Hasil untuk "Medical philosophy. Medical ethics"

Olga Maiatska, Torsten Dünnebacke, Martin Kreuels et al.

Organic scintillators using a solid polyurethane (PU) matrix have been introduced to combine the robustness of a construction material with scintillating properties that allow gamma rays and fast neutrons to be detected efficiently and at low cost. This work compares two corresponding materials, the older M600 and the more recent M700, with EJ-276D and EJ-200 representing common plastic scintillators with and without pulse-shape discrimination (PSD) capabilities, respectively. Characterization measurements were performed with small samples of 26 mm diameter and 10 mm height, which were coupled to a photomultiplier tube (PMT) and simultaneously exposed to 252Cf fission neutrons and 137Cs gamma rays. M700 turned out to provide the best PSD performance and about the same light yield as EJ-276D, while its light pulses exhibit a shorter pulse decay. An accelerated ageing process applied in between two test campaigns was too short to trigger distinct performance degradation in any of the materials, though optical degradation was visible in EJ-276D and in EJ-200 but not in the PU-based materials. Nevertheless, the extremely robust polyurethane matrix promises advantages in medical and industrial applications where resilience and long-term stability are of crucial importance.

Fang JIA, Yong LUAN, Changli LIU

The "internet plus" model has introduced an innovative platform to the field of clinical teaching, which is an inevitable requirement for future teaching modernization. However, it has also triggered a series of ethical challenges. These issues include but are not limited to the protection of patient privacy, data security, fair access to information, fair evaluation, and weakened emotional connections between teachers and students. By improving relevant laws and regulations, strengthening the technical security measures of the platform, improving the information literacy of teachers and students, establishing a comprehensive ethical supervision system, and improving the efficiency of information interaction, it is expected to effectively address these challenges in the future, so as to promote the standardized implementation of the "Internet plus" model in clinical teaching, and cultivate more excellent medical talents.

Louis Vervoort, Vitaly Nikolaev

We propose a test for abstract causal reasoning in AI, based on scholarship in the philosophy of causation, in particular on the neuron diagrams popularized by D. Lewis. We illustrate the test on advanced Large Language Models (ChatGPT, DeepSeek and Gemini). Remarkably, these chatbots are already capable of correctly identifying causes in cases that are hotly debated in the literature. In order to assess the results of these LLMs and future dedicated AI, we propose a definition of cause in neuron diagrams with a wider validity than published hitherto, which challenges the widespread view that such a definition is elusive. We submit that these results are an illustration of how future philosophical research might evolve: as an interplay between human and artificial expertise.

Yu Shi, Shuyi Fan, Changsheng Fang et al.

Limited-angle computed tomography (LACT) offers improved temporal resolution and reduced radiation dose for cardiac imaging, but suffers from severe artifacts due to truncated projections. To address the ill-posedness of LACT reconstruction, we propose a two-stage diffusion framework guided by structured clinical metadata. In the first stage, a transformer-based diffusion model conditioned exclusively on metadata, including acquisition parameters, patient demographics, and diagnostic impressions, generates coarse anatomical priors from noise. The second stage further refines the images by integrating both the coarse prior and metadata to produce high-fidelity results. Physics-based data consistency is enforced at each sampling step in both stages using an Alternating Direction Method of Multipliers module, ensuring alignment with the measured projections. Extensive experiments on both synthetic and real cardiac CT datasets demonstrate that incorporating metadata significantly improves reconstruction fidelity, particularly under severe angular truncation. Compared to existing metadata-free baselines, our method achieves superior performance in SSIM, PSNR, nMI, and PCC. Ablation studies confirm that different types of metadata contribute complementary benefits, particularly diagnostic and demographic priors under limited-angle conditions. These findings highlight the dual role of clinical metadata in improving both reconstruction quality and efficiency, supporting their integration into future metadata-guided medical imaging frameworks.

Congzhen Shi, Ryan Rezai, Jiaxi Yang et al.

