The last decade has witnessed significant advances in energy harvesting technologies as a possible alternative to provide a continuous power supply for small, low-power devices in applications, such as wireless sensing, data transmission, actuation, and medical implants. Piezoelectric energy harvesting (PEH) has been a salient topic in the literature and has attracted widespread attention from researchers due to its advantages of simple architecture, high power density, and good scalability. This paper presents a comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Various key aspects to improve the overall performance of a PEH device are discussed, including basic fundamentals and configurations, materials and fabrication, performance enhancement mechanisms, applications, and future outlooks.The last decade has witnessed significant advances in energy harvesting technologies as a possible alternative to provide a continuous power supply for small, low-power devices in applications, such as wireless sensing, data transmission, actuation, and medical implants. Piezoelectric energy harvesting (PEH) has been a salient topic in the literature and has attracted widespread attention from researchers due to its advantages of simple architecture, high power density, and good scalability. This paper presents a comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Various key aspects to improve the overall performance of a PEH device are discussed, including basic fundamentals and configurations, materials and fabrication, performance enhancement mechanisms, applications, and future outlooks.
K. Paranjape, M. Schinkel, R. N. Nannan Panday
et al.
Health care is evolving and with it the need to reform medical education. As the practice of medicine enters the age of artificial intelligence (AI), the use of data to improve clinical decision making will grow, pushing the need for skillful medicine-machine interaction. As the rate of medical knowledge grows, technologies such as AI are needed to enable health care professionals to effectively use this knowledge to practice medicine. Medical professionals need to be adequately trained in this new technology, its advantages to improve cost, quality, and access to health care, and its shortfalls such as transparency and liability. AI needs to be seamlessly integrated across different aspects of the curriculum. In this paper, we have addressed the state of medical education at present and have recommended a framework on how to evolve the medical education curriculum to include AI.
Abstract Blockchain is an emerging technology being applied for creating innovative solutions in various sectors, including healthcare. A Blockchain network is used in the healthcare system to preserve and exchange patient data through hospitals, diagnostic laboratories, pharmacy firms, and physicians. Blockchain applications can accurately identify severe mistakes and even dangerous ones in the medical field. Thus, it can improve the performance, security, and transparency of sharing medical data in the health care system. This technology is helpful to medical institutions to gain insight and enhance the analysis of medical records. In this paper, we studied Blockchain technology and its significant benefits in healthcare. Various Capabilities, Enablers, and Unified Work-Flow Process of Blockchain Technology to support healthcare globally are discussed diagrammatically. Finally, the paper identifies and debates fourteen significant applications of Blockchain for healthcare. Blockchain plays a decisive part in handling deception in clinical trials; here, the potential of this technology offer is to improve data efficiency for healthcare. It can help avoid the fear of data manipulation in healthcare and supports a unique data storage pattern at the highest level of security. It provides versatility, interconnection, accountability, and authentication for data access. For different purposes, health records must be kept safe and confidential. Blockchain helps for the decentralised protection of data in healthcare and avoids specific threats.
Medical AI has transformed modern medicine and created a new environment for future doctors. However, medical education has failed to keep pace with these advances, and it is essential to provide systematic education on medical AI to current medical undergraduate and postgraduate students. To address this issue, our study utilized the Unified Theory of Acceptance and Use of Technology model to identify key factors that influence the acceptance and intention to use medical AI. We collected data from 1,243 undergraduate and postgraduate students from 13 universities and 33 hospitals, and 54.3% reported prior experience using medical AI. Our findings indicated that medical postgraduate students have a higher level of awareness in using medical AI than undergraduate students. The intention to use medical AI is positively associated with factors such as performance expectancy, habit, hedonic motivation, and trust. Therefore, future medical education should prioritize promoting students’ performance in training, and courses should be designed to be both easy to learn and engaging, ensuring that students are equipped with the necessary skills to succeed in their future medical careers.
As belief around the potential of computational social science grows, fuelled by recent advances in machine learning, data scientists are ostensibly becoming the new experts in education. Scholars engaged in critical studies of education and technology have sought to interrogate the growing datafication of education yet tend not to use computational methods as part of this response. In this paper, we discuss the feasibility and desirability of the use of computational approaches as part of a critical research agenda. Presenting and reflecting upon two examples of projects that use computational methods in education to explore questions of equity and justice, we suggest that such approaches might help expand the capacity of critical researchers to highlight existing inequalities, make visible possible approaches for beginning to address such inequalities, and engage marginalised communities in designing and ultimately deploying these possibilities. Drawing upon work within the fields of Critical Data Studies and Science and Technology Studies, we further reflect on the two cases to discuss the possibilities and challenges of reimagining computational methods for critical research in education and technology, focusing on six areas of consideration: criticality, philosophy, inclusivity, context, classification, and responsibility.
