Julia Tinland, Oussama Haddadi, Marie-Pierre Aimée Dann
Abstract Background The diagnosis of chronic lymphocytic leukaemia (CLL) is often fortuitous. Stage A patients do not experience severe symptoms and therefore do not require initial treatment. Instead, they are placed under active surveillance in order to track disease deterioration over time. Around half of these patients will never need treatment. Diagnostic disclosure of Stage A CLL thus leads to a great deal of uncertainty. Accordingly, stage A patients’ lives are sometimes more impacted by their diagnosis than by the disease itself. This study aimed to outline the ethical issues surrounding the diagnosis of potentially indolent CLL in stage A patients under active surveillance in France by investigating their experiences, needs, expectations and self-perceptions. Methods We co-constructed an online questionnaire with the French leukaemia and lymphomas patient association ELLyE. Comprising 46 closed, semi-closed and semi-open questions, it focused on stage A patients’ experiences regarding their diagnosis and active surveillance, their encounters with medical professionals, as well as the support they received from family members, friends, non-medical healthcare professionals, and patient associations. We conducted a descriptive analysis to examine the results. Results Of the 235 participants included in the study, 46% were women. Mean age was 66 years old. All had been diagnosed between 1982 and 2021 (median year of diagnosis was 2013). Almost three-quarters (72.8%) of the sample did not consider themselves as sick. Two-thirds (66.8%) reported feeling uncertain about their disease progression and the feeling of a constant threat hanging over them. Patients recently diagnosed (i.e., < 5 years) were more likely to experience anxiety, anguish and powerlessness related to this uncertainty than patients diagnosed more than ten years previously. Many participants did not wish for more frequent or longer follow-up consultations with hospital-based specialists. Moreover, some wished to distance themselves as much as possible from anything related to cancer care and to live their lives as normally as possible. Conclusions Our study highlights that the difficulties patients with stage A CLL encounter tend to remain invisible because of limited interactions with medical professionals. Greater acknowledgment and understanding of these difficulties by these professionals are needed in order to adequately address different needs.
İstanbul’un manevi fatihi olarak bilinen Akşemseddin (ö. 863/1459), tasavvufi kimliğinin yanı sıra hekimliği ile de oldukça meşhur olmuştur. O, tasavvuf alanında yazmış olduğu Risâletü’n-Nûriyye, Makāmât-ı Evliyâ ve Defʿu Meṭâʿini’ṣ-Ṣûfiyye isimli eserlerinin dışında tıp alanında da günümüzde hâlâ üzerinde çalışılması gereken Mâddetü’l-Hayât isimli eseri kaleme almıştır. Eserinde hastalığın ırsiliği, kanser gibi konulara değinmiş ayrıca mikrop ve bulaşma konusunda kesin bilgiler veren İtalyan hekim Fracastor’dan (ö.1553) en az bir asır önce mikroptan bahsetmiştir. Akşemseddin, gördüğü bir rüya üzerine eseriyle aynı adı taşıyan mâddetü’l-hayât ilacının diğer adıyla mübarek sıvının da her türlü hastalığa iyi geleceğini söylemiştir.
Yapılan araştırmalar sonucunda Akşemseddin’in her derde şifa olduğunu söylediği mâddetü’l-hayât isimli ilacın içeriğinde yer alan yakut, cıva ve kükürdün, geçmişte de birçok hekim tarafından kullanıldığı tespit edilmiştir. Bu hekimlerden birisi Akşemseddin’den dört asır önce yaşayan ve eş-Şifâ, en-Necât, Uyûnü’l-Ḥikme gibi musiki, felsefe, tıp alanlarında birçok önemli eseri kaleme alan İbn Sînâ’dır. Onun tıp alanında yazmış olduğu el-Kanun fi’t-Tıb isimli eseri ise yaşadığı döneme damgasını vurmuş, Batı’da yıllarca ders kitabı olarak okutulmuştur. İbn Sînâ tıp tarihinde önemli bir yeri bulunan bu eserinde Akşemseddin gibi her derde şifa olacak bir ilaçtan bahsetmemiştir. Ancak tedavilere dair vermiş olduğu reçetelerde yakut, cıva ve özelikle de kükürdü çok sık kullanmıştır. Bu çalışma, İbn Sînâ’nın el-Kanun fi’t-tıb isimli eserinin içeriği ile her derde şifa olduğu söylenen mâddetü’l-hayât’ın içeriğinin karşılaştırılarak kullanıldıkları yerlerdeki benzerliklerinin ve farklılıklarının tespit edilmesi amacıyla kaleme alınmıştır.
