Hasil untuk "Medical philosophy. Medical ethics"

Jia Wei, Xiaoqi Zhao, Jonghye Woo et al.

Single domain generalization (SDG) has recently attracted growing attention in medical image segmentation. One promising strategy for SDG is to leverage consistent semantic shape priors across different imaging protocols, scanner vendors, and clinical sites. However, existing dictionary learning methods that encode shape priors often suffer from limited representational power with a small set of offline computed shape elements, or overfitting when the dictionary size grows. Moreover, they are not readily compatible with large foundation models such as the Segment Anything Model (SAM). In this paper, we propose a novel Mixture-of-Shape-Experts (MoSE) framework that seamlessly integrates the idea of mixture-of-experts (MoE) training into dictionary learning to efficiently capture diverse and robust shape priors. Our method conceptualizes each dictionary atom as a shape expert, which specializes in encoding distinct semantic shape information. A gating network dynamically fuses these shape experts into a robust shape map, with sparse activation guided by SAM encoding to prevent overfitting. We further provide this shape map as a prompt to SAM, utilizing the powerful generalization capability of SAM through bidirectional integration. All modules, including the shape dictionary, are trained in an end-to-end manner. Extensive experiments on multiple public datasets demonstrate its effectiveness.

Amr Alshatnawi, Remi Sampaleanu, David Liebovitz

Artificial Intelligence (AI) has been advancing rapidly and with the advent of large language models (LLMs) in late 2022, numerous opportunities have emerged for adopting this technology across various domains, including medicine. These innovations hold immense potential to revolutionize and modernize medical education. Our research project leverages large language models to enhance medical education and address workflow challenges through the development of MediTools - AI Medical Education. This prototype application focuses on developing interactive tools that simulate real-life clinical scenarios, provide access to medical literature, and keep users updated with the latest medical news. Our first tool is a dermatology case simulation tool that uses real patient images depicting various dermatological conditions and enables interaction with LLMs acting as virtual patients. This platform allows users to practice their diagnostic skills and enhance their clinical decision-making abilities. The application also features two additional tools: an AI-enhanced PubMed tool for engaging with LLMs to gain deeper insights into research papers, and a Google News tool that offers LLM generated summaries of articles for various medical specialties. A comprehensive survey has been conducted among medical professionals and students to gather initial feedback on the effectiveness and user satisfaction of MediTools, providing insights for further development and refinement of the application. This research demonstrates the potential of AI-driven tools in transforming and revolutionizing medical education, offering a scalable and interactive platform for continuous learning and skill development.

Antonio Romano, Giuseppe Riccio, Mariano Barone et al.

Online medical forums have long served as vital platforms where patients seek professional healthcare advice, generating vast amounts of valuable knowledge. However, the informal nature and linguistic complexity of forum interactions pose significant challenges for automated question answering systems, especially when dealing with non-English languages. We present two comprehensive Italian medical benchmarks: \textbf{IMB-QA}, containing 782,644 patient-doctor conversations from 77 medical categories, and \textbf{IMB-MCQA}, comprising 25,862 multiple-choice questions from medical specialty examinations. We demonstrate how Large Language Models (LLMs) can be leveraged to improve the clarity and consistency of medical forum data while retaining their original meaning and conversational style, and compare a variety of LLM architectures on both open and multiple-choice question answering tasks. Our experiments with Retrieval Augmented Generation (RAG) and domain-specific fine-tuning reveal that specialized adaptation strategies can outperform larger, general-purpose models in medical question answering tasks. These findings suggest that effective medical AI systems may benefit more from domain expertise and efficient information retrieval than from increased model scale. We release both datasets and evaluation frameworks in our GitHub repository to support further research on multilingual medical question answering: https://github.com/PRAISELab-PicusLab/IMB.

Ivana Tutić Grokša, Ana Depope, Tijana Trako Poljak et al.

Human reproduction has traditionally been an important issue in medical ethics. Advances in medical technology and the development of medically assisted reproduction (MAR) procedures are creating new bioethical dilemmas. This study is based on a quantitative approach using the survey method on a convenience sample of students (N=1097) from five universities from four fields of study – Medicine, Law, Theology and Philosophy – in Croatia, Greece and Italy. The aim of this study was to investigate students’ attitudes towards various aspects of medically assisted reproduction. Three hypotheses were tested using t-tests and ANOVA to examine differences in attitudes based on variables such as country, field of study, gender, year of study, religiosity, political orientation, financial status and size of their place of residence. Despite sharing a common Mediterranean cultural heritage, students from Italy showed a greater disapproval of MAR, but due to the small effect size, this difference should be interpreted with caution and the hypothesis could not be fully confirmed. In addition, Theology students had statistically significantly more negative attitudes toward MAR. Regarding differences in students’ socio-demographic characteristics, women, older students, individuals who are not religious and those who are politically left-oriented tended to have more liberal attitudes toward MAR. The results enable further reflection on the concept of Mediterranean Bioethics. These findings highlight how disciplinary background and religiosity shape ethical attitudes toward MAR within the Mediterranean context.

