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Visual spatial intelligence is critical for medical image interpretation, yet remains largely unexplored in Multimodal Large Language Models (MLLMs) for 3D imaging. This gap persists due to a systemic lack of datasets featuring structured 3D spatial annotations beyond basic labels. In this study, we introduce an agentic pipeline that autonomously synthesizes spatial visual question-answering (VQA) data by orchestrating computational tools such as volume and distance calculators with multi-agent collaboration and expert radiologist validation. We present SpatialMed, the first comprehensive benchmark for evaluating 3D spatial intelligence in medical MLLMs, comprising nearly 10K question-answer pairs across multiple organs and tumor types. Our evaluations on 14 state-of-the-art MLLMs and extensive analyses reveal that current models lack robust spatial reasoning capabilities for medical imaging.
Este artículo ofrece un análisis crítico de la propuesta de Reglamento europeo sobre vegetales obtenidos mediante nuevas técnicas genómicas (NTG). Desde una perspectiva jurídico-administrativa y bioética, se examinan los cambios introducidos en la regulación del riesgo, el tratamiento del principio de precaución y las cuestiones éticas asociadas, articuladas en torno a los principios de beneficencia, no maleficencia, autonomía y justicia. El texto defiende un enfoque normativo equilibrado, que incorpore el conocimiento científico sin renunciar a salvaguardas institucionales, la transparencia y la protección de los grupos considerados vulnerables en este contexto.
Jurisprudence. Philosophy and theory of law, Medical philosophy. Medical ethics
Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning the Segment Anything Model (SAM) 3 architecture on medical images paired with semantic conceptual labels, our MedSAM-3 enables medical Promptable Concept Segmentation (PCS), allowing precise targeting of anatomical structures via open-vocabulary text descriptions rather than solely geometric prompts. We further introduce the MedSAM-3 Agent, a framework that integrates Multimodal Large Language Models (MLLMs) to perform complex reasoning and iterative refinement in an agent-in-the-loop workflow. Comprehensive experiments across diverse medical imaging modalities, including X-ray, MRI, Ultrasound, CT, and video, demonstrate that our approach significantly outperforms existing specialist and foundation models. We will release our code and model at https://github.com/Joey-S-Liu/MedSAM3.
Localized image captioning has made significant progress with models like the Describe Anything Model (DAM), which can generate detailed region-specific descriptions without explicit region-text supervision. However, such capabilities have yet to be widely applied to specialized domains like medical imaging, where diagnostic interpretation relies on subtle regional findings rather than global understanding. To mitigate this gap, we propose MedDAM, the first comprehensive framework leveraging large vision-language models for region-specific captioning in medical images. MedDAM employs medical expert-designed prompts tailored to specific imaging modalities and establishes a robust evaluation benchmark comprising a customized assessment protocol, data pre-processing pipeline, and specialized QA template library. This benchmark evaluates both MedDAM and other adaptable large vision-language models, focusing on clinical factuality through attribute-level verification tasks, thereby circumventing the absence of ground-truth region-caption pairs in medical datasets. Extensive experiments on the VinDr-CXR, LIDC-IDRI, and SkinCon datasets demonstrate MedDAM's superiority over leading peers (including GPT-4o, Claude 3.7 Sonnet, LLaMA-3.2 Vision, Qwen2.5-VL, GPT-4Rol, and OMG-LLaVA) in the task, revealing the importance of region-level semantic alignment in medical image understanding and establishing MedDAM as a promising foundation for clinical vision-language integration.
Based on in-depth interviews with infertile women, this paper examines the identity construction, social support, and assisted reproductive treatment experience under the theoretical lens of "illness narratives". By comparing situations in which infertility is caused by female factors versus male factors, the findings show that when infertility is attributed primarily to women, they tend to exhibit negative identity construction, low self-recognition, and heightened anxiety during treatment. In contrast, when infertility is primarily due to male factors, women display greater bodily confidence and share reproductive responsibility jointly with their husbands. In these two different situations, the social support that women receive also varies, leading to different treatment experiences. Although these differences appear to be driven by the biological cause of infertility, they are in fact deeply shaped by broader socio-cultural influences. This paper puts forward corresponding policy suggestions in order to promote shared responsibility among infertile couples, and to advance gender equality.