The rapid advancement of foundation models in medical imaging represents a significant leap toward enhancing diagnostic accuracy and personalized treatment. However, the deployment of foundation models in healthcare necessitates a rigorous examination of their trustworthiness, encompassing privacy, robustness, reliability, explainability, and fairness. The current body of survey literature on foundation models in medical imaging reveals considerable gaps, particularly in the area of trustworthiness. Additionally, existing surveys on the trustworthiness of foundation models do not adequately address their specific variations and applications within the medical imaging domain. This survey aims to fill that gap by presenting a novel taxonomy of foundation models used in medical imaging and analyzing the key motivations for ensuring their trustworthiness. We review current research on foundation models in major medical imaging applications, focusing on segmentation, medical report generation, medical question and answering (Q\&A), and disease diagnosis. These areas are highlighted because they have seen a relatively mature and substantial number of foundation models compared to other applications. We focus on literature that discusses trustworthiness in medical image analysis manuscripts. We explore the complex challenges of building trustworthy foundation models for each application, summarizing current concerns and strategies for enhancing trustworthiness. Furthermore, we examine the potential of these models to revolutionize patient care. Our analysis underscores the imperative for advancing towards trustworthy AI in medical image analysis, advocating for a balanced approach that fosters innovation while ensuring ethical and equitable healthcare delivery.

Louise McCormack, Malika Bendechache

Trustworthy Artificial Intelligence (TAI) integrates ethics that align with human values, looking at their influence on AI behaviour and decision-making. Primarily dependent on self-assessment, TAI evaluation aims to ensure ethical standards and safety in AI development and usage. This paper reviews the current TAI evaluation methods in the literature and offers a classification, contributing to understanding self-assessment methods in this field.

Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Sara Pieri et al.

We introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. It enables multi-turn conversation in Arabic and English and supports diverse medical imaging modalities, including radiology, CT, and histology. To train BiMediX2, we curate BiMed-V, an extensive Arabic-English bilingual healthcare dataset consisting of 1.6M samples of diverse medical interactions. This dataset supports a range of medical Large Language Model (LLM) and Large Multimodal Model (LMM) tasks, including multi-turn medical conversations, report generation, and visual question answering (VQA). We also introduce BiMed-MBench, the first Arabic-English medical LMM evaluation benchmark, verified by medical experts. BiMediX2 demonstrates excellent performance across multiple medical LLM and LMM benchmarks, achieving state-of-the-art results compared to other open-sourced models. On BiMed-MBench, BiMediX2 outperforms existing methods by over 9% in English and more than 20% in Arabic evaluations. Additionally, it surpasses GPT-4 by approximately 9% in UPHILL factual accuracy evaluations and excels in various medical VQA, report generation, and report summarization tasks. Our trained models, instruction set, and source code are available at https://github.com/mbzuai-oryx/BiMediX2

Yingbo Ma, Yukyeong Song, Jeremy A. Balch et al.

As more clinical workflows continue to be augmented by artificial intelligence (AI), AI literacy among physicians will become a critical requirement for ensuring safe and ethical AI-enabled patient care. Despite the evolving importance of AI in healthcare, the extent to which it has been adopted into traditional and often-overloaded medical curricula is currently unknown. In a scoping review of 1,699 articles published between January 2016 and June 2024, we identified 18 studies which propose guiding frameworks, and 11 studies documenting real-world instruction, centered around the integration of AI into medical education. We found that comprehensive guidelines will require greater clinical relevance and personalization to suit medical student interests and career trajectories. Current efforts highlight discrepancies in the teaching guidelines, emphasizing AI evaluation and ethics over technical topics such as data science and coding. Additionally, we identified several challenges associated with integrating AI training into the medical education program, including a lack of guidelines to define medical students AI literacy, a perceived lack of proven clinical value, and a scarcity of qualified instructors. With this knowledge, we propose an AI literacy framework to define competencies for medical students. To prioritize relevant and personalized AI education, we categorize literacy into four dimensions: Foundational, Practical, Experimental, and Ethical, with tailored learning objectives to the pre-clinical, clinical, and clinical research stages of medical education. This review provides a road map for developing practical and relevant education strategies for building an AI-competent healthcare workforce.

Erva Nihan Kandemir, Jill-Jenn Vie, Adam Sanchez-Ayte et al.