Zamira Verónika Montiel Boehringer

Los desórdenes de la consciencia (DoC) son un desafío para la comprensión del dolor y del sufrimiento, ya que son experiencias subjetivas complejas que involucran múltiples redes neuronales. Estudios neurofisiológicos y de neuroimagen sugieren que algunos pacientes en estado vegetativo podrían experimentar dolor y que aquellos con consciencia encubierta tienen mayor probabilidad de percibirlo. Sin embargo, los pacientes al no poderlo expresar son ignorados por el personal médico. Esta revisión narrativa aborda las investigaciones neurocientíficas recientes sobre el dolor en estos pacientes, resaltando la necesidad de reconsiderarlo en la práctica clínica. En un contexto de incertidumbre diagnóstica y pronóstica, es fundamental profundizar en la investigación y establecer marcos éticos que garanticen el respeto a la autonomía y el bienestar de estos pacientes, por lo que se abordan los dilemas bioéticos derivados del uso de neurotecnologías en pacientes que no pueden expresar su consentimiento. 

Nan MO

Patients with sexually transmitted AIDS frequently encounter discrimination in medical treatment. To effectively reduce discrimination, it is necessary for medical personnel to uphold the principle of equality. The core of the principle involves determining which forms of differential treatment are unjustified, based on three criteria: first, whether the medical staff's conduct is abnormal; second, whether the abnormality is related to the patient's HIV infection; third, whether the abnormality has a sufficient and reasonable reason based on medical science. In the face of the reasons for the interweaving of medical necessity and value evaluation, it is necessary to weigh risks and benefits, compliance with regulations and industry norms, and make a comprehensive judgment around the rights and interests of patients. Practicing the principle of equality also requires that healthcare professionals refrain from inquiring into a patient's route of HIV transmission unless medically necessary, and proactively communicate regarding any diagnostic or treatment decisions that may lead to disagreement.

Binyan Hu, A. K. Qin

Medical image segmentation (MIS) plays an instrumental role in medical image analysis, where considerable effort has been devoted to automating the process. Currently, mainstream MIS approaches are based on deep neural networks (DNNs), which are typically trained on a dataset with annotations produced by certain medical experts. In the medical domain, the annotations generated by different experts can be inherently distinct due to complexity of medical images and variations in expertise and post-segmentation missions. Consequently, the DNN model trained on the data annotated by some experts may hardly adapt to a new expert. In this work, we evaluate a customised expert-adaptive method, characterised by multi-expert annotation, multi-task DNN-based model training, and lightweight model fine-tuning, to investigate model's adaptivity to a new expert in the situation where the amount and mobility of training images are limited. Experiments conducted on brain MRI segmentation tasks with limited training data demonstrate its effectiveness and the impact of its key parameters.

Yuxuan Zhou, Xien Liu, Chen Ning et al.

Mastering medical knowledge is crucial for medical-specific LLMs. However, despite the existence of medical benchmarks like MedQA, a unified framework that fully leverages existing knowledge bases to evaluate LLMs' mastery of medical knowledge is still lacking. In the study, we propose a novel framework PretexEval that dynamically generates reliable and diverse test samples to evaluate LLMs for any given medical knowledge base. We notice that test samples produced directly from knowledge bases by templates or LLMs may introduce factual errors and also lack diversity. To address these issues, we introduce a novel schema into our proposed evaluation framework that employs predicate equivalence transformations to produce a series of variants for any given medical knowledge point. Finally, these produced predicate variants are converted into textual language, resulting in a series of reliable and diverse test samples to evaluate whether LLMs fully master the given medical factual knowledge point. Here, we use our proposed framework to systematically investigate the mastery of medical factual knowledge of 12 well-known LLMs, based on two knowledge bases that are crucial for clinical diagnosis and treatment. The evaluation results illustrate that current LLMs still exhibit significant deficiencies in fully mastering medical knowledge, despite achieving considerable success on some famous public benchmarks. These new findings provide valuable insights for developing medical-specific LLMs, highlighting that current LLMs urgently need to strengthen their comprehensive and in-depth mastery of medical knowledge before being applied to real-world medical scenarios.

