Hasil untuk "Medical philosophy. Medical ethics"

Nazanin Fard Moghadam, Azin Hassani, Loghman Khaninezhad

Abstract Background Euthanasia is a polarizing topic in healthcare, particularly in Iran, where Islamic principles emphasizing the sanctity of life shape ethical perspectives. Understanding the attitudes of Iranian healthcare providers toward euthanasia and the factors influencing these views is critical, given the cultural and religious context. The primary objective of this study was to systematically identify and synthesize the key factors influencing healthcare providers’ attitudes toward euthanasia in Iran. Methods Following PRISMA guidelines, a systematic search was conducted across PubMed, Scopus, Web of Science, Magiran, and SID databases up to March 10, 2025. Inclusion criteria encompassed observational studies reporting quantitative data on euthanasia attitudes among Iranian healthcare providers. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Joanna Briggs Institute tools. Due to heterogeneity in study designs and measurement tools, a narrative synthesis was performed. Results Of 595 identified records, 36 studies involving 7,790 participants met inclusion criteria. Attitudes toward euthanasia were predominantly cautious or negative, with stronger opposition among older providers, females, and those with deep religious beliefs. Younger age, male gender, clinical experience, and exposure to terminal patients correlated with more positive attitudes. Religious and cultural factors, particularly Islamic teachings, were significant barriers to acceptance, while urban settings and higher education were linked to neutral or mixed views. Conclusion Iranian healthcare providers’ attitudes toward euthanasia reflect a complex interplay of religious, cultural, and professional influences. These findings underscore the need for enhanced palliative care and ethical training in Iran’s healthcare system to address end-of-life dilemmas while respecting cultural boundaries. Clinical trial number Not applicable.

Lakshmi K Josyula

Abstract This commentary is a reflection, through a health policy and systems lens, on the medically pluralistic workforce, particularly in low- and middle-income countries that have and utilise numerous systems of medicine, indigenous and adopted. It analyses and distinguishes integration and pluralism, and examines the interaction, and the more frequently observed lack thereof, among different systems of medicine. It highlights the implicit and express hierarchies among the different systems of medicine; the epistemic injustices, wrongs, and resultant harms to different systems of medicine, their practitioners, and the populations that could benefit from them; concomitant inequities in the administration and functioning of the workforce; and the gaps in coordination among diverse disciplines in health care. The commentary underscores the imperative for thoughtful and equitable administration of pluralistic health systems, including emic and etic enquiry, sensitisation, and participatory action, to accomplish the diverse goals of health systems, the health workforce, and the population.

Nicole M. Lee, Nolan Speicher, Rachel Adair et al.

Despite the popularity of direct-to-consumer (DTC) genetic tests, vocal critics have voiced concerns about test utility, data security, and consumers’ ability to interpret results. This study explores what issues consumers are concerned with and how prevalent these concerns are. Through an analysis of open-ended survey responses and publicly available social media comments, we examined the factors that prevent individuals from purchasing a DTC genetic test, and the broader social media discourse surrounding these issues. Findings highlight the differences between consumer decision-making and public discourse, with survey results emphasizing interest and cost as major factors and social media comments focusing on issues of privacy and institutional distrust.

Upreet Dhaliwal

Komal Maheshwari

Trigger warning: The poem, written in free verse, tries to bring into light the hidden struggles of persons with mental illness. It includes reference to self harm.

Qiang Sun, Zongcheng Ji, Yinlong Xiao et al.

Generating medical reports from chest X-ray images is a critical and time-consuming task for radiologists, especially in emergencies. To alleviate the stress on radiologists and reduce the risk of misdiagnosis, numerous research efforts have been dedicated to automatic medical report generation in recent years. Most recent studies have developed methods that represent images by utilizing various medical metadata, such as the clinical document history of the current patient and the medical graphs constructed from retrieved reports of other similar patients. However, all existing methods integrate additional metadata representations with visual representations through a simple "Add and LayerNorm" operation, which suffers from the information asymmetry problem due to the distinct distributions between them. In addition, chest X-ray images are usually represented using pre-trained models based on natural domain images, which exhibit an obvious domain gap between general and medical domain images. To this end, we propose a novel approach called Enhanced Image Representations (EIR) for generating accurate chest X-ray reports. We utilize cross-modal transformers to fuse metadata representations with image representations, thereby effectively addressing the information asymmetry problem between them, and we leverage medical domain pre-trained models to encode medical images, effectively bridging the domain gap for image representation. Experimental results on the widely used MIMIC and Open-I datasets demonstrate the effectiveness of our proposed method.