Accurate estimation of question difficulty and prediction of student performance play key roles in optimizing educational instruction and enhancing learning outcomes within digital learning platforms. The Elo rating system is widely recognized for its proficiency in predicting student performance by estimating both question difficulty and student ability while providing computational efficiency and real-time adaptivity. This paper presents an adaptation of a multi concept variant of the Elo rating system to the data collected by a medical training platform, a platform characterized by a vast knowledge corpus, substantial inter-concept overlap, a huge question bank with significant sparsity in user question interactions, and a highly diverse user population, presenting unique challenges. Our study is driven by two primary objectives: firstly, to comprehensively evaluate the Elo rating systems capabilities on this real-life data, and secondly, to tackle the issue of imprecise early stage estimations when implementing the Elo rating system for online assessments. Our findings suggest that the Elo rating system exhibits comparable accuracy to the well-established logistic regression model in predicting final exam outcomes for users within our digital platform. Furthermore, results underscore that initializing Elo rating estimates with historical data remarkably reduces errors and enhances prediction accuracy, especially during the initial phases of student interactions.

Andre Ye

Vision is an important metaphor in ethical and political questions of knowledge. The feminist philosopher Donna Haraway points out the ``perverse'' nature of an intrusive, alienating, all-seeing vision (to which we might cry out ``stop looking at me!''), but also encourages us to embrace the embodied nature of sight and its promises for genuinely situated knowledge. Current technologies of machine vision -- surveillance cameras, drones (for war or recreation), iPhone cameras -- are usually construed as instances of the former rather than the latter, and for good reasons. However, although in no way attempting to diminish the real suffering these technologies have brought about in the world, I make the case for understanding technologies of computer vision as material instances of embodied seeing and situated knowing. Furthermore, borrowing from Iris Murdoch's concept of moral vision, I suggest that these technologies direct our labor towards self-reflection in ethically significant ways. My approach draws upon paradigms in computer vision research, phenomenology, and feminist epistemology. Ultimately, this essay is an argument for directing more philosophical attention from merely criticizing technologies of vision as ethically deficient towards embracing them as complex, methodologically and epistemologically important objects.

Xia Li, Runzhao Yang, Xiangtai Li et al.

Motion information from 4D medical imaging offers critical insights into dynamic changes in patient anatomy for clinical assessments and radiotherapy planning and, thereby, enhances the capabilities of 3D image analysis. However, inherent physical and technical constraints of imaging hardware often necessitate a compromise between temporal resolution and image quality. Frame interpolation emerges as a pivotal solution to this challenge. Previous methods often suffer from discretion when they estimate the intermediate motion and execute the forward warping. In this study, we draw inspiration from fluid mechanics to propose a novel approach for continuously modeling patient anatomic motion using implicit neural representation. It ensures both spatial and temporal continuity, effectively bridging Eulerian and Lagrangian specifications together to naturally facilitate continuous frame interpolation. Our experiments across multiple datasets underscore the method's superior accuracy and speed. Furthermore, as a case-specific optimization (training-free) approach, it circumvents the need for extensive datasets and addresses model generalization issues.

Vijay Kumar, Kolin Paul

Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of fraudulent practices by healthcare professionals and social workers exchanging unverified images via mobile applications. To mitigate these risks, this study proposes using watermarking techniques to embed a device fingerprint (DFP) into captured images, ensuring data provenance. The DFP, representing the unique attributes of the capturing device and raw image, is embedded into raw images before storage, thus enabling verification of image authenticity and source. Moreover, a robust remote validation method is introduced to authenticate images, enhancing the integrity of medical image data in interconnected healthcare systems. Through a case study on mobile fundus imaging, the effectiveness of the proposed framework is evaluated in terms of computational efficiency, image quality, security, and trustworthiness. This approach is suitable for a range of applications, including telemedicine, the Internet of Medical Things (IoMT), eHealth, and Medical Cyber-Physical Systems (MCPS) applications, providing a reliable means to maintain data provenance in diagnostic settings utilizing medical images or videos.

Ana Paula Neroni Stina Saura, Izabel Alves das Chagas Valóta, Maiara Rodrigues dos Santos et al.