Daniel Wolf, Heiko Hillenhagen, Billurvan Taskin
et al.
Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in clinical practice, the ability to accurately determine relative positions on medical images is a fundamental prerequisite. Despite its importance, this capability remains highly underexplored. To address this gap, we evaluate the ability of state-of-the-art VLMs, GPT-4o, Llama3.2, Pixtral, and JanusPro, and find that all models fail at this fundamental task. Inspired by successful approaches in computer vision, we investigate whether visual prompts, such as alphanumeric or colored markers placed on anatomical structures, can enhance performance. While these markers provide moderate improvements, results remain significantly lower on medical images compared to observations made on natural images. Our evaluations suggest that, in medical imaging, VLMs rely more on prior anatomical knowledge than on actual image content for answering relative position questions, often leading to incorrect conclusions. To facilitate further research in this area, we introduce the MIRP , Medical Imaging Relative Positioning, benchmark dataset, designed to systematically evaluate the capability to identify relative positions in medical images.
The research evaluates lightweight medical abstract classification methods to establish their maximum performance capabilities under financial budget restrictions. On the public medical abstracts corpus, we finetune BERT base and Distil BERT with three objectives cross entropy (CE), class weighted CE, and focal loss under identical tokenization, sequence length, optimizer, and schedule. DistilBERT with plain CE gives the strongest raw argmax trade off, while a post hoc operating point selection (validation calibrated, classwise thresholds) sub stantially improves deployed performance; under this tuned regime, focal benefits most. We report Accuracy, Macro F1, and WeightedF1, release evaluation artifacts, and include confusion analyses to clarify error structure. The practical takeaway is to start with a compact encoder and CE, then add lightweight calibration or thresholding when deployment requires higher macro balance.
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.
Multimodal large language models (MLLMs) have shown strong potential for medical image reasoning, yet fairness across demographic groups remains a major concern. Existing debiasing methods often rely on large labeled datasets or fine-tuning, which are impractical for foundation-scale models. We explore In-Context Learning (ICL) as a lightweight, tuning-free alternative for improving fairness. Through systematic analysis, we find that conventional demonstration selection (DS) strategies fail to ensure fairness due to demographic imbalance in selected exemplars. To address this, we propose Fairness-Aware Demonstration Selection (FADS), which builds demographically balanced and semantically relevant demonstrations via clustering-based sampling. Experiments on multiple medical imaging benchmarks show that FADS consistently reduces gender-, race-, and ethnicity-related disparities while maintaining strong accuracy, offering an efficient and scalable path toward fair medical image reasoning. These results highlight the potential of fairness-aware in-context learning as a scalable and data-efficient solution for equitable medical image reasoning.
Paul Zaha, Lars Böcking, Simeon Allmendinger
et al.
Medical image segmentation is crucial for disease diagnosis and treatment planning, yet developing robust segmentation models often requires substantial computational resources and large datasets. Existing research shows that pre-trained and finetuned foundation models can boost segmentation performance. However, questions remain about how particular image preprocessing steps may influence segmentation performance across different medical imaging modalities. In particular, edges-abrupt transitions in pixel intensity-are widely acknowledged as vital cues for object boundaries but have not been systematically examined in the pre-training of foundation models. We address this gap by investigating to which extend pre-training with data processed using computationally efficient edge kernels, such as kirsch, can improve cross-modality segmentation capabilities of a foundation model. Two versions of a foundation model are first trained on either raw or edge-enhanced data across multiple medical imaging modalities, then finetuned on selected raw subsets tailored to specific medical modalities. After systematic investigation using the medical domains Dermoscopy, Fundus, Mammography, Microscopy, OCT, US, and XRay, we discover both increased and reduced segmentation performance across modalities using edge-focused pre-training, indicating the need for a selective application of this approach. To guide such selective applications, we propose a meta-learning strategy. It uses standard deviation and image entropy of the raw image to choose between a model pre-trained on edge-enhanced or on raw data for optimal performance. Our experiments show that integrating this meta-learning layer yields an overall segmentation performance improvement across diverse medical imaging tasks by 16.42% compared to models pre-trained on edge-enhanced data only and 19.30% compared to models pre-trained on raw data only.
Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by source-target alignment, or the source-free setting, which typically resorts to implicit adaptation mechanisms such as pseudo-labeling and network distillation. This substantial divergence in methodological designs between the two settings reveals an inherent flaw: the lack of an explicit, structured construction of anatomical knowledge that naturally generalizes across domains and settings. To bridge this longstanding divide, we introduce a unified, semantically grounded framework that supports both source-accessible and source-free adaptation. Fundamentally distinct from all prior works, our framework's adaptability emerges naturally as a direct consequence of the model architecture, without relying on explicit cross-domain alignment strategies. Specifically, our model learns a domain-agnostic probabilistic manifold as a global space of anatomical regularities, mirroring how humans establish visual understanding. Thus, the structural content in each image can be interpreted as a canonical anatomy retrieved from the manifold and a spatial transformation capturing individual-specific geometry. This disentangled, interpretable formulation enables semantically meaningful prediction with intrinsic adaptability. Extensive experiments on challenging cardiac and abdominal datasets show that our framework achieves state-of-the-art results in both settings, with source-free performance closely approaching its source-accessible counterpart, a level of consistency rarely observed in prior works. The results provide a principled foundation for anatomically informed, interpretable, and unified solutions for domain adaptation in medical imaging. The code is available at https://github.com/wxdrizzle/remind
ObjectivesRetinal pigment epithelium (RPE) cell transplantation holds therapeutic promise for retinal degenerative diseases, but longitudinal monitoring of graft survival and efficacy remains clinically challenging. The aim of this study is to develop a simple and effective method for the therapeutic quantification of RPE cell transplantation and immune rejection in vivo.MethodsA nanoprobe was developed and modified to label donor RPE cells, and used to monitor the position and intensity of the fluorescence signal in vivo. Immunofluorescence staining and single-cell RNA sequencing (scRNA-seq) were used to characterize the cell types showing the fluorescence signal of the nanoprobe and to determine the composition of the immune microenvironment associated with subretinal transplantation.ResultsThe spatial distribution of the fluorescence signal of the nanoprobe corresponded with the site of transplantation, but the signal intensity decreased over time, while the signal distribution extended to the choroid. Additionally, the nanoprobe fluorescence signal was detected in the liver and spleen during long-term monitoring. Conversely, in mice administered the immunosuppressive drug cyclosporine A, the decrease in signal intensity was slower and the expansion of the signal distribution was less pronounced. Immunofluorescence analysis revealed a significant temporal increase in the proportion of macrophages with nanoprobe-labeled cells following transplantation. The stability and cell-penetrating ability of the nanoprobe enables the labeling of immune cell niches in RPE transplantation. Additionally, scRNA-seq analysis of nanoprobe-labeled cells identified MDK and ANXA1 signaling pathway in donor RPE cells as initiators of the immune rejection cascade, which were further amplified by macrophage-mediated pro-inflammatory signaling.ConclusionNear-infrared fluorescent nanoprobes represent a reliable method for in vivo tracing of donor RPE cells and long-term observation of nanoprobe distribution can be used to evaluate the degree of immune rejection. Molecular analysis of nanoprobe-labeled cells facilitates the characterization of the dynamic immune cell rejection niche and the landscape of donor-host interactions in RPE transplantation.
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance for diagnosis and treatment. Before that, it is crucial to develop benchmarks to evaluate LVLMs' effectiveness in various medical applications. Current benchmarks are often built upon specific academic literature, mainly focusing on a single domain, and lacking varying perceptual granularities. Thus, they face specific challenges, including limited clinical relevance, incomplete evaluations, and insufficient guidance for interactive LVLMs. To address these limitations, we developed the GMAI-MMBench, the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format. Additionally, we implemented a lexical tree structure that allows users to customize evaluation tasks, accommodating various assessment needs and substantially supporting medical AI research and applications. We evaluated 50 LVLMs, and the results show that even the advanced GPT-4o only achieves an accuracy of 53.96%, indicating significant room for improvement. Moreover, we identified five key insufficiencies in current cutting-edge LVLMs that need to be addressed to advance the development of better medical applications. We believe that GMAI-MMBench will stimulate the community to build the next generation of LVLMs toward GMAI.
Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significant data imbalance, with most data from Europe/America and under 10% from Asia, despite its 60% global population share; and 2) hefty time and investment needed to curate proprietary datasets for commercial use. In response, we established the first commercial medical imaging platform, encompassing steps like: 1) data collection, 2) data selection, 3) annotation, and 4) pre-processing. Moreover, we focus on harnessing under-represented data from Japan and broader Asia, including Computed Tomography, Magnetic Resonance Imaging, and Whole Slide Imaging scans. Using the collected data, we are preparing/providing ready-to-use datasets for medical AI R&D by 1) offering these datasets to AI firms, biopharma, and medical device makers and 2) using them as training/test data to develop tailored AI solutions for such entities. We also aim to merge Blockchain for data security and plan to synthesize rare disease data via generative AI. DataHub Website: https://medical-datahub.ai/
Medical image classification plays a crucial role in computer-aided clinical diagnosis. While deep learning techniques have significantly enhanced efficiency and reduced costs, the privacy-sensitive nature of medical imaging data complicates centralized storage and model training. Furthermore, low-resource healthcare organizations face challenges related to communication overhead and efficiency due to increasing data and model scales. This paper proposes a novel privacy-preserving medical image classification framework based on federated learning to address these issues, named FedMIC. The framework enables healthcare organizations to learn from both global and local knowledge, enhancing local representation of private data despite statistical heterogeneity. It provides customized models for organizations with diverse data distributions while minimizing communication overhead and improving efficiency without compromising performance. Our FedMIC enhances robustness and practical applicability under resource-constrained conditions. We demonstrate FedMIC's effectiveness using four public medical image datasets for classical medical image classification tasks.
