Hasil untuk "Medical philosophy. Medical ethics"

Dianfan WANG, Zongming ZHANG, Hui SHI

Australia is the first Western country to recognize the legal status of traditional Chinese medicine by legislation. In the process of legalization of traditional Chinese medicine, Australia went through three stages: an early period of disorder, a period of regeneration and consolidation, and a formal legislation period, which created a precedent for the overall legislation of traditional Chinese medicine. Institutionally, traditional Chinese medicine legislation in Australia has been supported by the national government, in terms of content, it reflects parity between traditional Chinese medicine and acupuncture, as well as between traditional Chinese medicine and Western medicine, procedurally, it has become increasingly standardized and systematized. The legislative process and characteristics of traditional Chinese medicine in Australia indicate that advancing legalization requires the leadership of the national government, relies on institutional organizations to ensure implementation, and establishes overall legislative objectives. Simultaneously, corresponding measures should be taken in light of the differences in national systems and legal frameworks to promote the legislative process of traditional Chinese medicine, providing a reference for its integration into the mainstream Western medical system.

Ayo Adedeji, Mardhiyah Sanni, Emmanuel Ayodele et al.

The global adoption of Large Language Models (LLMs) in healthcare shows promise to enhance clinical workflows and improve patient outcomes. However, Automatic Speech Recognition (ASR) errors in critical medical terms remain a significant challenge. These errors can compromise patient care and safety if not detected. This study investigates the prevalence and impact of ASR errors in medical transcription in Nigeria, the United Kingdom, and the United States. By evaluating raw and LLM-corrected transcriptions of accented English in these regions, we assess the potential and limitations of LLMs to address challenges related to accents and medical terminology in ASR. Our findings highlight significant disparities in ASR accuracy across regions and identify specific conditions under which LLM corrections are most effective.

Jialun Zhong, Yanzeng Li, Sen Hu et al.

Medication recommendations have become an important task in the healthcare domain, especially in measuring the accuracy and safety of medical dialogue systems (MDS). Different from the recommendation task based on electronic health records (EHRs), dialogue-based medication recommendations require research on the interaction details between patients and doctors, which is crucial but may not exist in EHRs. Recent advancements in large language models (LLM) have extended the medical dialogue domain. These LLMs can interpret patients' intent and provide medical suggestions including medication recommendations, but some challenges are still worth attention. During a multi-turn dialogue, LLMs may ignore the fine-grained medical information or connections across the dialogue turns, which is vital for providing accurate suggestions. Besides, LLMs may generate non-factual responses when there is a lack of domain-specific knowledge, which is more risky in the medical domain. To address these challenges, we propose a \textbf{G}raph-\textbf{A}ssisted \textbf{P}rompts (\textbf{GAP}) framework for dialogue-based medication recommendation. It extracts medical concepts and corresponding states from dialogue to construct an explicitly patient-centric graph, which can describe the neglected but important information. Further, combined with external medical knowledge graphs, GAP can generate abundant queries and prompts, thus retrieving information from multiple sources to reduce the non-factual responses. We evaluate GAP on a dialogue-based medication recommendation dataset and further explore its potential in a more difficult scenario, dynamically diagnostic interviewing. Extensive experiments demonstrate its competitive performance when compared with strong baselines.