Resumo Este artigo busca identificar fatores que podem promover ou prejudicar a qualidade de vida profissional dos profissionais oncológicos segundo critérios de fadiga e satisfação por compaixão. Utilizou-se estudo bibliográfico descritivo, tipo revisão integrativa, sem recorte temporal. Utilizaram-se as bases de dados CINAHL, Embase, Web of Science, PsycINFO, Scopus, MEDLINE e Biblioteca Virtual em Saúde para a pesquisa analisada por três revisores independentes. Incluíram-se estudos primários nos idiomas português, inglês e espanhol. Realizaram-se análise para alcançar os objetivos propostos neste estudo e síntese dos dados para a apresentação em tabelas e categorias temáticas. Como resultados, selecionaram-se 18 artigos para análise entre os 909 encontrados. Evidenciou-se que fatores sociodemográficos, internos e externos aos indivíduos podem alterar a qualidade de vida profissional. Concluiu-se que características intrínsecas e subjetivas, bem como aspectos do ambiente de trabalho, contribuíram para o desenvolvimento da fadiga por compaixão e da satisfação por compaixão.

Bram de Wilde, Anindo Saha, Maarten de Rooij et al.

Diffusion models for text-to-image generation, known for their efficiency, accessibility, and quality, have gained popularity. While inference with these systems on consumer-grade GPUs is increasingly feasible, training from scratch requires large captioned datasets and significant computational resources. In medical image generation, the limited availability of large, publicly accessible datasets with text reports poses challenges due to legal and ethical concerns. This work shows that adapting pre-trained Stable Diffusion models to medical imaging modalities is achievable by training text embeddings using Textual Inversion. In this study, we experimented with small medical datasets (100 samples each from three modalities) and trained within hours to generate diagnostically accurate images, as judged by an expert radiologist. Experiments with Textual Inversion training and inference parameters reveal the necessity of larger embeddings and more examples in the medical domain. Classification experiments show an increase in diagnostic accuracy (AUC) for detecting prostate cancer on MRI, from 0.78 to 0.80. Further experiments demonstrate embedding flexibility through disease interpolation, combining pathologies, and inpainting for precise disease appearance control. The trained embeddings are compact (less than 1 MB), enabling easy data sharing with reduced privacy concerns.

Shaodong Wang, Qing Li, Wenli Zhang

Effectively representing medical concepts and patients is important for healthcare analytical applications. Representing medical concepts for healthcare analytical tasks requires incorporating medical domain knowledge and prior information from patient description data. Current methods, such as feature engineering and mapping medical concepts to standardized terminologies, have limitations in capturing the dynamic patterns from patient description data. Other embedding-based methods have difficulties in incorporating important medical domain knowledge and often require a large amount of training data, which may not be feasible for most healthcare systems. Our proposed framework, MD-Manifold, introduces a novel approach to medical concept and patient representation. It includes a new data augmentation approach, concept distance metric, and patient-patient network to incorporate crucial medical domain knowledge and prior data information. It then adapts manifold learning methods to generate medical concept-level representations that accurately reflect medical knowledge and patient-level representations that clearly identify heterogeneous patient cohorts. MD-Manifold also outperforms other state-of-the-art techniques in various downstream healthcare analytical tasks. Our work has significant implications in information systems research in representation learning, knowledge-driven machine learning, and using design science as middle-ground frameworks for downstream explorative and predictive analyses. Practically, MD-Manifold has the potential to create effective and generalizable representations of medical concepts and patients by incorporating medical domain knowledge and prior data information. It enables deeper insights into medical data and facilitates the development of new analytical applications for better healthcare outcomes.

Wenxiong Liao, Zhengliang Liu, Haixing Dai et al.

Background: Large language models such as ChatGPT are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the Internet. However, medical texts such as clinical notes and diagnoses require rigorous validation, and erroneous medical content generated by ChatGPT could potentially lead to disinformation that poses significant harm to healthcare and the general public. Objective: This research is among the first studies on responsible and ethical AIGC (Artificial Intelligence Generated Content) in medicine. We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT. Methods: We first construct a suite of datasets containing medical texts written by human experts and generated by ChatGPT. In the next step, we analyze the linguistic features of these two types of content and uncover differences in vocabulary, part-of-speech, dependency, sentiment, perplexity, etc. Finally, we design and implement machine learning methods to detect medical text generated by ChatGPT. Results: Medical texts written by humans are more concrete, more diverse, and typically contain more useful information, while medical texts generated by ChatGPT pay more attention to fluency and logic, and usually express general terminologies rather than effective information specific to the context of the problem. A BERT-based model can effectively detect medical texts generated by ChatGPT, and the F1 exceeds 95%.