Rochana Prih Hastuti, Rian Adam Rajagede, Mansour Al Ghanim et al.

As large language models (LLMs) are adapted to sensitive domains such as medicine, their fluency raises safety risks, particularly regarding provenance and accountability. Watermarking embeds detectable patterns to mitigate these risks, yet its reliability in medical contexts remains untested. Existing benchmarks focus on detection-quality tradeoffs and overlook factual risks. In medical text, watermarking often reweights low-entropy tokens, which are highly predictable and often carry critical medical terminology. Shifting these tokens can cause inaccuracy and hallucinations, risks that prior general-domain benchmarks fail to capture. We propose a medical-focused evaluation workflow that jointly assesses factual accuracy and coherence. Using GPT-Judger and further human validation, we introduce the Factuality-Weighted Score (FWS), a composite metric prioritizing factual accuracy beyond coherence to guide watermarking deployment in medical domains. Our evaluation shows current watermarking methods substantially compromise medical factuality, with entropy shifts degrading medical entity representation. These findings underscore the need for domain-aware watermarking approaches that preserve the integrity of medical content.

Juncheng Wu, Wenlong Deng, Xingxuan Li et al.

Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasoning demands meticulous, verifiable thought processes to ensure reliability and accuracy. However, there is a notable lack of datasets that provide transparent, step-by-step reasoning to validate and enhance the medical reasoning ability of AI models. To bridge this gap, we introduce MedReason, a large-scale high-quality medical reasoning dataset designed to enable faithful and explainable medical problem-solving in large language models (LLMs). We utilize a structured medical knowledge graph (KG) to convert clinical QA pairs into logical chains of reasoning, or ``thinking paths'', which trace connections from question elements to answers via relevant KG entities. Each path is validated for consistency with clinical logic and evidence-based medicine. Our pipeline generates detailed reasoning for various medical questions from 7 medical datasets, resulting in a dataset of 32,682 question-answer pairs, each with detailed, step-by-step explanations. Experiments demonstrate that fine-tuning with our dataset consistently boosts medical problem-solving capabilities, achieving significant gains of up to 7.7% for DeepSeek-Ditill-8B. Our top-performing model, MedReason-8B, outperforms the Huatuo-o1-8B, a state-of-the-art medical reasoning model, by up to 4.2% on the clinical benchmark MedBullets. We also engage medical professionals from diverse specialties to assess our dataset's quality, ensuring MedReason offers accurate and coherent medical reasoning. Our data, models, and code is available at https://github.com/UCSC-VLAA/MedReason.

Jeremy Fischer, Mahdi Al-Husseini, Ram Krishnamoorthy et al.

Medical evacuation is one of the United States Army's most storied and critical mission sets, responsible for efficiently and expediently evacuating the battlefield ill and injured. Medical evacuation planning involves designing a robust network of medical platforms and facilities capable of moving and treating large numbers of casualties. Until now, there has not been a medium to simulate these networks in a classroom setting and evaluate both offline planning and online decision-making performance. This work describes the Medical Evacuation Wargaming Initiative (MEWI), a three-dimensional multiplayer simulation developed in Unity that replicates battlefield constraints and uncertainties. MEWI accurately models patient interactions at casualty collection points, ambulance exchange points, medical treatment facilities, and evacuation platforms. Two operational scenarios are introduced: an amphibious island assault in the Pacific and a Eurasian conflict across a sprawling road and river network. These scenarios pit students against the clock to save as many casualties as possible while adhering to doctrinal lessons learned during didactic training. We visualize performance data collected from two iterations of the MEWI Pacific scenario executed in the United States Army's Medical Evacuation Doctrine Course. We consider post-wargame Likert survey data from student participants and external observer notes to identify key planning decision points, document medical evacuation lessons learned, and quantify general utility. Results indicate that MEWI participation substantially improves uptake of medical evacuation lessons learned and co-operative decision-making. MEWI is a substantial step forward in the field of high-fidelity training tools for medical education, and our study findings offer critical insights into improving medical evacuation education and operations across the joint force.

Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth et al.

Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to generate a response-a process similar to that of a human expert-has led many to see potential in deploying LLMs for clinical use. However, medicine is a setting where accurate reasoning is paramount. Many researchers are questioning the effectiveness of multiple choice question answering (MCQA) benchmarks, frequently used to test LLMs. Researchers and clinicians alike must have complete confidence in LLMs' abilities for them to be deployed in a medical setting. To address this need for understanding, we introduce a knowledge graph (KG)-based method to evaluate the biomedical reasoning abilities of LLMs. Essentially, we map how LLMs link medical concepts in order to better understand how they reason. We test GPT-4, Llama3-70b, and PalmyraMed-70b, a specialized medical model. We enlist a panel of medical students to review a total of 60 LLM-generated graphs and compare these graphs to BIOS, a large biomedical KG. We observe GPT-4 to perform best in our human review but worst in our ground truth comparison; vice-versa with PalmyraMed, the medical model. Our work provides a means of visualizing the medical reasoning pathways of LLMs so they can be implemented in clinical settings safely and effectively.

Julien Khlaut, Corentin Dancette, Elodie Ferreres et al.

In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. Large language models, such as GPT-4, obtain reasonable scores on medical question answering tasks, but smaller models are far behind. In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model. Additionally, we introduce ECN-QA, a novel medical question answering dataset containing ``progressive questions'' composed of related sequential questions. We show the benefits of our training strategy on this dataset. The study's findings highlight the potential of small language models in the medical domain when appropriately fine-tuned. The code and weights are available at https://github.com/raidium-med/MQG.

Kalina P. Slavkova, Melanie Traughber, Oliver Chen et al.

Technological advances in artificial intelligence (AI) have enabled the development of large vision language models (LVLMs) that are trained on millions of paired image and text samples. Subsequent research efforts have demonstrated great potential of LVLMs to achieve high performance in medical imaging use cases (e.g., radiology report generation), but there remain barriers that hinder the ability to deploy these solutions broadly. These include the cost of extensive computational requirements for developing large scale models, expertise in the development of sophisticated AI models, and the difficulty in accessing substantially large, high-quality datasets that adequately represent the population in which the LVLM solution is to be deployed. The HOPPR Medical-Grade Platform addresses these barriers by providing powerful computational infrastructure, a suite of foundation models on top of which developers can fine-tune for their specific use cases, and a robust quality management system that sets a standard for evaluating fine-tuned models for deployment in clinical settings. The HOPPR Platform has access to millions of imaging studies and text reports sourced from hundreds of imaging centers from diverse populations to pretrain foundation models and enable use case-specific cohorts for fine-tuning. All data are deidentified and securely stored for HIPAA compliance. Additionally, developers can securely host models on the HOPPR platform and access them via an API to make inferences using these models within established clinical workflows. With the Medical-Grade Platform, HOPPR's mission is to expedite the deployment of LVLM solutions for medical imaging and ultimately optimize radiologist's workflows and meet the growing demands of the field.

Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan et al.

In this paper, we introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. Our model facilitates a wide range of medical interactions in English and Arabic, including multi-turn chats to inquire about additional details such as patient symptoms and medical history, multiple-choice question answering, and open-ended question answering. We propose a semi-automated English-to-Arabic translation pipeline with human refinement to ensure high-quality translations. We also introduce a comprehensive evaluation benchmark for Arabic medical LLMs. Furthermore, we introduce BiMed1.3M, an extensive Arabic-English bilingual instruction set covering 1.3 Million diverse medical interactions, resulting in over 632 million healthcare specialized tokens for instruction tuning. Our BiMed1.3M dataset includes 250k synthesized multi-turn doctor-patient chats and maintains a 1:2 Arabic-to-English ratio. Our model outperforms state-of-the-art Med42 and Meditron by average absolute gains of 2.5% and 4.1%, respectively, computed across multiple medical evaluation benchmarks in English, while operating at 8-times faster inference. Moreover, our BiMediX outperforms the generic Arabic-English bilingual LLM, Jais-30B, by average absolute gains of 10% on our Arabic medical benchmark and 15% on bilingual evaluations across multiple datasets. Our project page with source code and trained model is available at https://github.com/mbzuai-oryx/BiMediX .

Shuangping Huang, Hao Liang, Qingfeng Wang et al.

