Hasil untuk "Medicine"

A. Esteva, Alexandre Robicquet, Bharath Ramsundar et al.

Wei Yu, Lina Zhao, Sujuan Li et al.

T. Shanafelt, S. Boone, Litjen L J Tan et al.

L. Mandell, R. Wunderink, A. Anzueto et al.

Lionel A. Mandell, Richard G. Wunderink, Antonio Anzueto, John G. Bartlett, G. Douglas Campbell, Nathan C. Dean, Scott F. Dowell, Thomas M. File, Jr. Daniel M. Musher, Michael S. Niederman, Antonio Torres, and Cynthia G. Whitney McMaster University Medical School, Hamilton, Ontario, Canada; Northwestern University Feinberg School of Medicine, Chicago, Illinois; University of Texas Health Science Center and South Texas Veterans Health Care System, San Antonio, and Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas; Johns Hopkins University School of Medicine, Baltimore, Maryland; Division of Pulmonary, Critical Care, and Sleep Medicine, University of Mississippi School of Medicine, Jackson; Division of Pulmonary and Critical Care Medicine, LDS Hospital, and University of Utah, Salt Lake City, Utah; Centers for Disease Control and Prevention, Atlanta, Georgia; Northeastern Ohio Universities College of Medicine, Rootstown, and Summa Health System, Akron, Ohio; State University of New York at Stony Brook, Stony Brook, and Department of Medicine, Winthrop University Hospital, Mineola, New York; and Cap de Servei de Pneumologia i Allergia Respiratoria, Institut Clinic del Torax, Hospital Clinic de Barcelona, Facultat de Medicina, Universitat de Barcelona, Institut d’Investigacions Biomediques August Pi i Sunyer, CIBER CB06/06/0028, Barcelona, Spain.

Sharon K. Inouye, C. H. Dyck, Cathy A. Alessi et al.

J. Kabat‐Zinn

R. Palisano, P. Rosenbaum, Stephen Walter et al.

G. Berntson, J. Thomas Bigger, D. Eckberg et al.

G. Borg

J. Thomson, J. Itskovitz‐Eldor, S. Shapiro et al.

E. Lander, L. Linton, B. Birren et al.

M. Levy, M. Fink, J. Marshall et al.

G. Mandell, R. Douglas, J. Bennett

J. Avise, J. Arnold, R. Ball et al.

Beria Chingnabe Kalpelbe, Angel Gabriel Adaambiik, Wei Peng

With the advent of Vision-Language Models (VLMs), medical artificial intelligence (AI) has experienced significant technological progress and paradigm shifts. This survey provides an extensive review of recent advancements in Medical Vision-Language Models (Med-VLMs), which integrate visual and textual data to enhance healthcare outcomes. We discuss the foundational technology behind Med-VLMs, illustrating how general models are adapted for complex medical tasks, and examine their applications in healthcare. The transformative impact of Med-VLMs on clinical practice, education, and patient care is highlighted, alongside challenges such as data scarcity, narrow task generalization, interpretability issues, and ethical concerns like fairness, accountability, and privacy. These limitations are exacerbated by uneven dataset distribution, computational demands, and regulatory hurdles. Rigorous evaluation methods and robust regulatory frameworks are essential for safe integration into healthcare workflows. Future directions include leveraging large-scale, diverse datasets, improving cross-modal generalization, and enhancing interpretability. Innovations like federated learning, lightweight architectures, and Electronic Health Record (EHR) integration are explored as pathways to democratize access and improve clinical relevance. This review aims to provide a comprehensive understanding of Med-VLMs' strengths and limitations, fostering their ethical and balanced adoption in healthcare.

Leifeng Zhang, Xin Dong, Shuaibing Jia et al.