The inclusion of human sex and gender data in statistical analysis invokes multiple considerations for data collection, combination, analysis, and interpretation. These considerations are not unique to variables representing sex and gender. However, considering the relevance of the ethical practice standards for statistics and data science to sex and gender variables is timely, with results that can be applied to other sociocultural variables. Historically, human gender and sex have been categorized with a binary system. This tradition persists mainly because it is easy, and not because it produces the best scientific information. Binary classification simplifies combinations of older and newer data sets. However, this classification system eliminates the ability for respondents to articulate their gender identity, conflates gender and sex, and also obscures potentially important differences by collapsing across valid and authentic categories. This approach perpetuates historical inaccuracy, simplicity, and bias, while also limiting the information that emerges from analyses of human data. The approach also violates multiple elements in the American Statistical Association (ASA) Ethical Guidelines for Statistical Practice. Information that would be captured with a nonbinary classification could be relevant to decisions about analysis methods and to decisions based on otherwise expert statistical work. Statistical practitioners are increasingly concerned with inconsistent, uninformative, and even unethical data collection and analysis practices. This paper presents a historical introduction to the collection and analysis of human gender and sex data, offers a critique of a few common survey questioning methods based on alignment with the ASA Ethical Guidelines, and considers the scope of ethical considerations for human gender and sex data from design through analysis and interpretation.
Nagur Shareef Shaik, Teja Krishna Cherukuri, Dong Hye Ye
Automated retinal image medical description generation is crucial for streamlining medical diagnosis and treatment planning. Existing challenges include the reliance on learned retinal image representations, difficulties in handling multiple imaging modalities, and the lack of clinical context in visual representations. Addressing these issues, we propose the Multi-Modal Medical Transformer (M3T), a novel deep learning architecture that integrates visual representations with diagnostic keywords. Unlike previous studies focusing on specific aspects, our approach efficiently learns contextual information and semantics from both modalities, enabling the generation of precise and coherent medical descriptions for retinal images. Experimental studies on the DeepEyeNet dataset validate the success of M3T in meeting ophthalmologists' standards, demonstrating a substantial 13.5% improvement in BLEU@4 over the best-performing baseline model.
Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the complex and critical nature of medical information. This paper provides a comprehensive overview of the landscape of medical LLM evaluation, synthesizing insights from existing studies and highlighting evaluation data sources, task scenarios, and evaluation methods. Additionally, it identifies key challenges and opportunities in medical LLM evaluation, emphasizing the need for continued research and innovation to ensure the responsible integration of LLMs into clinical practice.
Matthieu Le Dorze, Romain Barthélémy, Olivier Lesieur
et al.
Abstract Background The development of controlled donation after circulatory death (cDCD) is both important and challenging. The tension between end-of-life care and organ donation raises significant ethical issues for healthcare professionals in the intensive care unit (ICU). The aim of this prospective, multicenter, observational study is to better understand ICU physicians’ and nurses’ experiences with cDCD. Methods In 32 ICUs in France, ICU physicians and nurses were invited to complete a questionnaire after the death of end-of-life ICU patients identified as potential cDCD donors who had either experienced the withdrawal of life-sustaining therapies alone or with planned organ donation (OD(-) and OD( +) groups). The primary objective was to assess their anxiety (State Anxiety Inventory STAI Y-A) following the death of a potential cDCD donor. Secondary objectives were to explore potential tensions experienced between end-of-life care and organ donation. Results Two hundred six ICU healthcare professionals (79 physicians and 127 nurses) were included in the course of 79 potential cDCD donor situations. STAI Y-A did not differ between the OD(-) and OD( +) groups for either physicians or nurses (STAI Y-A were 34 (27–38) in OD(-) vs. 32 (27–40) in OD( +), p = 0.911, for physicians and 32 (25–37) in OD(-) vs. 39 (26–37) in OD( +), p = 0.875, for nurses). The possibility of organ donation was a factor influencing the WLST decision for nurses only, and a factor influencing the WLST implementation for both nurses and physicians. cDCD experience is perceived positively by ICU healthcare professionals overall. Conclusions cDCD does not increase anxiety in ICU healthcare professionals compared to other situations of WLST. WLST and cDCD procedures could further be improved by supporting professionals in making their intentions clear between end-of-life support and the success of organ donation, and when needed, by enhancing communication between ICU physician and nurses. Trial registration This research was registered in ClinicalTrials.gov (Identifier: NCT05041023, September 10, 2021).