Sarah Al-Nasser, Maha Hamadien Abdulla, Noura Alhassan
et al.
<i>Background and Objectives</i>: Colorectal cancer (CRC) remains a major global health issue. Although chemotherapy is the first-line treatment, its effectiveness is limited due to drug resistance developed in CRC. To overcome resistance and improve the prognosis of CRC patients, investigating new therapeutic approaches is necessary. <i>Materials and Methods</i>: Using human colorectal adenocarcinoma (HT29) and metastatic CRC (SW620) cell lines, the potential anticancer properties of a newly synthesized compound 1-(Isobutyl)-3-(4-methylbenzyl) benzimidazolium chloride (IMBZC) were evaluated by performing MTT cytotoxicity, cell migration, and colony formation assays, as well as by monitoring apoptosis-related protein and gene expression using Western blot and reverse transcription–quantitative polymerase chain reaction technologies. <i>Results:</i> Tested at various concentrations, the half-maximal inhibitory concentrations (IC<sub>50</sub>) of IMBZC on HT29 and SW620 cell growth were determined to be 22.13 µM (6.97 μg/mL) and 15.53 µM (4.89 μg/mL), respectively. IMBZC did not alter the cell growth of normal HEK293 cell lines. In addition, IMBZC inhibited cell migration and significantly decreased colony formation, suggesting its promising role in suppressing cancer metastasis. Mechanistic analyses revealed that IMBZC treatment increased the expression of pro-apoptotic proteins p53 and Bax, while decreasing the expression of anti-apoptotic proteins Bcl-2 and Bcl-xL, thus indicating the induction of apoptosis in IMBZC-treated CRC cells, compared to untreated cells. Additionally, the addition of IMBZC to conventional chemotherapeutic drugs (i.e., 5-fluorouracil, irinotecan, and oxaliplatin) resulted in an increase in the cytotoxic potential of the drugs. <i>Conclusions</i>: This study suggests that IMBZC has substantial anticancer effects against CRC cells through its ability to induce apoptosis, inhibit cancer cell migration and colony formation, and enhance the cytotoxic effects of conventional chemotherapeutic drugs. These findings indicate that IMBZC could be a promising chemotherapeutic drug for the treatment of CRC. Further research should be conducted using in vivo models to confirm the anti-CRC activities of IMBZC.
BackgroundThe current rate of organ donation in China falls significantly below the global average and the actual demand. Nursing students play a crucial role in supporting and promoting social and public welfare activities. This study primary aims to analyze the levels of knowledge, attitudes, willingness toward organ donation, and attitudes toward death among nursing students, and investigate the mediating role of attitude in the relationship between knowledge and willingness. The secondary aims to identify factors that may influence the willingness.MethodsA convenience sample of nursing students completed online-administered questionnaires measuring the level of knowledge, attitudes, and willingness toward organ donation before and after clinical internship. Spearman correlation and mediation analyses were used for data analyses.ResultsBefore the clinical internship, there were 435 nursing students who had not yet obtained their degrees and were completing their clinical internships. After the internship, this number decreased to 323. The mean score for knowledge before and after the clinical internship (7.17 before and 7.22 after, with no significant difference), the attitude (4.58 before and 4.36 after, with significant difference), the willingness (12.41% before and 8.67% after, with significant difference), the Death Attitude Profile-Revised (DAP-R) score (94.41 before and 92.56 after, with significant difference). The knowledge indirectly affected nursing students’ willingness to organ donation through attitude. Knowledge had a direct and positive impact on attitudes (β = 1.564). Additionally, nursing students’ attitudes positively affected their willingness (β = 0.023). Attitudes played a mediating role in the relationship between knowledge and willingness (β = 0.035). Additionally, attitude toward death, fear of death, and acceptance of the concept of escape were found to be correlated with their willingness.ConclusionOrgan donation willingness was found to be low among nursing students. Positive attitudes were identified as a mediating factor between knowledge and willingness. Additionally, DAP-R was a related factor. Therefore, it is recommended to focus on improving knowledge and attitude, as well as providing death education to help nursing students establish a positive attitude toward death. These efforts can contribute to the promotion of organ donation.