Recently, developing unified medical image segmentation models gains increasing attention, especially with the advent of the Segment Anything Model (SAM). SAM has shown promising binary segmentation performance in natural domains, however, transferring it to the medical domain remains challenging, as medical images often possess substantial inter-category overlaps. To address this, we propose the SEmantic-Guided SAM (SEG-SAM), a unified medical segmentation model that incorporates semantic medical knowledge to enhance medical segmentation performance. First, to avoid the potential conflict between binary and semantic predictions, we introduce a semantic-aware decoder independent of SAM's original decoder, specialized for both semantic segmentation on the prompted object and classification on unprompted objects in images. To further enhance the model's semantic understanding, we solicit key characteristics of medical categories from large language models and incorporate them into SEG-SAM through a text-to-vision semantic module, adaptively transferring the language information into the visual segmentation task. In the end, we introduce the cross-mask spatial alignment strategy to encourage greater overlap between the predicted masks from SEG-SAM's two decoders, thereby benefiting both predictions. Extensive experiments demonstrate that SEG-SAM outperforms state-of-the-art SAM-based methods in unified binary medical segmentation and task-specific methods in semantic medical segmentation, showcasing promising results and potential for broader medical applications.

Qiyang Sun, Alican Akman, Björn W. Schuller

The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.

Shahin Nakhaii, Qasem Ahi, Mohamad Hasan Qanifar et al.

Background and Objectives: Nurses often face many challenges in life which negatively affect their well-being. This study aims to investigate the mediating role of self-transcendence in the relationship between psychological vulnerability and work-related well-being of nurses in Birjand City, Iran. Methods: The present study was a descriptive correlation with the structural equation modeling (SEM) approach. The statistical population included 795 public hospital nurses in Birjand City, Iran in 2020. The research sample included 440 people selected using the convenience sampling method. Data collection tools included the psychological vulnerability scale, the subscale self-transcendence and character questionnaire, and the work-related well-being scale. The data were analyzed using the SEM approach, SPSS software, version 22 and LISREL software, version 8.8. Results: The results showed that the direct path of psychological vulnerability to spiritual acceptance, creative self-forgetfulness and transpersonal identity was negative and significant (P<0.01). The direct path of self-forgetfulness, transpersonal identity, and spiritual acceptance to work-related well-being was positive and significant (P<0.01). Also, the indirect route of psychological vulnerability to work-related well-being through spiritual acceptance, self-forgetfulness, and transpersonal identity was significant (P<0.01). Conclusion: The results of the study indicate that self-transcendence can play a significant indirect role in reducing the adverse effects of psychological vulnerability on nurses’ work-related well-being. Therefore, using the self-care training program based on the theory of self-transcendence, it is possible to improve the three dimensions of self-transcendence of nurses, increase their work well-being, and reduce their psychological vulnerability.

Qiang Li, Xiaoyan Yang, Haowen Wang et al.

Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However, these models face a significant challenge when it comes to sensitive applications, such as reasoning over medical knowledge and answering medical questions in a physician-like manner. Prior studies attempted to overcome this challenge by increasing the model size (>100B) to learn more general medical knowledge, while there is still room for improvement in LLMs with smaller-scale model sizes (<100B). In this work, we start from a pre-trained general LLM model (AntGLM-10B) and fine-tune it from a medical beginner towards a medical expert (called AntGLM-Med-10B), which leverages a 3-stage optimization procedure, i.e., general medical knowledge injection, medical domain instruction tuning, and specific medical task adaptation. Our contributions are threefold: (1) We specifically investigate how to adapt a pre-trained general LLM in medical domain, especially for a specific medical task. (2) We collect and construct large-scale medical datasets for each stage of the optimization process. These datasets encompass various data types and tasks, such as question-answering, medical reasoning, multi-choice questions, and medical conversations. (3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs. Remarkably, by combining the above approaches, our AntGLM-Med-10B model can outperform the most of LLMs on PubMedQA, including both general and medical LLMs, even when these LLMs have larger model size.

Wenjian Yao, Jiajun Bai, Wei Liao et al.

Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend. The widely adopted approach currently is U-Net and its variants. Additionally, with the remarkable success of pre-trained models in natural language processing tasks, transformer-based models like TransUNet have achieved desirable performance on multiple medical image segmentation datasets. In this paper, we conduct a survey of the most representative four medical image segmentation models in recent years. We theoretically analyze the characteristics of these models and quantitatively evaluate their performance on two benchmark datasets (i.e., Tuberculosis Chest X-rays and ovarian tumors). Finally, we discuss the main challenges and future trends in medical image segmentation. Our work can assist researchers in the related field to quickly establish medical segmentation models tailored to specific regions.

Kaishuai Xu, Wenjun Hou, Yi Cheng et al.