Jia-Hong Huang

The increasing prevalence of retinal diseases poses a significant challenge to the healthcare system, as the demand for ophthalmologists surpasses the available workforce. This imbalance creates a bottleneck in diagnosis and treatment, potentially delaying critical care. Traditional methods of generating medical reports from retinal images rely on manual interpretation, which is time-consuming and prone to errors, further straining ophthalmologists' limited resources. This thesis investigates the potential of Artificial Intelligence (AI) to automate medical report generation for retinal images. AI can quickly analyze large volumes of image data, identifying subtle patterns essential for accurate diagnosis. By automating this process, AI systems can greatly enhance the efficiency of retinal disease diagnosis, reducing doctors' workloads and enabling them to focus on more complex cases. The proposed AI-based methods address key challenges in automated report generation: (1) A multi-modal deep learning approach captures interactions between textual keywords and retinal images, resulting in more comprehensive medical reports; (2) Improved methods for medical keyword representation enhance the system's ability to capture nuances in medical terminology; (3) Strategies to overcome RNN-based models' limitations, particularly in capturing long-range dependencies within medical descriptions; (4) Techniques to enhance the interpretability of the AI-based report generation system, fostering trust and acceptance in clinical practice. These methods are rigorously evaluated using various metrics and achieve state-of-the-art performance. This thesis demonstrates AI's potential to revolutionize retinal disease diagnosis by automating medical report generation, ultimately improving clinical efficiency, diagnostic accuracy, and patient care.

Jianan Li, Yangtao Zhou, Zhifu Zhao et al.

Recommendation systems in AI-based medical diagnostics and treatment constitute a critical component of AI in healthcare. Although some studies have explored this area and made notable progress, healthcare recommendation systems remain in their nascent stage. And these researches mainly target the treatment process such as drug or disease recommendations. In addition to the treatment process, the diagnostic process, particularly determining which medical examinations are necessary to evaluate the condition, also urgently requires intelligent decision support. To bridge this gap, we first formalize the task of medical examination recommendations. Compared to traditional recommendations, the medical examination recommendation involves more complex interactions. This complexity arises from two folds: 1) The historical medical records for examination recommendations are heterogeneous and redundant, which makes the recommendation results susceptible to noise. 2) The correlation between the medical history of patients is often irregular, making it challenging to model spatiotemporal dependencies. Motivated by the above observation, we propose a novel Diffusion-driven SpatioTemporal Graph KANsformer for Medical Examination Recommendation (DST-GKAN) with a two-stage learning paradigm to solve the above challenges. In the first stage, we exploit a task-adaptive diffusion model to distill recommendation-oriented information by reducing the noises in heterogeneous medical data. In the second stage, a spatiotemporal graph KANsformer is proposed to simultaneously model the complex spatial and temporal relationships. Moreover, to facilitate the medical examination recommendation research, we introduce a comprehensive dataset. The experimental results demonstrate the state-of-the-art performance of the proposed method compared to various competitive baselines.

Alexander Geiger, Lars Wagner, Daniel Rueckert et al.

The explainability of deep learning models remains a significant challenge, particularly in the medical domain where interpretable outputs are critical for clinical trust and transparency. Path attribution methods such as Integrated Gradients rely on a baseline representing the absence of relevant features ("missingness"). Commonly used baselines, such as all-zero inputs, are often semantically meaningless, especially in medical contexts. While alternative baseline choices have been explored, existing methods lack a principled approach to dynamically select baselines tailored to each input. In this work, we examine the notion of missingness in the medical context, analyze its implications for baseline selection, and introduce a counterfactual-guided approach to address the limitations of conventional baselines. We argue that a generated counterfactual (i.e. clinically "normal" variation of the pathological input) represents a more accurate representation of a meaningful absence of features. We use a Variational Autoencoder in our implementation, though our concept is model-agnostic and can be applied with any suitable counterfactual method. We evaluate our concept on three distinct medical data sets and empirically demonstrate that counterfactual baselines yield more faithful and medically relevant attributions, outperforming standard baseline choices as well as other related methods.

Pengchen Liang, Bin Pu, Haishan Huang et al.