Eline W. Dalmijn, Merel A. Visse, Inge van Nistelrooij
Introduction: Unintended pregnancies are a worldwide health issue, faced each year by one in 16 people, and experienced in various ways. In this study we focus on unintended pregnancies that are, at some point, experienced as unwanted because they present the pregnant person with a decision to continue or terminate the pregnancy. The aim of this study is to learn more about the decision-making process, as there is a lack of insights into how people with an unintended pregnancy reach a decision. This is caused by 1) assumptions of rationality in reproductive autonomy and decision-making, 2) the focus on pregnancy outcomes, e.g. decision-certainty and reasons and, 3) the focus on abortion in existing research, excluding 40% of people with an unintended pregnancy who continue the pregnancy. Method: We conducted a narrative literature review to examine what is known about the decision-making process and aim to provide a deeper understanding of how persons with unintended pregnancy come to a decision.Results: Our analysis demonstrates that the decision-making process regarding unintended pregnancy consists of navigating entangled layers, rather than weighing separable elements or factors. The layers that are navigated are both internal and external to the person, in which a ‘sense of knowing’ is essential in the decision-making process. Conclusion: The layers involved and complexity of the decision-making regarding unintended pregnancy show that a rational decision-making frame is inadequate and a more holistic frame is needed to capture this dynamic and personal experience.
As a rule, physicians’ reputation significantly influences public confidence in the medical profession. Unfortunately, the societal perception of physicians in contemporary Iran appears to be negatively impacted. Therefore, the present study aimed to analyze and elucidate the fundamental causes of this phenomenon.This qualitative study employed content analysis of semi-structured interviews conducted in 2022. The study population consisted of 6 physicians, 6 nurses and 12 patients in the the affiliated hospitals in Kerman University of Medical Sciences selected through purposive sampling. Extraction of the main themes followed the Graneheim and Lundman approach, and data management was facilitated through MAXQDA 20. The study identified five themes encapsulating the causes for damage to physicians’ reputation: physicians' relationship with patients, physicians' relationship with the community, physicians' relationship with the medical profession, challenges within medical practice, and challenges related to medical education. Within these themes, a total of 38 subthemes emerged.The primary drivers that seem to damage physicians’ reputation include: non-effective communication, negative public attitudes toward certain physicians and medical centers due to malpractice, illegitimate relationships of physicians, gaps in physicians’ skills, insufficient education, and ethical lapses.It was concluded that several infrastructural elements negatively impact physicians' reputation. Consequently, it is recommended to monitor the professional behaviors, practices and relationships of physicians, while scrutinizing the medical education system.
History of medicine. Medical expeditions, Medical philosophy. Medical ethics
Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research has primarily focused on 2D medical images, leaving 3D images under-explored, despite their richer spatial information. This paper aims to advance 3D medical image analysis with MLLMs. To this end, we present a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs specifically tailored for various 3D medical tasks, such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis. Furthermore, we introduce a new 3D multi-modal medical benchmark, M3D-Bench, which facilitates automatic evaluation across eight tasks. Through comprehensive evaluation, our method proves to be a robust model for 3D medical image analysis, outperforming existing solutions. All code, data, and models are publicly available at: https://github.com/BAAI-DCAI/M3D.
Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays an important role in rare disease recognition and health screening in the medical domain. Despite the emergence of numerous methods for medical AD, the lack of a fair and comprehensive evaluation causes ambiguous conclusions and hinders the development of this field. To address this problem, this paper builds a benchmark with unified comparison. Seven medical datasets with five image modalities, including chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and histopathology images, are curated for extensive evaluation. Thirty typical AD methods, including reconstruction and self-supervised learning-based methods, are involved in comparison of image-level anomaly classification and pixel-level anomaly segmentation. Furthermore, for the first time, we systematically investigate the effect of key components in existing methods, revealing unresolved challenges and potential future directions. The datasets and code are available at https://github.com/caiyu6666/MedIAnomaly.
Image-to-image translation is a common task in computer vision and has been rapidly increasing the impact on the field of medical imaging. Deep learning-based methods that employ conditional generative adversarial networks (cGANs), such as Pix2PixGAN, have been extensively explored to perform image-to-image translation tasks. However, when noisy medical image data are considered, such methods cannot be directly applied to produce clean images. Recently, an augmented GAN architecture named AmbientGAN has been proposed that can be trained on noisy measurement data to synthesize high-quality clean medical images. Inspired by AmbientGAN, in this work, we propose a new cGAN architecture, Ambient-Pix2PixGAN, for performing medical image-to-image translation tasks by use of noisy measurement data. Numerical studies that consider MRI-to-PET translation are conducted. Both traditional image quality metrics and task-based image quality metrics are employed to assess the proposed Ambient-Pix2PixGAN. It is demonstrated that our proposed Ambient-Pix2PixGAN can be successfully trained on noisy measurement data to produce high-quality translated images in target imaging modality.