Medical dialogue systems (MDS) aim to provide patients with medical services, such as diagnosis and prescription. Since most patients cannot precisely describe their symptoms, dialogue understanding is challenging for MDS. Previous studies mainly addressed this by extracting the mentioned medical entities as critical dialogue history information. In this work, we argue that it is also essential to capture the transitions of the medical entities and the doctor's dialogue acts in each turn, as they help the understanding of how the dialogue flows and enhance the prediction of the entities and dialogue acts to be adopted in the following turn. Correspondingly, we propose a Dual Flow enhanced Medical (DFMed) dialogue generation framework. It extracts the medical entities and dialogue acts used in the dialogue history and models their transitions with an entity-centric graph flow and a sequential act flow, respectively. We employ two sequential models to encode them and devise an interweaving component to enhance their interactions. Experiments on two datasets demonstrate that our method exceeds baselines in both automatic and manual evaluations.

Ried Mackay

Photo by Andrew James on Unsplash INTRODUCTION Since its inception, bioethics has focused on Western conceptions of ethics and science. This has provided a strong foundation to build bioethics as a field and discipline. However, it has largely failed to consider non-Western systems of ethics and science like Indigenous American thought frameworks. This has important implications for the treatment of Indigenous Americans in healthcare settings, something that I have considered as a mixed Indigenous American researcher of sociology, global health and bioethics. While there has been recognition of other thought frameworks impacting bioethics, these have largely focused on differences among religions and their adherents. While religious frameworks are important, bioethics must also recognize, learn, and implement different cultural frameworks. Bioethics cannot simply recognize these non-Western frameworks; it must also legitimately incorporate them as frameworks. Non-Western conceptions have too often been used in a secondary status or as a historical outlier or curiosity which leads to failure in integrating non-Western frameworks to their full extent and benefit. BACKGROUND Indigenous American philosophy has strong foundations throughout North and South America[1] that can provide a base for developing Indigenous-focused bioethics. While Indigenous Americans cannot be considered a monolith, there are similarities across Indigenous cultures in their approach to reality—developing Indigenous bioethics will necessarily incorporate these shared philosophies.[2] These similarities are present in the US philosophical tradition of pragmatism. Pragmatism arises from the Indigenous pragmatism which values interacting with other beings and the environment, plurality of thought, connections to the community, and growth from the other three dimensions.[3] This shared pragmatism centers the actions of individuals as the defining feature of maintaining balance in all scenarios of socializing and work—achieving the “center good.” [4] Tlacoqualli in Monequi is a Mexica (Aztec) saying that translates to the “center good is required” and means “middle way of being” or consistently managing imbalances and obligations to achieve a long-term balance. For example, indulging in excesses requires the opposite to follow: making do with less for a period or balancing receiving with giving.[5] The actions of individuals must maintain the balance, which simultaneously encourages self-responsibility and responsibility to the community.[6] Additionally, the individual is not judged only by their actions but also by their omissions—failure to act either unintentionally or intentionally—and judged on the morality of their decision as if they had acted.[7] There have been calls for developing Indigenous bioethics,[8] including analyses of important distinctions between Western and Indigenous ethical approaches to health care such as how Indigenous patients understand core bioethical concepts like autonomy and non-maleficence.[9] These differences in thought impact all aspects of Indigenous health care and how providers approach fundamental tasks like revealing diagnoses or encouraging surgeries.[10] Indigenous ethics place great value on individual decisions made in the context of community input. Failing to appreciate this cultural difference can prevent an Indigenous patient from maintaining the center good and therefore violate a fundamental aspect of being.[11] Providers must also understand the nuances of religion and Indigenous ethics. While many Indigenous people adhere to elements of Christianity or other religious philosophies, many Indigenous people still maintain traditional cosmovisions.[12] These cosmovisions—ways that Indigenous communities understand life, earth, the universe and its moral constructs—demand adherence not to a supreme ethereal deity but to Indigenous pragmatism and, therefore, the “center good” so that an individual can live a happy and healthy life. Failing to adhere to the cosmovisions results in the decay of that person, and even their community, on physical, emotional, and spiritual levels.