The rapid development of Vision Foundation Models (VFMs), particularly Vision Transformers (ViT) and Segment Anything Model (SAM), has sparked significant advances in the field of medical image analysis. These models have demonstrated exceptional capabilities in capturing long-range dependencies and achieving high generalization in segmentation tasks. However, adapting these large models to medical image analysis presents several challenges, including domain differences between medical and natural images, the need for efficient model adaptation strategies, and the limitations of small-scale medical datasets. This paper reviews the state-of-the-art research on the adaptation of VFMs to medical image segmentation, focusing on the challenges of domain adaptation, model compression, and federated learning. We discuss the latest developments in adapter-based improvements, knowledge distillation techniques, and multi-scale contextual feature modeling, and propose future directions to overcome these bottlenecks. Our analysis highlights the potential of VFMs, along with emerging methodologies such as federated learning and model compression, to revolutionize medical image analysis and enhance clinical applications. The goal of this work is to provide a comprehensive overview of current approaches and suggest key areas for future research that can drive the next wave of innovation in medical image segmentation.

Hao WANG

The concept of the body has undergone significant changes in the post-human era, transitioning from the human body to the post-human body. The post-human body mainly includes four forms: the primitive body which resembles the traditional human body, the enhanced body that undergoes human enhancement and transformation, the virtual body constructed by virtual information technology, and the non-human body expanded by multiple entities. The post-human body has triggered and exacerbated privacy and security crises, ethical and legal crises, socioeconomic crises, creative destruction crises, social control crises, and human nature loss crises. Addressing the body crises of the post-human era requires a shift in technological perspectives, a transformation of technological ethics, a redirection of technological approaches, an improvement in technological capabilities, and an optimization of technological structures.

Lvmo LI, Mengting DONG

The limited promotion of the living will system in China reflects the normative conflicts between the right to dignity of life and the right to medical autonomy. The concept and function of living will in the cognitive paradigm of the right to life dignity, the exercise mode and the distribution mode of medical resources are blocked by the lack of legislative norms. It is necessary to clarify the primacy of living wills, integrate hospice care into the payment system through medical insurance payment reform, and establish a stratified, compound, and rapid adjudication mechanism integrating medicine and law. These multi-dimensional measures aim to enhance the implementation of the living will system and promote the transformation of patients' autonomy from ethical declarations to legally protected practices.

Juan Manuel Palomares Cantero

Este artículo examina los aspectos éticos y legales de la biojurídica en el contexto del alotrasplante compuesto vascularizado facial. Se destaca la importancia de preservar la identidad facial como un derecho fundamental debido al papel central del rostro en la comunicación y las relaciones humanas. El trasplante facial permite a las personas restaurar su apariencia original, lo que contribuye a recuperar confianza y conexión emocional. Desde una perspectiva bioética y humanista, esta intervención protege los derechos humanos y mejora el bienestar de los receptores. El consentimiento informado garantiza la autonomía del receptor, mientras que la ciencia proporciona opciones personalizadas y fomenta una ética rigurosa. El trasplante facial promueve el derecho a tener un rostro reconocible, abordando dilemas éticos y ofreciendo nuevas posibilidades en un enfoque multidisciplinario.

Prajwal Kailas, Max Homilius, Rahul C. Deo et al.

Accurate diagnostic coding of medical notes is crucial for enhancing patient care, medical research, and error-free billing in healthcare organizations. Manual coding is a time-consuming task for providers, and diagnostic codes often exhibit low sensitivity and specificity, whereas the free text in medical notes can be a more precise description of a patients status. Thus, accurate automated diagnostic coding of medical notes has become critical for a learning healthcare system. Recent developments in long-document transformer architectures have enabled attention-based deep-learning models to adjudicate medical notes. In addition, contrastive loss functions have been used to jointly pre-train large language and image models with noisy labels. To further improve the automated adjudication of medical notes, we developed an approach based on i) models for ICD-10 diagnostic code sequences using a large real-world data set, ii) large language models for medical notes, and iii) contrastive pre-training to build an integrated model of both ICD-10 diagnostic codes and corresponding medical text. We demonstrate that a contrastive approach for pre-training improves performance over prior state-of-the-art models for the MIMIC-III-50, MIMIC-III-rare50, and MIMIC-III-full diagnostic coding tasks.

