25 papers · ranked by Valyu relevance
Fatemeh Haghayegh, Alireza Norouziazad, Elnaz Haghani, Ariel Avraham Feygin + 7 more
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases.…
Mugahed A. Al-antari
Rapid advancements in artificial intelligence (AI) and machine learning (ML) are currently transforming the field of diagnostics, enabling unprecedented accuracy and efficiency in disease detection, classification, and treatment planning. This Special Issue, entitled “Artificial Intelligence Advances for Medical…
Mugahed A. Al-Antari
We would like to express our gratitude to all authors who contributed to the Special Issue of “Artificial Intelligence Advances for Medical Computer-Aided Diagnosis” by providing their excellent and recent research findings for AI-based medical diagnosis. Furthermore, special thanks are extended to all reviewers who…
Emmanuel Ifeanyi Obeagu
The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI technologies, leveraging advanced machine learning algorithms and big data analytics, are revolutionizing the diagnosis of hematological disorders such as anemia…
Alexander Muacevic, John R Adler, Vidhya Rekha Umapathy, Suba Rajinikanth B + 7 more
'Suba Rajinikanth B' 'Rajkumar Densingh Samuel Raj' 'Sankalp Yadav' 'Sithy Athiya Munavarah' 'Ponsekar Abraham Anandapandian' 'A Vinita Mary' 'Karthika Padmavathy' 'Akshay R'] Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered…
Kyle D. Howey, Manci Li, Peter R. Christenson, Peter A. Larsen + 1 more
Advancements in AI, particularly deep learning, have revolutionized protein folding modeling, offering insights into biological processes and accelerating drug discovery for protein misfolding diseases. However, detecting misfolded proteins associated with neurodegenerative disorders, such as Alzheimer’s, Parkinson’s…
Christian Lovis, Emmanouil Karampinis, Xuguang Ai, Takanobu Hirosawa + 5 more
'Yukinori Harada' 'Kazuki Tokumasu' 'Takahiro Ito' 'Tomoharu Suzuki' 'Taro Shimizu'] Background Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user’s login, while Gemini…
Authors not listed
Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…
Ilia Kopanichuk, Petr Anokhin, Владимир Шапошников, Vladimir Makharev + 4 more
The integration of artificial intelligence (AI) into medical diagnostic workflows requires robust and consistent evaluation methods to ensure reliability, clinical relevance, and the inherent variability in expert judgments. Traditional metrics like precision and recall often fail to account for the inherent…
Muhammad Nauman Aftab, Faisal Mehmood, Chengjuan Zhang, Alishba Nadeem + 3 more
and Deep Learning Applications Authors: ['Muhammad Nauman Aftab' 'Faisal Mehmood' 'Chengjuan Zhang' 'Alishba Nadeem' 'Zigang Dong' 'Yanan Jiang' 'Kangdongs Liu'] Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment…
Tittaya Mairittha, Tanakon Sawanglok, Panuwit Raden, Sorrawit Treesuk
'Sorrawit Treesuk'] Swine disease surveillance is critical to the sustainability of global agriculture, yet its effectiveness is frequently undermined by limited veterinary resources, delayed identification of cases, and variability in diagnostic accuracy. To overcome these barriers, we introduce a novel AI-powered…
Derek J Van Booven, Cheng-Bang Chen, Sheetal Malpani, Yasamin Mirzabeigi + 3 more
In the realm of computational pathology, the scarcity and restricted diversity of genitourinary (GU) tissue datasets pose significant challenges for training robust diagnostic models. This study explores the potential of Generative Adversarial Networks (GANs) to mitigate these limitations by generating high-quality…
Abdullah Alharthi, Ahmed Abdulmohsen Alqurashi, Turki A Alharbi, Mohammed M. Alammar + 5 more
'Mohammed M. Alammar' 'Nasser Aldosari' 'Houssem R. E. H. Bouchekara' "Yusuf Sha'aban" 'Mohammad Shoaib Shahriar' 'Abdulrahman Al Ayidh'] Abstract— The complex nature of disease mechanisms and the variability of patient symptoms present significant obstacles in developing effective diagnostic tools. Although machine…
Marina Zajnulina
Cancer is one of the leading causes of death worldwide. Fast and safe early-stage, pre- and intraoperative diagnostics can significantly contribute to successful cancer identification and treatment. Artificial intelligence has played an increasing role in the enhancement of cancer diagnostics techniques in the last 15…
Jithin K. Sreedharan, Fred Saleh, Abdullah Alqahtani, Ibrahim Ahmed Albalawi + 7 more
This study has several limitations including the number of studies analysed due to the inclusion criteria. First, it primarily focuses on specific conditions such as Barrett’s neoplasia, sepsis, cardiac arrest, esophageal adenocarcinoma, and gastrointestinal stromal tumours, which limits the generalizability of…
Mita Banik, Ken Kreutz-Delgado, Ishan Mohanty, James B. Brown + 1 more
Understanding the decision-making process of black-box neural network classifiers is crucial for their adoption in medical applications, including histopathology and cancer diagnostics. An approach of increasing interest is to clarify how the decisions of neural networks compare to, and perform parallel to, those of…
Livia Faes, Siegfried K. Wagner, Dun Jack Fu, Xiaoxuan Liu + 13 more
Deep learning has huge potential to transform healthcare. However, significant expertise is required to train such models and this is a significant blocker for their translation into clinical practice. In this study, we therefore sought to evaluate the use of automated deep learning software to develop medical image…
Miriam Angeloni, Davide Rizzi, Simon Schoen, Alessandro Caputo + 4 more
Digital pathology (DP) has revolutionized cancer diagnostics, allowing the development of deep-learning (DL) models supporting pathologists in their daily work and contributing to the improvement of patient care. However, the clinical adoption of such models remains challenging. Here we describe a proof-of-concept…
Sahil Sharma, Muskaan Singh, Liam McDaid, Saugat Bhattacharyya
Explainable Artificial Intelligence (XAI) is crucial in healthcare as it helps make intricate machine learning models understandable and clear, especially when working with diverse medical data, enhancing trust, improving diagnostic accuracy, and facilitating better patient outcomes. This paper thoroughly examines the…
Ranjan Kumar Barman, Saugato Rahman Dhruba, Danh-Tai Hoang, Eldad D. Shulman + 8 more
Artificial intelligence (AI) is making notable advances in digital pathology but faces challenges in human interpretability. Here we introduce EXPAND (EXplainable Pathologist Aligned Nuclear Discriminator), the first pathologist-interpretable AI model to predict breast cancer tumor subtypes and patient survival. EXPAND…
Michael van Hartskamp, Sergio Consoli, Wim Verhaegh, Milan Petković + 1 more
'Anja van de Stolpe'] The idea of Artificial Intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even…
Jonathan Zaslavsky, Pauric Bannigan, Christine Allen
Introduction Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such…
Authors not listed
The ongoing threat of global warming necessitates a shift towards clean energy sources to meet rising demands while reducing carbon emissions. Polymer electrolyte fuel cells (PEFCs) represent a promising technology for both mobile and stationary applications but their poor operational lifetimes and frequent faults are…
Authors not listed
Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Authors not listed
Artificial intelligence (AI) is poised to transform heterogeneous catalysis, ushering in a new paradigm for catalytic materials discovery. By uncovering intricate patterns in high-dimensional data, AI has been reshaping our pursuit of sustainable catalytic processes across the energy, environmental, and chemical…