Alternative Medicine

Human-like dissociations between confidence and accuracy in convolutional neural networks.

Prior research has shown that manipulating stimulus energy by changing both stimulus contrast and variability results in confidence-accuracy dissociations in humans. Specifically, even when performance is matched, higher stimulus energy leads to higher confidence. The most common explanation for this effect, derived from cognitive modeling, is the positive evidence heuristic where confidence negle...

Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal Medicine.

Generative artificial intelligence (generative AI) is a new technology with potentially broad applications across important domains of healthcare, but serious questions remain about how to balance the promise of generative AI against unintended consequences from adoption of these tools. In this position statement, we provide recommendations on behalf of the Society of General Internal Medicine on ...

Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.

Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for...

Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT.

RATIONALE AND OBJECTIVES: Detection and diagnosis of architectural distortion (AD) on digital breast tomosynthesis (DBT) is challenging. This study ap...

Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques.

Brain cancer poses a significant global health challenge, with mortality rates showing a concerning surge over recent decades. The incidence of brain ...

Automated detection of motion artifacts in brain MR images using deep learning.

Quality assessment, including inspecting the images for artifacts, is a critical step during magnetic resonance imaging (MRI) data acquisition to ensu...

Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens.

The field of radiology imaging has experienced a remarkable increase in using of deep learning (DL) algorithms to support diagnostic and treatment dec...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning m...

Explainable biology for improved therapies in precision medicine: AI is not enough.

Technological advances and high-throughput bio-chemical assays are rapidly changing ways how we formulate and test biological hypotheses, and how we t...

Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR.

Allosteric drugs offer a new avenue for modern drug design. However, the identification of cryptic allosteric sites presents a formidable challenge. F...

Investigation of the potential molecular mechanisms of acupuncture in the treatment of long COVID: a bioinformatics approach.

Long COVID is a poorly understood condition characterized by persistent symptoms following the acute phase of COVID-19, including fatigue, cognitive i...

Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.

MOTIVATION: There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histop...

Enhancing the Diagnostic Accuracy of Sacroiliitis: A Machine Learning Approach Applied to Computed Tomography Imaging.

Sacroiliitis is a challenging condition to diagnose accurately due to the subtle nature of its presentation in imaging studies. This study aims to im...

Classification of optic neuritis in neuromyelitis optica spectrum disorders (NMOSD) on MRI using CNN with transfer learning and manipulation of pre-processing on augmentation.

Neuromyelitis optica spectrum disorder (NMOSD), also known as Devic disease, is an autoimmune central nervous system disorder in humans that commonly ...

HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning.

INTRODUCTION: Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial sin...

Holistic understanding of trimethoprim resistance in using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning.

UNLABELLED: Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunitie...

Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images.

Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morpho...

A Deep Learning Approach Considering Image Background for Pneumonia Identification Using Explainable AI (XAI).

Pneumonia mainly refers to lung infections caused by pathogens, such as bacteria and viruses. Currently, deep learning methods have been applied to id...

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning.

Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible th...

Establishment of an HPLC Fingerprint and Cluster Analysis for Miao Ethnic Medicine .

, a traditional Miao medicine, is commonly used by the renowned national-level Chinese Traditional Medicine practitioner Zhengshi Wu for the treatment...