Xiaoming Shi, Zeming Liu, Li Du et al.

This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, and evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, and natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshaped medical dialog systems' foundation. Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists the grand challenges of medical dialog systems, especially of large language models.

Yunsoo Kim, Jinge Wu, Yusuf Abdulle et al.

This paper introduces MedExQA, a novel benchmark in medical question-answering, to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five distinct medical specialties that are underrepresented in current datasets and further incorporating multiple explanations for each question-answer pair, we address a major gap in current medical QA benchmarks which is the absence of comprehensive assessments of LLMs' ability to generate nuanced medical explanations. Our work highlights the importance of explainability in medical LLMs, proposes an effective methodology for evaluating models beyond classification accuracy, and sheds light on one specific domain, speech language pathology, where current LLMs including GPT4 lack good understanding. Our results show generation evaluation with multiple explanations aligns better with human assessment, highlighting an opportunity for a more robust automated comprehension assessment for LLMs. To diversify open-source medical LLMs (currently mostly based on Llama2), this work also proposes a new medical model, MedPhi-2, based on Phi-2 (2.7B). The model outperformed medical LLMs based on Llama2-70B in generating explanations, showing its effectiveness in the resource-constrained medical domain. We will share our benchmark datasets and the trained model.

Jiayu Huo, Xi Ouyang, Sébastien Ourselin et al.

Rapid advancements in medical image segmentation performance have been significantly driven by the development of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). These models follow the discriminative pixel-wise classification learning paradigm and often have limited ability to generalize across diverse medical imaging datasets. In this manuscript, we introduce Generative Medical Segmentation (GMS), a novel approach leveraging a generative model to perform image segmentation. Concretely, GMS employs a robust pre-trained vision foundation model to extract latent representations for images and corresponding ground truth masks, followed by a model that learns a mapping function from the image to the mask in the latent space. Once trained, the model generates an estimated segmentation mask using the pre-trained vision foundation model to decode the predicted latent representation back into the image space. The design of GMS leads to fewer trainable parameters in the model which reduces the risk of overfitting and enhances its generalization capability. Our experimental analysis across five public datasets in different medical imaging domains demonstrates GMS outperforms existing discriminative and generative segmentation models. Furthermore, GMS is able to generalize well across datasets from different centers within the same imaging modality. Our experiments suggest GMS offers a scalable and effective solution for medical image segmentation. GMS implementation and trained model weights are available at https://github.com/King-HAW/GMS.

Thalita da Rocha Bastos, Letícia Fonseca Macedo, Yasmim Carmine Brito da Silva et al.

Resumo A pesquisa investigou o conhecimento de professores e alunos do internato médico acerca das diretivas antecipadas de vontade, que visam assegurar os direitos dos pacientes de registrar sua preferência pelos cuidados médicos a que serão submetidos quando estiverem incapacitados de tomar decisões. Trata-se de estudo transversal, descritivo, observacional, com abordagem majoritariamente quantitativa, que contou com a participação de 30 professores do curso de medicina e 121 acadêmicos de medicina vinculados a duas instituições de ensino localizadas em Belém/PA. Os resultados revelaram lacunas no conhecimento a respeito do tema, apontando a necessidade de uma abordagem mais aprofundada durante a formação e a prática médica. Conclui-se que é necessário intensificar a divulgação de diretivas antecipadas de vontade no âmbito do ensino médico, de forma a favorecer a autonomia e o compartilhamento das decisões.

Eloy Girela-Lopez, Cristina M. Beltran-Aroca, Jaime Boceta-Osuna et al.

