AIMC Topic: Humans

Clear Filters Showing 16461 to 16470 of 95995 articles

Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.

Current problems in diagnostic radiology
In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms ar...

Release of complex imaging reports to patients, do radiologists trust AI to help?

Current problems in diagnostic radiology
BACKGROUND: As a result of the 21st Century Cures Act, radiology reports are immediately released to patients. However, these reports are often too complex for the lay patient, potentially leading to stress and anxiety. While solutions such as patien...

Progress of AI assisted synthesis of polysaccharides-based hydrogel and their applications in biomedical field.

International journal of biological macromolecules
Polymeric hydrogels, characterized by their highly hydrophilic three-dimensional network structures, boast exceptional physical and chemical properties alongside high biocompatibility and biodegradability. These attributes make them indispensable in ...

Artificial intelligence terminology, methodology, and critical appraisal: A primer for headache clinicians and researchers.

Headache
OBJECTIVE: The goal is to provide an overview of artificial intelligence (AI) and machine learning (ML) methodology and appraisal tailored to clinicians and researchers in the headache field to facilitate interdisciplinary communications and research...

Docking-Informed Machine Learning for Kinome-wide Affinity Prediction.

Journal of chemical information and modeling
Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, r...

Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Journal of the American Heart Association
BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage...

Integrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization.

Sensors (Basel, Switzerland)
Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent's sensor readings to calculate a...

Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma.

International journal of molecular sciences
Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with a high 5-year mortality rate. However, proteomic technologies have not yet been utilized to identify SNSCC-associated proteins, which could be used as biomarkers. In this study, we aimed ...

AI in Biomedicine-A Forward-Looking Perspective on Health Equity.

International journal of environmental research and public health
As new artificial intelligence (AI) tools are being developed and as AI continues to revolutionize healthcare, its potential to advance health equity is increasingly recognized. The 2024 Research Centers in Minority Institutions (RCMI) Consortium Nat...