[13] ANALYSIS Previous work on Indigenous bioethics has referred to Indigenous philosophies in a secondary way. This placement of Indigenous ethics onto a second tier still demands that Indigenous patients adhere to dominant Western ethical discourses. Indigenous pragmatism calls for a plurality of thought and method. This means that Western ethics can apply in the treatment of Indigenous patients, but that Indigenous ethics must be on an equal plane or elevated above Western ethics in the treatment of Indigenous patients. The Indian Health Service (IHS) in the US provides an example. This system is treaty-obligated to provide health care to Indigenous Americans, the only racialized group in the US to have federal government-provided healthcare. However, many providers in the expansive IHS system operate from a Western ethic. This viewpoint leads to negative interactions and miscommunications.[14] It also adds to the persistent racism and ethnocentrism documented in the IHS by the US Commission on Civil Rights.[15] Multiple paths to achieving Indigenous health equity exist. However, a vital component is engaging a specific Indigenous ethic based on Indigenous philosophies rather than merely referencing them. Indigenous patients of the IHS can receive more culturally sensitive and competent care if they are engaged on a cultural level.   A brief hypothetical to illustrate: a non-Indigenous IHS physician requests an ethics consult to convince a patient they need surgery; the provider views this as non-maleficence (a way to avoid harm to the patient) and is frustrated with the involvement of the patient’s family and close friends in the patient’s decision to delay the surgery; the doctor views the involvement of other people as a violation of the patient’s autonomy. The Indigenous patient views the physician’s continued insistence as offensive and becomes frustrated that their decision to delay is not respected. The physician and the patient view both autonomy and non-maleficence differently. The Indigenous patient views autonomy as a fundamental interaction between oneself and one’s communities, not as a purely individual choice. The patient also does not view the physician’s actions as doing no harm, as the violation of the Indigenous worldview is causing distress to the patient. In this hypothetical, the physician could alleviate the distress by understanding the patient’s ethics. The decision to delay the operation based on community feedback is an autonomous decision of the patient who does not view the delay as a harm but as a positive, since they are patiently exploring their options and ensuring that their community is equally comfortable with the decision. In this case, the insistence that the patient violate their Indigenous pragmatism causes the harm and violation of autonomy. l.     Progress and Considerations Bioethics has made important strides toward cultural competency and many programs train students in medical disciplines. While there have been significant improvements in cultural competency training and recognition, programs still do not adequately consider care of Indigenous patients,[16] and they do not sufficiently consider the complexities of Indigenous decision making. US laws privilege the Western conception of autonomy and the accompanying understanding of individualism.[17] However, these laws and frameworks do not sufficiently consider complex Indigenous nuances and communal social structures. While Indigenous patients, or their proxy, will be the one to sign off on a decision, the process still centers a Western understanding of individualism, autonomy, and body[18] that constrains Indigenous patients and often demands violation of their balanced “center good.” Resolving this conflict conceptually and in practice is not easy and will continue to require patience and the necessary involvement of Indigenous communities, bioethicists, and practitioners. CONCLUSION Indigenous philosophies often oppose traditional Western ethics employed in US healthcare. The IHS provides care to Indigenous people and could employ and further develop the use of Indigenous pragmatism and ethics. An Indigenous patient that is treated based on Indigenous philosophies and ethics can receive care and consultations that incorporate their interactions and responsibilities to their families and communities and recognize that the Indigenous patient will have a plurality of thought systems and requests based in multiple cultural contexts that may seem foreign to non-Indigenous practitioners and ethicists. The center good here demands fully incorporating Indigenous philosophies and bioethics. Failing to maintain this center good and develop explicit Indigenous ethics for all Indigenous patients—inside and out of the IHS—only serves to continue the severe healthcare inequalities experienced by Indigenous communities. - [1] Léon-Portilla, Miguel. 1963. Aztec Thought and Culture: A Study of the Ancient Nahuatl Mind. Norman, OK: University of Oklahoma Press; Pratt, Scott L. 2002. Native Pragmatism: Rethinking the Roots of American Philosophy. Bloomington, IN: Indiana University Press.    [2] Ellerby, Jonathan H., John McKenzie, Stanley McKay, Gilbert J. Gariépy, Joseph M. Kaufert. 2000. “Bioethics for clinicians: 18. Aboriginal cultures”. Canadian Medical Association Journal 163(7):845-850; Cordova, V.F. 2007. How It Is: The Native American Philosophy of V.F. Cordova. Tucson, AZ: University of Arizona Press. [3] Pratt 2002 [4] Dunbar-Ortiz, Roxanne. 2014. An Indigenous Peoples' History of the United States. Boston, MA: Beacon Press; Graeber, David and David Wengrow. 2021. The Dawn of Everything: A New History of Humanity. New York, NY: Farrar, Straus and Giroux.  [5] Maffie, James. 2019. “Weaving the Good Life in a Living World: Recip