Abstract Background Moral distress (MD) is the psychological damage caused when people are forced to witness or carry out actions which go against their fundamental moral values. The main objective was to evaluate the prevalence and predictive factors associated with MD among health professionals during the pandemic and to determine its causes. Methods A regional, observational and cross-sectional study in a sample of 566 professionals from the Public Health Service of Andalusia (68.7% female; 66.9% physicians) who completed the MMD-HP-SPA scale to determine the level of MD (0-432 points). Five dimensions were used: i) Health care; ii) Therapeutic obstinacy-futility, iii) Interpersonal relations of the Healthcare Team, iv) External pressure; v) Covering up of medical malpractice. Results The mean level of MD was 127.3 (SD=66.7; 95% CI 121.8-132.8), being higher in female (135 vs. 110.3; p<0.01), in nursing professionals (137.8 vs. 122; p<0.01) and in the community setting (136.2 vs. 118.3; p<0.001), with these variables showing statistical significance in the multiple linear regression model (p<0.001; r2=0.052). With similar results, the multiple logistic regression model showed being female was a higher risk factor (OR=2.27; 95% CI 1.5-3.4; p<0.001). 70% of the sources of MD belonged to the dimension "Health Care" and the cause "Having to attend to more patients than I can safely attend to" obtained the highest average value (Mean=9.8; SD=4.9). Conclusions Female, nursing professionals, and those from the community setting presented a higher risk of MD. The healthcare model needs to implement an ethical approach to public health issues to alleviate MD among its professionals.

María Florencia Santi, Martín Hernán Di Marco

El objetivo de este artículo es analizar, desde una perspectiva ética, la investigación cualitativa en salud. Se problematizará en particular la cuestión de los daños y riesgos que emergen en el contexto de estas investigaciones. Se brindará una definición de daño apropiada para el campo de estudio propuesto y se argumentará en pos del reconocimiento de diversos tipos de daños en investigación cualitativa. Finalmente, se presentarán dos ejemplos que dan cuenta de lo analizado. Con este trabajo se espera contribuir al debate ético en investigación cualitativa en salud reconociendo las particularidades de este campo de estudio.

Luz Nelly Tobar Bonilla, Giovane Mendieta Izquierdo

La investigación científica y la praxis profesional deben estar normadas por una serie de principios éticos que aseguren la confiabilidad de los resultados. A pesar de la relevancia que eso tiene para el avance de la ciencia, en el mundo académico a veces no se le da la suficiente importancia a lo relacionado con la ética, bioética e integridad científica (EIBIC). En ese sentido, el objetivo de este trabajo es presentar a manera de reflexión algunas estrategias conceptuales de EIBIC para la formación de estudiantes de posgrado en medicina. Se presenta un texto reflexivo sobre los aspectos propios de la EIBIC. Además, se concluye que en el contexto colombiano es necesario fortalecer la formación en EIBIC por medio de cambios curriculares, institucionales y ampliación de contenidos vinculados que son oportunos para el buen ejercicio profesional en medicina.

Qingsong Yao, Zecheng He, Yuexiang Li et al.

Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which optimize the classification logits, are easy to distinguish in the feature space, resulting in accurate reactive defenses. To better understand this phenomenon and reassess the reliability of the reactive defenses for medical AEs, we thoroughly investigate the characteristic of conventional medical AEs. Specifically, we first theoretically prove that conventional adversarial attacks change the outputs by continuously optimizing vulnerable features in a fixed direction, thereby leading to outlier representations in the feature space. Then, a stress test is conducted to reveal the vulnerability of medical images, by comparing with natural images. Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs. We then propose a simple-yet-effective hierarchical feature constraint (HFC), a novel add-on to conventional white-box attacks, which assists to hide the adversarial feature in the target feature distribution. The proposed method is evaluated on three medical datasets, both 2D and 3D, with different modalities. The experimental results demonstrate the superiority of HFC, \emph{i.e.,} it bypasses an array of state-of-the-art adversarial medical AE detectors more efficiently than competing adaptive attacks, which reveals the deficiencies of medical reactive defense and allows to develop more robust defenses in future.

Yang Ouyang, Yuchen Wu, He Wang et al.

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.