P Ravi Shankar

For several decades the first MBBS (undergraduate medical course) in India was of 18 months duration. This time was exclusively devoted to learning the preclinical subjects of Anatomy, Physiology, and Biochemistry. There was little to no early clinical exposure and communication skills, empathy, the patient perspective, and the medical humanities were not addressed. In this article, the author discusses his preclinical years in a government medical college in Kerala, India.

Albrecht Kurze, Arne Berger

This paper relates findings of own research in the domain of co-design tools in terms of ethical aspects and their opportunities for inspiration and in HCI education. We overview a number of selected general-purpose HCI/design tools as well as domain specific tools for the Internet of Things. These tools are often card-based, not only suitable for workshops with co-designers but also for internal workshops with students to include these aspects in the built-up of their expertise, sometimes even in a playful way.

Mingyuan Meng, Lei Bi, Dagan Feng et al.

Deformable image registration is a crucial step in medical image analysis for finding a non-linear spatial transformation between a pair of fixed and moving images. Deep registration methods based on Convolutional Neural Networks (CNNs) have been widely used as they can perform image registration in a fast and end-to-end manner. However, these methods usually have limited performance for image pairs with large deformations. Recently, iterative deep registration methods have been used to alleviate this limitation, where the transformations are iteratively learned in a coarse-to-fine manner. However, iterative methods inevitably prolong the registration runtime, and tend to learn separate image features for each iteration, which hinders the features from being leveraged to facilitate the registration at later iterations. In this study, we propose a Non-Iterative Coarse-to-finE registration Network (NICE-Net) for deformable image registration. In the NICE-Net, we propose: (i) a Single-pass Deep Cumulative Learning (SDCL) decoder that can cumulatively learn coarse-to-fine transformations within a single pass (iteration) of the network, and (ii) a Selectively-propagated Feature Learning (SFL) encoder that can learn common image features for the whole coarse-to-fine registration process and selectively propagate the features as needed. Extensive experiments on six public datasets of 3D brain Magnetic Resonance Imaging (MRI) show that our proposed NICE-Net can outperform state-of-the-art iterative deep registration methods while only requiring similar runtime to non-iterative methods.

Jesse Tanguay, Mats Persson

Spectroscopic x-ray detectors (SXDs) are poised to play a substantial role in the next generation of medical x-ray imaging. Evaluating their performance in terms of the detective quantum efficiency (DQE) requires normalization of the frequency-dependent signal-to-noise ratio (SNR) by that of an ideal SXD. We provide mathematical expressions of the SNR of ideal SXDs for quantification and detection tasks and tabulate their numeric values for standardized tasks. We propose using standardized RQA-series x-ray spectra. We define ideal SXDs as those that (1) have an infinite number of infinitesimal energy bins, (2) do not distort the incident distribution of x-ray photons in the spatial or energy domains, and (3) do not decrease the frequency-dependent SNR of the incident distribution of x-ray quanta. We derive analytic expressions for the noise power spectrum (NPS) of such ideal detectors for detection and quantification tasks. We tabulate the NPS of ideal SXDs for RQA x-ray spectra for detection and quantification of aluminum, PMMA, iodine, and gadolinium basis materials. Our analysis shows that a single matrix determines the noise power of ideal SXDs in detection and quantification tasks, including basis material decomposition and line-integral estimation for pseudo-mono-energetic imaging. This NPS matrix is determined by the x-ray spectrum incident on the detector and the mass-attenuation coefficients of the set of basis materials. Combining existing tabulated values of the mass-attenuation coefficients of basis materials with standardized RQA x-ray spectra enabled tabulating numeric values of the NPS matrix for selected spectra and tasks. The numeric values and mathematical expressions of the NPS of ideal SXDs reported here can be used to normalize measurements of the frequency-dependent SNR of SXDs for experimental study of the task-dependent DQE.