George Webber, Yuya Mizuno, Oliver D. Howes et al.

Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image distribution modeling. SGM-based reconstruction has recently been applied to simulated positron emission tomography (PET) datasets, showing improved contrast recovery for out-of-distribution lesions relative to the state-of-the-art. However, existing methods for SGM-based reconstruction from PET data suffer from slow reconstruction, burdensome hyperparameter tuning and slice inconsistency effects (in 3D). In this work, we propose a practical methodology for fully 3D reconstruction that accelerates reconstruction and reduces the number of critical hyperparameters by matching the likelihood of an SGM's reverse diffusion process to a current iterate of the maximum-likelihood expectation maximization algorithm. Using the example of low-count reconstruction from simulated [$^{18}$F]DPA-714 datasets, we show our methodology can match or improve on the NRMSE and SSIM of existing state-of-the-art SGM-based PET reconstruction while reducing reconstruction time and the need for hyperparameter tuning. We evaluate our methodology against state-of-the-art supervised and conventional reconstruction algorithms. Finally, we demonstrate a first-ever implementation of SGM-based reconstruction for real 3D PET data, specifically [$^{18}$F]DPA-714 data, where we integrate perpendicular pre-trained SGMs to eliminate slice inconsistency issues.

Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu

Federated learning is emerging as a promising machine learning technique in the medical field for analyzing medical images, as it is considered an effective method to safeguard sensitive patient data and comply with privacy regulations. However, recent studies have revealed that the default settings of federated learning may inadvertently expose private training data to privacy attacks. Thus, the intensity of such privacy risks and potential mitigation strategies in the medical domain remain unclear. In this paper, we make three original contributions to privacy risk analysis and mitigation in federated learning for medical data. First, we propose a holistic framework, MedPFL, for analyzing privacy risks in processing medical data in the federated learning environment and developing effective mitigation strategies for protecting privacy. Second, through our empirical analysis, we demonstrate the severe privacy risks in federated learning to process medical images, where adversaries can accurately reconstruct private medical images by performing privacy attacks. Third, we illustrate that the prevalent defense mechanism of adding random noises may not always be effective in protecting medical images against privacy attacks in federated learning, which poses unique and pressing challenges related to protecting the privacy of medical data. Furthermore, the paper discusses several unique research questions related to the privacy protection of medical data in the federated learning environment. We conduct extensive experiments on several benchmark medical image datasets to analyze and mitigate the privacy risks associated with federated learning for medical data.

Haitao Zhang, Bo An

Large models are a hot research topic in the field of artificial intelligence. Leveraging their generative capabilities has the potential to enhance the level and quality of medical services. In response to the limitations of current large language models, which often struggle with accuracy and have narrow capabilities in medical applications, this paper presents a Chinese medical large language model, MedGo. MedGo was trained using a combination of high quality unsupervised medical data, supervised data, and preference alignment data, aimed at enhancing both its versatility and precision in medical tasks. The model was evaluated through the public CBLUE benchmark and a manually constructed dataset ClinicalQA. The results demonstrate that MedGo achieved promising performance across various Chinese medical information processing tasks, achieved the first place in the CBLUE evaluation. Additionally, on our constructed dataset ClinicalQA, MedGo outperformed its base model Qwen2, highlighting its potential to improve both automated medical question answering and clinical decision support. These experimental results demonstrate that MedGo possesses strong information processing capabilities in the medical field. At present, we have successfully deployed MedGo at Shanghai East Hospital.

Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar et al.

Research on language technology for the development of medical applications is currently a hot topic in Natural Language Understanding and Generation. Thus, a number of large language models (LLMs) have recently been adapted to the medical domain, so that they can be used as a tool for mediating in human-AI interaction. While these LLMs display competitive performance on automated medical texts benchmarks, they have been pre-trained and evaluated with a focus on a single language (English mostly). This is particularly true of text-to-text models, which typically require large amounts of domain-specific pre-training data, often not easily accessible for many languages. In this paper, we address these shortcomings by compiling, to the best of our knowledge, the largest multilingual corpus for the medical domain in four languages, namely English, French, Italian and Spanish. This new corpus has been used to train Medical mT5, the first open-source text-to-text multilingual model for the medical domain. Additionally, we present two new evaluation benchmarks for all four languages with the aim of facilitating multilingual research in this domain. A comprehensive evaluation shows that Medical mT5 outperforms both encoders and similarly sized text-to-text models for the Spanish, French, and Italian benchmarks, while being competitive with current state-of-the-art LLMs in English.

Xiaodan Xing, Huiyu Zhou, Yingying Fang et al.

AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exhibit remarkable realism with their real copies, remains a concern. To mitigate this challenge, image generators such as DALLE and Imagen, have integrated digital watermarks aimed at facilitating the discernment of synthetic images' authenticity. These watermarks are embedded within the image pixels and are invisible to the human eye while remains their detectability. Nevertheless, a comprehensive investigation into the potential impact of these invisible watermarks on the utility of synthetic medical images has been lacking. In this study, we propose the incorporation of invisible watermarks into synthetic medical images and seek to evaluate their efficacy in the context of downstream classification tasks. Our goal is to pave the way for discussions on the viability of such watermarks in boosting the detectability of synthetic medical images, fortifying ethical standards, and safeguarding against data pollution and potential scams.

Haoshen Wang, Zhentao Liu, Kaicong Sun et al.

The generation of medical images presents significant challenges due to their high-resolution and three-dimensional nature. Existing methods often yield suboptimal performance in generating high-quality 3D medical images, and there is currently no universal generative framework for medical imaging. In this paper, we introduce a 3D Medical Latent Diffusion (3D MedDiffusion) model for controllable, high-quality 3D medical image generation. 3D MedDiffusion incorporates a novel, highly efficient Patch-Volume Autoencoder that compresses medical images into latent space through patch-wise encoding and recovers back into image space through volume-wise decoding. Additionally, we design a new noise estimator to capture both local details and global structural information during diffusion denoising process. 3D MedDiffusion can generate fine-detailed, high-resolution images (up to 512x512x512) and effectively adapt to various downstream tasks as it is trained on large-scale datasets covering CT and MRI modalities and different anatomical regions (from head to leg). Experimental results demonstrate that 3D MedDiffusion surpasses state-of-the-art methods in generative quality and exhibits strong generalizability across tasks such as sparse-view CT reconstruction, fast MRI reconstruction, and data augmentation for segmentation and classification. Source code and checkpoints are available at https://github.com/ShanghaiTech-IMPACT/3D-MedDiffusion.

Jimena Mónica Muñoz Merino

El Hospital Johns Hopkins fue condenado a pagar 261 millones de dólares a la familia Kowalski tras una demanda que revela fallos éticos y sistémicos en la atención médica. La historia sigue a Maya, diagnosticada con Síndrome de dolor regional complejo (SDRC). El tratamiento con ketamina se convierte en una tragedia cuando malentendidos llevan a acusaciones falsas contra la madre. Maya es separada de sus padres, enfrentando consecuencias devastadoras. Este caso destaca el “gaslighting médico”. La falta de comunicación, responsabilidad institucional, la mala relación médico-paciente y no maleficencia, contribuyeron a esta tragedia y plantea preguntas éticas sobre el uso de tratamientos experimentales. La historia subraya la importancia de una relación médico-paciente basada en la confianza y la empatía, así como la necesidad de formación ética continua para los profesionales de la salud. La bioética debe ser fundamental en la aten- ción médica para evitar tragedias como la de la familia Kowalski.

Prashant Shrestha, Sanskar Amgain, Bidur Khanal et al.

Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to the scarcity of labeled data in the medical domain. By leveraging paired/unpaired vision and text datasets through self-supervised learning, models can be trained to acquire vast knowledge and learn robust feature representations. Such pretrained models have the potential to enhance multiple downstream medical tasks simultaneously, reducing the dependency on labeled data. However, despite recent progress and its potential, there is no such comprehensive survey paper that has explored the various aspects and advancements in medical VLP. In this paper, we specifically review existing works through the lens of different pretraining objectives, architectures, downstream evaluation tasks, and datasets utilized for pretraining and downstream tasks. Subsequently, we delve into current challenges in medical VLP, discussing existing and potential solutions, and conclude by highlighting future directions. To the best of our knowledge, this is the first survey focused on medical VLP.

Jinan Bao, Hanshi Sun, Hanqiu Deng et al.

Anomaly detection (AD) is a fundamental research problem in machine learning and computer vision, with practical applications in industrial inspection, video surveillance, and medical diagnosis. In medical imaging, AD is especially vital for detecting and diagnosing anomalies that may indicate rare diseases or conditions. However, there is a lack of a universal and fair benchmark for evaluating AD methods on medical images, which hinders the development of more generalized and robust AD methods in this specific domain. To bridge this gap, we introduce a comprehensive evaluation benchmark for assessing anomaly detection methods on medical images. This benchmark encompasses six reorganized datasets from five medical domains (i.e. brain MRI, liver CT, retinal OCT, chest X-ray, and digital histopathology) and three key evaluation metrics, and includes a total of fourteen state-of-the-art AD algorithms. This standardized and well-curated medical benchmark with the well-structured codebase enables comprehensive comparisons among recently proposed anomaly detection methods. It will facilitate the community to conduct a fair comparison and advance the field of AD on medical imaging. More information on BMAD is available in our GitHub repository: https://github.com/DorisBao/BMAD

Kazuma Kobayashi, Lin Gu, Ryuichiro Hataya et al.

