Artificial intelligence will be revolutionizing the healthcare in near future and is already being integrated in several areas. The utilization of artificial intelligence algorithms to extract quantitative information from full-slide histopathology images has been demonstrated in digital pathology. Artificial intelligence (AI) is anticipated to reduce the workload of pathologists, enhance the impartiality and uniformity of pathology reports, and impact treatment choices through the identification of concealed information within easily accessible data. This review sheds light on how deep learning and machine learning can enhance the imaging of the slides in digital pathology and help physicians make the diagnosis faster.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11888715 | PMC |
| http://dx.doi.org/10.4103/jpbs.jpbs_727_24 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Of rats and robots: A mutual learning paradigm.
J Exp Anal Behav
March 2025
Behavioral Neuroscience Laboratory, Department of Psychology, Boğaziçi University, Istanbul, Turkey.
Robots are increasingly used alongside Skinner boxes to train animals in operant conditioning tasks. Similarly, animals are being employed in artificial intelligence research to train various algorithms. However, both types of experiments rely on unidirectional learning, where one partner-the animal or the robot-acts as the teacher and the other as the student.
View Article and Find Full Text PDFA Self-Priming Pyroptosis-Inducing Agent for Activating Anticancer Immunity.
Adv Healthc Mater
March 2025
Department of Hepatobiliary and Pancreatic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, China.
Pyroptosis, a form of programmed cell death mediated by the gasdermin family, has emerged as a promising strategy for inducing anti-tumor immunity. However, efficiently inducing pyroptosis in tumor cells remains a significant challenge due to the limited activation of key mediators like caspases in tumor tissues. Herein, a self-priming pyroptosis-inducing agent (MnNZ@OMV) is developed by integrating outer membrane vesicles (OMVs) with manganese dioxide nanozymes (MnNZ) to trigger pyroptosis in tumor cells.
View Article and Find Full Text PDFUnlocking the Potential of Machine Learning in Co-crystal Prediction by a Novel Approach Integrating Molecular Thermodynamics.
Angew Chem Int Ed Engl
March 2025
Southeast University, School of Chemistry and Chemical Engineering, Moling Street, Jiangning District, 211189, Nanjing, CHINA.
Co-crystal engineering is of interest for many applications in pharmaceutical, chemistry and material fields, but rational design of co-crystals is still challenging. Although artificial intelligence has brought major changes in the decision-making process for materials design, yet limitations in generalization and mechanistic understanding remain. Herein, we sought to improve prediction of co-crystal by combining mechanistic thermodynamic modeling with machine learning.
View Article and Find Full Text PDFDeep Learning Estimation of Small Airways Disease from Inspiratory Chest CT: Clinical Validation, Repeatability, and Associations with Adverse Clinical Outcomes in COPD.
Am J Respir Crit Care Med
March 2025
University of Iowa, Radiology and Biomedical Engineering, Iowa City, Iowa, United States;
Rationale: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSAD).
Objectives: To evaluate an AI model for estimating fSAD, compare it with dual-volume parametric response mapping fSAD (fSAD), and assess its clinical associations and repeatability in chronic obstructive pulmonary disease (COPD).
Enhancing Large Language Models with Retrieval-augmented Generation: A Radiology-specific Approach.
Radiol Artif Intell
March 2025
Department of Radiology & Biomedical Imaging, University of California, San Francisco (UCSF), San Francisco, Calif.
Retrieval-augmented generation (RAG) is a strategy to improve performance of large language models (LLMs) by providing the LLM with an updated corpus of knowledge that can be used for answer generation in real-time. RAG may improve LLM performance and clinical applicability in radiology by providing citable, up-to-date information without requiring model fine-tuning. In this retrospective study, a radiology-specific RAG was developed using a vector database of 3,689 articles published from January 1999 to December 2023.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!