Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Introduction Healthcare systems are complex and challenging for all stakeholders, but
artificial intelligence (AI) has transformed various fields, including healthcare, with the …
artificial intelligence (AI) has transformed various fields, including healthcare, with the …
Potential of Artificial Intelligence in Healthcare Sector
Artificial intelligence (AI) is gaining attention in multidisciplinary area such as business,
management, decision sciences, and healthcare sector. Nowadays AI is receiving more …
management, decision sciences, and healthcare sector. Nowadays AI is receiving more …
[PDF][PDF] Bridging the gap in alcohol use disorder treatment: integrating psychological, physical, and artificial intelligence interventions
F Uwaifo, AO Uwaifo - … Journal of Applied Research in Social …, 2023 - researchgate.net
BRIDGING THE GAP IN ALCOHOL USE DISORDER TREATMENT: INTEGRATING
PSYCHOLOGICAL, PHYSICAL, AND ARTIFICIAL INTELLIGENCE INTERVENTIO Page 1 …
PSYCHOLOGICAL, PHYSICAL, AND ARTIFICIAL INTELLIGENCE INTERVENTIO Page 1 …
Drug discovery and mechanism prediction with explainable graph neural networks
Apprehension of drug action mechanism is paramount for drug response prediction and
precision medicine. The unprecedented development of machine learning and deep …
precision medicine. The unprecedented development of machine learning and deep …
Integration of computational docking into anti-cancer drug response prediction models
Simple Summary Anti-cancer drug response prediction models aim to reduce the time
necessary for develo** a treatment for patients affected by this complex disease. Their …
necessary for develo** a treatment for patients affected by this complex disease. Their …
NeuPD—A neural network-based approach to predict antineoplastic drug response
With the beginning of the high-throughput screening, in silico-based drug response analysis
has opened lots of research avenues in the field of personalized medicine. For a decade …
has opened lots of research avenues in the field of personalized medicine. For a decade …
CTDN (convolutional temporal based deep‐neural network): an improvised stacked hybrid computational approach for anticancer drug response prediction
The characterization of drug-metabolizing enzymes is a significant problem for customized
therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast …
therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast …
Development and validation of prognostic machine learning models for short-and long-term mortality among acutely admitted patients based on blood tests
Several scores predicting mortality at the emergency department have been developed.
However, all with shortcomings either simple and applicable in a clinical setting, with poor …
However, all with shortcomings either simple and applicable in a clinical setting, with poor …
CICADA (UCX): A Novel Approach for Automated Breast Cancer Classification through Aggressiveness Delineation
Breast cancer remains one of the leading causes of mortality worldwide, with current
classification and segmentation techniques often falling short in accurately distinguishing …
classification and segmentation techniques often falling short in accurately distinguishing …
Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation
V Devaraji, J Sivaraman - Journal of Biomolecular Structure and …, 2024 - Taylor & Francis
Recent advances in hardware and software algorithms have led to the rise of data-driven
approaches for designing therapeutic modalities. One of the major causes of human …
approaches for designing therapeutic modalities. One of the major causes of human …