The amount of medical images stored in hospitals is increasing faster than ever; however, utilizing the accumulated medical images has been limited. This is because existing content-based medical image retrieval (CBMIR) systems usually require example images to construct query vectors; nevertheless, example images cannot always be prepared. Besides, there can be images with rare characteristics that make it difficult to find similar example images, which we call isolated samples. Here, we introduce a novel sketch-based medical image retrieval (SBMIR) system that enables users to find images of interest without example images. The key idea lies in feature decomposition of medical images, whereby the entire feature of a medical image can be decomposed into and reconstructed from normal and abnormal features. By extending this idea, our SBMIR system provides an easy-to-use two-step graphical user interface: users first select a template image to specify a normal feature and then draw a semantic sketch of the disease on the template image to represent an abnormal feature. Subsequently, it integrates the two kinds of input to construct a query vector and retrieves reference images with the closest reference vectors. Using two datasets, ten healthcare professionals with various clinical backgrounds participated in the user test for evaluation. As a result, our SBMIR system enabled users to overcome previous challenges, including image retrieval based on fine-grained image characteristics, image retrieval without example images, and image retrieval for isolated samples. Our SBMIR system achieves flexible medical image retrieval on demand, thereby expanding the utility of medical image databases.

Sham Santhanam, Mohit Goyal

Scientific knowledge needs to be widely disseminated across the globe, for it to be critically analyzed or to be built upon for future studies. The conventional publication model has been less accessible due to prohibitive subscription costs and hence the need arose for the open access model where the readers would have free access. The Open Science movement is not only about open-access journals but also includes open source, open data and methodology, open peer review, open-access indexing, and archiving. The prototype open access model is the gold model where researchers (themselves or supported by grants or funding agencies) pay certain article processing charges and the readers have free access to the content without any restrictions. Additionally, there is a need for free-to-use open-access platforms or repositories like PubMed Central to archive the open-access content. Institutional repository is another way for collecting, archiving, and distributing the scholarly contents of an academic institution. Preprint servers allow archiving manuscripts before they are submitted to or undergo review for publication, and they offer an important platform for freely sharing knowledge. While open-access model looks attractive, it has its challenges. Currently, the change to open-access model has meant the transfer of the financial burden, earlier borne by the readers, to the authors in the form of APCs. Irrespective of the model, there is a need to reconsider the high subscription costs and the article processing charges which are often prohibitive for many. Science must be accessible to the researchers and the public at a reasonable cost without delay.

Simin Tahmasbi, Azam Alavi

Background and Objectives: Ethical sensitivity prevents moral dilemmas; moral self-concept develops via adaptation to others’ expectations. Nursing education provides an opportunity to internalize ethical sensitivity and improve moral self-concept because nurses are often faced with serious situations that require higher ethical knowledge. Therefore, we assessed sensitivity and moral self-concept in nursing students and related factors. Methods: This quasi-experimental study was designed for one group to investigate the degree of students’ moral self-concept and ethical sensitivity in an internship program (12 months) and their related factors. Thirty-nine undergraduate nursing students were selected by census sampling method. The inclusion criteria included senior bachelor’s degree students and their desire to participate in the study. Lutzen’s ethical sensitivity questionnaire (25 items) and Lutzen’s moral self-concept questionnaire (18 items) were used. Data were analyzed by SPSS software, version 22 using described by number, percentage, Mean±SD and assessed by the paired t-test. Results: The majority of the participants (74.4%) were women (76.9%) and single, (48.7%) aged 23 years, and (76.9%) reported participation in the ethics workshops. The mean score of students’ ethical sensitivity before the internship was at a medium level (109.64±9.51), which improved to a higher level (115.56±8.88) after the internship. There was a significant difference between the mean score of ethical sensitivity before and after the internship (P<0.001). This difference was not significant for the mean score of moral self-concept. The correlation of study variables with gender, marital status, and age was not significant, while there was a significant difference regarding participation in the workshop (P<0.02). Conclusion: Ethical sensitivity was influenced by participating in ethics workshops; thus, continuous training can be effective to improve it. It can enhance ethical sensitivity and reduce the risk of unethical behavior among nurses in the future.