarely considered. In this paper, we propose GIRNet, a novel unsupervised approach for pathological image registration by incorporating segmentation and inpainting through the principles of Generation, Inpainting, and Registration (GIR). The registration, segmentation, and inpainting modules are trained simultaneously in a co-learning manner so that the segmentation of the focal area and the registration of inpainted pairs can improve collaboratively. Overall, the registration of pathological images is achieved in a completely unsupervised learning framework. Experimental results on multiple datasets, including Magnetic Resonance Imaging (MRI) of T1 sequences, demonstrate the efficacy of our proposed method. Our results show that our method can accurately achieve the registration of pathological images and identify lesions even in challenging imaging modalities. Our unsupervised approach offers a promising solution for the efficient and cost-effective registration of pathological images. Our code is available at https://github.com/brain-intelligence-lab/GIRNet.

Sebastian Joseph, Kathryn Kazanas, Keziah Reina et al.

Automated text simplification aims to produce simple versions of complex texts. This task is especially useful in the medical domain, where the latest medical findings are typically communicated via complex and technical articles. This creates barriers for laypeople seeking access to up-to-date medical findings, consequently impeding progress on health literacy. Most existing work on medical text simplification has focused on monolingual settings, with the result that such evidence would be available only in just one language (most often, English). This work addresses this limitation via multilingual simplification, i.e., directly simplifying complex texts into simplified texts in multiple languages. We introduce MultiCochrane, the first sentence-aligned multilingual text simplification dataset for the medical domain in four languages: English, Spanish, French, and Farsi. We evaluate fine-tuned and zero-shot models across these languages, with extensive human assessments and analyses. Although models can now generate viable simplified texts, we identify outstanding challenges that this dataset might be used to address.

Raziyeh Beykmirza, Lida Nikfarid, Reza Negarandeh et al.

Abstract Background and aim The nature of pediatric settings may encounter nurses with more complicated ethical issues. A code of ethics guides nurses to act and decide ethically as a profession. Also, there is always a need to evaluate amount nurses adhere to this code of ethics, using valid and reliable instruments. This study aimed to develop a questionnaire and assess its psychometric properties to measure pediatric nurses' adherence to the code of ethics. Methods In this methodological research study, firstly, the questionnaire was developed based on an extensive review of the related literature and the theoretical framework of nursing ethics. A panel of experts (n = 12) reviewed the preliminary questionnaire qualitatively and quantitatively (using CVI and CVR). A conveniently selected sample of 156 nurses working in pediatric wards in three hospitals filled out the questionnaire. The psychometric process included determining sample size and data adequacy using KMO and Bartlette's test of sphericity; exploratory factor analysis (principal component method with Promax rotation); item analysis; and Cronbach's alpha coefficient. Also, the Interclass Correlation Index (ICC) value was determined using a two-week interval test–retest method on 30 eligible nurses. Results The CVI and CVR for the entire questionnaire were 0.85 and 0.78, respectively. The CVI and CVR of all items were reported higher than 0.59 and 0.8, respectively. Cronbach's alpha of the 28-items instrument was 0.92. Extracted six factors explained 65.31% of the total variance, and the values of the item correlations with the total questionnaire showed good internal consistency (0.52 to 0.90). The items of each factor were evaluated to determine the values they represent. Accordingly, the factors were named beneficence, nonmaleficence, human dignity, autonomy, informed consent, and honesty. The ICC value was 0.99. Conclusions The developed instrument is acceptable and has good reliability and validity. It can be used to assess the amount of pediatric nurses' adherence to the code of ethics by managers, teachers, and researchers.