Abstract Background/Objective The act of surgery involves harming vulnerable patients with the intent that the results will improve their health and, ultimately, help the patients. Such activities will inevitably entail moral decisions, yet the ethics of surgery has only recently developed as a field of medical ethics. Within this field, it is striking how few accounts there are of actions within the operating room. The aim of this systematic review was to investigate how much of the scientific publications on surgical ethics focus on what take place inside the operating room and to explore the ethical issues included in the publications that focus on medical ethics in the operating room. Methods We conducted a systematic search of the Medline and Embase databases using a PICO model and the search terms “surgery”, “ethics” and “operating room”. Papers were included if they focused on doctors, entailed activities inside the operating room and contained some ethical analysis. Thematic synthesis was used for data extraction and analysis. Findings Fewer than 2% of the scientific publications on surgical ethics included activities inside the operating room. A total of 108 studies were included in the full-text analysis and reported according to the RESERVE guidelines. Eight content areas covered 2/3 of the included papers: DNR orders in the OR, overlapping surgery, donation of organs, broadcasting live surgery, video recordings in the OR, communication/teamwork, implementing new surgical technology, and denying blood to Jehovah’s Witness. Discussion/Conclusions This systematic review indicates that only a small fraction of scientific publications on the ethics of surgery focus on issues inside the operating room, accentuating the need for further research to close this gap. The ethical issues that repeatedly arose in the included papers included the meaning of patient autonomy inside the operating room, the consequences of technological advances in surgery, the balancing of legitimate interests, the dehumanising potential of the OR, and the strong notion of surgeon responsibility.
Purpose: Education in ancient India is way back in the 3rd century BC it is a source of knowledge, traditions, and practices focused on the holistic development provided by the ancient university in higher learnings provided by the Nalanda(5th century), Takshashila (6th century BC), Odantapuri (550-1040), Jagaddala, Sharada peeth Valabhi, Varanasi, Manyakheta in Karnataka, Kanchipuram, Nagarjinakonda focused on Moral, Physical, spiritual, intellectual through Vedas, Brahmanas, Upanishads, Dharmasutras the learning sources are Kavyas, Itihas, Anviksiki (logic), Arthashastra, Mimamsa, VArta (trade), Krida, ShastrArtha, Uyayamaprakara, Dhanurvidya, Yogasadhana, music, the system of ancient education was Vedic and Buddhist with the language of Sanskrit and Pali, Produced academic Scholars Panini well-known grammarian, Charaka medical teacher, Chanakya, Jivaka and Swami Vivekananda Ramakrishna Mission in the twentieth century are the hub of learning. The National Education Policy 2020 is the framework of the Indian Knowledge system to provide innovative developments through multidisciplinary linkages with other branches of knowledge contributed by Aryabhatta mathematician, astrologer and physicist he wrote the book Aryabhattiya (summary of Mathematics), Bramagupta book Brahm Sputa Siddantika on mathematical, Ganesha Upadhaya mathematician and philosopher, medical and Ayurveda by Susruta, Patanjali on Yoga and Vagbhata, The education agencies ancient days are Gurukula, Parishad and Samnelan, teaching methods are verbal and explanatory, lectures, debates and discussions to creating the Three R’s Religion, Resilience, and Responsibility. Design/Methodology/Approach: The Article is descriptive and based on reviews of the literature. Findings: The Ancient university is the embodiment of India Knowledge through Multidisciplinary approaches like philosophy, music, Ayurveda, and Warfare Skills and focuses more on moral values, ethics, and Spiritualism ancient universities and scholars are a gold mine for shaping and improving higher learning and imparting vocational training in all branches. Originality/Value: Present literature review-based study focuses on ancient university subjects’ thought and the contribution of ancient scholar/scientists in different fields of the Indian Knowledge system present NEP2020 draws contribution made to IKS and incorporate a multidisciplinary approach to gain knowledge, culture, and skills to enhance Indian glory. Paper Type: Literature Review-based Analysis
Farnaz Khun Jush, Tuan Truong, Steffen Vogler
et al.
A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial in advancing medical research, enabling large-scale studies and innovative diagnostic tools. Thus, addressing the challenges of medical image retrieval is essential for the continued enhancement of healthcare and research. In this study, we evaluated the feasibility of employing four state-of-the-art pretrained models for medical image retrieval at modality, body region, and organ levels and compared the results of two similarity indexing approaches. Since the employed networks take 2D images, we analyzed the impacts of weighting and sampling strategies to incorporate 3D information during retrieval of 3D volumes. We showed that medical image retrieval is feasible using pretrained networks without any additional training or fine-tuning steps. Using pretrained embeddings, we achieved a recall of 1 for various tasks at modality, body region, and organ level.