There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world clinical applications where LLMs are being deployed. Furthermore, existing studies on evaluating long-form answer generation in medical QA are primarily closed-source, lacking access to human medical expert annotations, which makes it difficult to reproduce results and enhance existing baselines. In this work, we introduce a new publicly available benchmark featuring real-world consumer medical questions with long-form answer evaluations annotated by medical doctors. We performed pairwise comparisons of responses from various open and closed-source medical and general-purpose LLMs based on criteria such as correctness, helpfulness, harmfulness, and bias. Additionally, we performed a comprehensive LLM-as-a-judge analysis to study the alignment between human judgments and LLMs. Our preliminary results highlight the strong potential of open LLMs in medical QA compared to leading closed models. Code & Data: https://github.com/lavita-ai/medical-eval-sphere
G. Constantin, Crisanta-Alina Mazilescu, Teodora Hoinoiu
et al.
This study investigated the attitude of Romanian medical students and doctors toward business ethics by measuring the preference for a particular ethical philosophy, namely, the preference for Machiavellianism, moral objectivism, social Darwinism, ethical relativism, and legalism. At the same time, this study aimed to explore the influence of sex, age, and ethics education on the attitude toward business ethics. The data collection was performed using a voluntary self-administered online survey including the Attitudes Toward Business Ethics Questionnaire (ATBEQ) instrument. Our findings show that the values based on which Romanian medical students and doctors make business decisions belong predominantly to the moral objectivism philosophy, which is grounded on rational actions based on a set of objective moral standards.
From 1963 until 1969, Reginald (Reg) Perkin, a physician, presented a series of lectures on medical ethics to fourth-year medical students. His approach to both the teaching of medical ethics and his understanding of what constitutes medical ethics are revealed through an interview with him and the notes he used for his lectures. This paper provides an example of a medical ethics course taught prior to the 1970s development of the discipline of bioethics. It argues that there is significant alignment with later medical ethics curricula structured around questions posed by Martin McKneally and Peter Singer in 2001.
Over the years, the Invariant Scattering Transform (IST) technique has become popular for medical image analysis, including using wavelet transform computation using Convolutional Neural Networks (CNN) to capture patterns' scale and orientation in the input signal. IST aims to be invariant to transformations that are common in medical images, such as translation, rotation, scaling, and deformation, used to improve the performance in medical imaging applications such as segmentation, classification, and registration, which can be integrated into machine learning algorithms for disease detection, diagnosis, and treatment planning. Additionally, combining IST with deep learning approaches has the potential to leverage their strengths and enhance medical image analysis outcomes. This study provides an overview of IST in medical imaging by considering the types of IST, their application, limitations, and potential scopes for future researchers and practitioners.
Tasmia Tahmida Jidney, Angona Biswas, MD Abdullah Al Nasim
et al.
The integration of machine learning in medical image analysis can greatly enhance the quality of healthcare provided by physicians. The combination of human expertise and computerized systems can result in improved diagnostic accuracy. An automated machine learning approach simplifies the creation of custom image recognition models by utilizing neural architecture search and transfer learning techniques. Medical imaging techniques are used to non-invasively create images of internal organs and body parts for diagnostic and procedural purposes. This article aims to highlight the potential applications, strategies, and techniques of AutoML in medical imaging through theoretical and empirical evidence.
Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called DiffBoost. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints and preserve the underlying structure of imaging data. Due to the random sampling process by the diffusion model, we can generate an arbitrary number of synthetic images with diverse appearances. To validate the effectiveness of our proposed method, we conduct an extensive set of medical image segmentation experiments on multiple datasets, including Ultrasound breast (+13.87%), CT spleen (+0.38%), and MRI prostate (+7.78%), achieving significant improvements over the baseline segmentation methods. The promising results demonstrate the effectiveness of our \textcolor{black}{DiffBoost} for medical image segmentation tasks and show the feasibility of introducing a first-ever text-guided diffusion model for general medical image segmentation tasks. With carefully designed ablation experiments, we investigate the influence of various data augmentations, hyper-parameter settings, patch size for generating random merging mask settings, and combined influence with different network architectures. Source code are available at https://github.com/NUBagciLab/DiffBoost.
Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising results across various tasks due to its generalizability and interpretability. The use of CLIP has recently gained increasing interest in the medical imaging domain, serving as a pre-training paradigm for image-text alignment, or a critical component in diverse clinical tasks. With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. In this paper, we (1) first start with a brief introduction to the fundamentals of CLIP methodology; (2) then investigate the adaptation of CLIP pre-training in the medical imaging domain, focusing on how to optimize CLIP given characteristics of medical images and reports; (3) further explore practical utilization of CLIP pre-trained models in various tasks, including classification, dense prediction, and cross-modal tasks; and (4) finally discuss existing limitations of CLIP in the context of medical imaging, and propose forward-looking directions to address the demands of medical imaging domain. Studies featuring technical and practical value are both investigated. We expect this survey will provide researchers with a holistic understanding of the CLIP paradigm and its potential implications. The project page of this survey can also be found on https://github.com/zhaozh10/Awesome-CLIP-in-Medical-Imaging.
En este artículo presentaré la importancia de revisar críticamente los términos usuales utilizados para denominar al vínculo que se establece entre quien gesta para otras personas y quienes requieren esa gestación para tener un bebé, con la intención de proponer un significante, a mi entender, más adecuado: relación de alquiler/sustitución del embarazo. Para ello, en primer lugar, aludiré sucintamente a cómo el uso del lenguaje en reproducción asistida no es neutral. En segundo lugar, examinaré las nomenclaturas más utilizadas en lengua castellana como por ejemplo “alquiler de vientre” y “gestación por sustitución”, entre otras, y las analizaré a través de dos criterios: el rol de la persona gestante y el tipo de relación entre quien gesta y quien/es quieren ser madre/s y/o padre/s Por último, utilizaré ambos criterios para proponer el nuevo significante.
Jurisprudence. Philosophy and theory of law, Medical philosophy. Medical ethics
Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored. Furthermore, systems in practice rely on standard video codecs, which naively allocate bits between medically relevant frames or parts of frames. In this work, we present an empirical study of some deficiencies of classical codecs on gastroenterology videos, and motivate our ongoing work to train a learned compression model for colonoscopy videos. We show that two of the most common classical codecs, H264 and HEVC, compress medically relevant frames statistically significantly worse than medically nonrelevant ones, and that polyp detector performance degrades rapidly as compression increases. We explain how a learned compressor could allocate bits to important regions and allow detection performance to degrade more gracefully. Many of our proposed techniques generalize to medical video domains beyond gastroenterology
Fahad Shamshad, Salman Khan, Syed Waqas Zamir
et al.
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at \url{https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging}.
Although recent trends in politics and media make it appear that conspiracy theories are on the rise, in fact they have always been present, probably because they are sustained by natural dispositions of the human brain. This is also the case with medical conspiracy theories. This article reviews some of the most notorious health-related conspiracy theories. It then approaches the reasons why people believe these theories, using concepts from cognitive science. On the basis of that knowledge, the article makes normative proposals for public health officials and health workers as a whole, to deal with conspiracy theories, in order to preserve some of the fundamental principles of medical ethics.
Amal G. Al-Bakri, Amal A. Akour, Wael K. Al-Delaimy
Abstract Background Fecal microbiota transplant (FMT) is a treatment modality that involves the introduction of stool from a healthy pre-screened donor into the gastrointestinal tract of a patient. It exerts its therapeutic effects by remodeling the gut microbiota and treating microbial dysbiosis-imbalance. FMT is not regulated in Jordan, and regulatory effort for FMT therapy in Jordan, an Islamic conservative country, might be faced with unique cultural, social, religious, and ethical challenges. We aimed to assess knowledge, attitudes, and perceptions of ethical and social issues of FMT use among Jordanian healthcare professionals. Methods An observational, cross-sectional study design was used to assess knowledge, attitudes, and perceptions of ethical and social issues of FMT among 300 Jordanian healthcare professionals. Results A large proportion (39 %) thought that the safety and efficacy of this technique are limited and 29.3 % thought there is no evidence to support its use. Almost all (95 %) responded that they would only perform it in certain cases, if ethically justified, and 48.3 % would use it due to treatment failure of other approaches. When reporting about reasons for not using it, 40 % reported that they would not perform it due to concerns about medical litigation, fear of infections (38 %), and lack of knowledge of long safety and efficacy (31.3 %). Interestingly, all practitioners said they would perform this procedure through the lower rather than upper gastrointestinal tract modality and the majority will protect the patient’s confidentiality via double-blinding (43.3 %). For a subset of participants (n = 100), the cultural constraints that might affect the choice of performing FMT were mainly due to donor’s religion, followed by dietary intake, and alcohol consumption. Conclusions Our healthcare practitioners are generally reluctant to use the FMT modality due to religious and ethical reasons but would consider it if there was a failure of other treatment and after taking into consideration many legislative, social, ethical and practice-based challenges including safety, efficacy and absence of guidelines.