Traditional Chinese medicine, as an essential component of traditional medicine, contains active ingredients that serve as a crucial source for modern drug development, holding immense therapeutic potential and development value. A multi-layered and complex network is formed from Chinese medicine to diseases and used to predict the potential associations between Chinese medicine ingredients and diseases. This study proposes an ingredient-disease association prediction model (Node2Vec-DGI-EL) based on hierarchical graph representation learning. First, the model uses the Node2Vec algorithm to extract node embedding vectors from the network as the initial features of the nodes. Next, the network nodes are deeply represented and learned using the DGI algorithm to enhance the model's expressive power. To improve prediction accuracy and robustness, an ensemble learning method is incorporated to achieve more accurate ingredient-disease association predictions. The effectiveness of the model is then evaluated through a series of theoretical verifications. The results demonstrated that the proposed model significantly outperformed existing methods, achieving an AUC of 0.9987 and an AUPR of 0.9545, thereby indicating superior predictive capability. Ablation experiments further revealed the contribution and importance of each module. Additionally, case studies explored potential associations, such as triptonide with hypertensive retinopathy and methyl ursolate with colorectal cancer. Molecular docking experiments validated these findings, showing the triptonide-PGR interaction and the methyl ursolate-NFE2L2 interaction can bind stable. In conclusion, the Node2Vec-DGI-EL model focuses on TCM datasets and effectively predicts ingredient-disease associations, overcoming the reliance on node semantic information.

Zhiyu Kan, Wensheng Gan, Zhenlian Qi et al.

Artificial intelligence (AI) technology has advanced rapidly in recent years, with large language models (LLMs) emerging as a significant breakthrough. LLMs are increasingly making an impact across various industries, with the medical field standing out as the most prominent application area. This paper systematically reviews the up-to-date research progress of LLMs in the medical field, providing an in-depth analysis of training techniques for large medical models, their adaptation in healthcare settings, related applications, as well as their strengths and limitations. Furthermore, it innovatively categorizes medical LLMs into three distinct types based on their training methodologies and classifies their evaluation approaches into two categories. Finally, the study proposes solutions to existing challenges and outlines future research directions based on identified issues in the field of medical LLMs. By systematically reviewing previous and advanced research findings, we aim to highlight the necessity of developing medical LLMs, provide a deeper understanding of their current state of development, and offer clear guidance for subsequent research.

Wan Nor Arifin, Najib Majdi Yaacob

Machine learning (ML) methods are being increasingly used across various domains of medicine research. However, despite advancements in the use of ML in medicine, clear and definitive guidelines for determining sample sizes in medical ML research are lacking. This article proposes a method for determining sample sizes for medical research utilizing ML methods, beginning with the determination of the testing set sample size, followed with the determination of the training set and total sample sizes.

R. Gonzalo Parra, Elizabeth A. Komives, Peter G. Wolynes et al.

Molecules provide the ultimate language in terms of which physiology and pathology must be understood. Myriads of proteins participate in elaborate networks of interactions and perform chemical activities coordinating the life of cells. To perform these often amazing tasks, proteins must move and we must think of them as dynamic ensembles of three dimensional structures formed first by folding the polypeptide chains so as to minimize the conflicts between the interactions of their constituent amino acids. It is apparent however that, even when completely folded, not all conflicting interactions have been resolved so the structure remains "locally frustrated". Over the last decades it has become clearer that this local frustration is not just a random accident but plays an essential part of the inner workings of protein molecules. We will review here the physical origins of the frustration concept and review evidence that local frustration is important for protein physiology, protein-protein recognition, catalysis and allostery. Also, we highlight examples showing how alterations in the local frustration patterns can be linked to distinct pathologies. Finally we explore the extensions of the impact of frustration in higher order levels of organization of systems including gene regulatory networks and the neural networks of the brain.

Emanuele Ratti, Mark Graves

We propose a conceptualization and implementation of AI ethics via the capability approach. We aim to show that conceptualizing AI ethics through the capability approach has two main advantages for AI ethics as a discipline. First, it helps clarify the ethical dimension of AI tools. Second, it provides guidance to implementing ethical considerations within the design of AI tools. We illustrate these advantages in the context of AI tools in medicine, by showing how ethics-based auditing of AI tools in medicine can greatly benefit from our capability-based approach.