Medicine, by its nature, is a multifaceted domain that requires the synthesis of information across various modalities. Medical generative vision-language models (VLMs) make a first step in this direction and promise many exciting clinical applications. However, existing models typically have to be fine-tuned on sizeable down-stream datasets, which poses a significant limitation as in many medical applications data is scarce, necessitating models that are capable of learning from few examples in real-time. Here we propose Med-Flamingo, a multimodal few-shot learner adapted to the medical domain. Based on OpenFlamingo-9B, we continue pre-training on paired and interleaved medical image-text data from publications and textbooks. Med-Flamingo unlocks few-shot generative medical visual question answering (VQA) abilities, which we evaluate on several datasets including a novel challenging open-ended VQA dataset of visual USMLE-style problems. Furthermore, we conduct the first human evaluation for generative medical VQA where physicians review the problems and blinded generations in an interactive app. Med-Flamingo improves performance in generative medical VQA by up to 20\% in clinician's rating and firstly enables multimodal medical few-shot adaptations, such as rationale generation. We release our model, code, and evaluation app under https://github.com/snap-stanford/med-flamingo.
Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most critical regions of an image used in a deep learning model's decision-making process, increasing interpretability and trust in the results. It is applied in many computer vision (CV) tasks such as classification and explanation. This study explores the principles of Explainable Deep Learning and its relevance to medical imaging, discusses various explainability techniques and their limitations, and examines medical imaging applications of Grad-CAM. The findings highlight the potential of Explainable Deep Learning and Grad-CAM in improving the accuracy and interpretability of deep learning models in medical imaging. The code is available in (will be available).
Remote medical diagnosis has emerged as a critical and indispensable technique in practical medical systems, where medical data are required to be efficiently compressed and transmitted for diagnosis by either professional doctors or intelligent diagnosis devices. In this process, a large amount of redundant content irrelevant to the diagnosis is subjected to high-fidelity coding, leading to unnecessary transmission costs. To mitigate this, we propose diagnosis-oriented medical image compression, a special semantic compression task designed for medical scenarios, targeting to reduce the compression cost without compromising the diagnosis accuracy. However, collecting sufficient medical data to optimize such a compression system is significantly expensive and challenging due to privacy issues and the lack of professional annotation. In this study, we propose DMIC, the first efficient transfer learning-based codec, for diagnosis-oriented medical image compression, which can be effectively optimized with only few-shot annotated medical examples, by reusing the knowledge in the existing reinforcement learning-based task-driven semantic coding framework, i.e., HRLVSC [1]. Concretely, we focus on tuning only the partial parameters of the policy network for bit allocation within HRLVSC, which enables it to adapt to the medical images. In this work, we validate our DMIC with the typical medical task, Coronary Artery Segmentation. Extensive experiments have demonstrated that our DMIC can achieve 47.594%BD-Rate savings compared to the HEVC anchor, by tuning only the A2C module (2.7% parameters) of the policy network with only 1 medical sample.
Katherine Evans, Nelson de Moura, Raja Chatila
et al.
The ethics of automated vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. After a discussion about the pertinence and cogency of the term 'artificial moral agent' to describe AVs that would accomplish these sorts of decisions, and starting from the assumption that human harm is unavoidable in some situations, a strategy for AV decision making is proposed using only pre-defined parameters to characterize the risk of possible accidents and also integrating the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation, into multiple possible decision rules to determine the most suitable action given the specific environment and decision context. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of human 'moral positions' concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an automated vehicle's decision making.
Resumo A medicina personalizada surgiu como uma abordagem promissora para fornecer tratamentos exclusivos e personalizados para doenças usando ferramentas genômicas. No campo dos estudos do envelhecimento, a medicina personalizada tem grande potencial para transformar o tratamento e a prevenção de doenças associadas à idade e relacionadas à nutrigenômica e à farmacogenômica. No entanto, o uso de dados genômicos na medicina personalizada levanta preocupações bioéticas significativas, incluindo questões como privacidade, consentimento, equidade e potencial uso indevido de dados genômicos para fins discriminatórios. Portanto, é crucial considerar cuidadosamente os aspectos biomédicos, sociais e éticos da medicina personalizada no contexto de condições relacionadas à idade. Esta revisão tem o objetivo de explorar os principais aspectos da medicina personalizada concernentes a doenças relacionadas à idade nos dados farmacogenômicos e nutrigenômicos, abordando as preocupações bioéticas envolvidas no uso desses dados.