AIMC Topic: Humans

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Deriving three one dimensional NMR spectra from a single experiment through machine learning.

Nature communications
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for analyzing complex mixtures due to its ability to manage matrix complexity, provide detailed molecular insights, and preserve sample integrity. In metabolomics, NMR enables the ident...

Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning.

BMC medical genomics
BACKGROUND: Newborn screening (NBS) enables early detection of metabolic disorders, but current tandem mass spectrometry (MS/MS) methods often lead to false positives and require confirmatory testing, causing diagnostic delays. We evaluated whether i...

Comparative performance of large language models in answering periodontology questions from the Turkish Dental Specialty Examination: a cross-sectional study on accuracy and coverage.

BMC oral health
BACKGROUND: In recent years, several studies have explored the use of large language models (LLMs) such as ChatGPT-4, Claude, Gemini Advanced, and DeepSeek-R1 in dental education. Nevertheless, no study has yet reported a comparative evaluation of mu...

Artificial intelligence in anesthesia: comparison of the utility of ChatGPT v/s google gemini large language models in pre-anesthetic education: content, readability and sentiment analysis.

BMC anesthesiology
BACKGROUND: Large Language Models (LLMs) such as ChatGPT and Google Gemini are increasingly explored for their potential in patient education, particularly in the perioperative setting. As text-based tools trained on extensive datasets, they can gene...

MPIDNN-GPPI: multi-protein language model with an improved deep neural network for generalized protein‒protein interaction prediction.

BMC genomics
Predicting protein‒protein interactions (PPIs) plays a crucial role in understanding biological processes. Although biological experimental methods can identify PPIs, they are costly, time-consuming, labor-intensive, and often lack stability. In cont...

Denoising single-cell RNA-seq data with a deep learning-embedded statistical framework.

BMC bioinformatics
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) provides extensive opportunities to explore cellular heterogeneity but is often limited by substantial technical noise and variability. The prevalence of zero counts, arising from both biological var...

Super-resolution reconstruction of OCT images based on frequency and spatial information in adversarial neural networks.

Physics in medicine and biology
Optical coherence tomography (OCT) has a wide range of applications in the diagnosis and treatment of diseases such as heart and ophthalmic diseases. However, the inherent limitations of imaging hardware, low spatial sampling rates, and noise severel...

Universal black-box attacks against a third-party Alzheimer's diagnostic system.

Biomedical physics & engineering express
Artificial intelligence (AI) systems are increasingly used in medical imaging for disease diagnosis, yet their vulnerability to adversarial attacks poses significant risks for clinical deployment. In this work, we systematically evaluate the suscepti...

Iterative reconstruction of industrial positron images with generative networks.

PloS one
Positron imaging has shown great potential in industrial non-destructive testing due to its high sensitivity and ability to reveal internal structures of complex components. However, reconstructing high-quality images from positron emission data rema...

Using machine learning for detection of Parkinson's disease and mild cognitive impairment.

PloS one
BACKGROUND: Parkinson's disease is a movement disorder featuring motor symptoms and cognitive decline, which can manifest as mild cognitive impairment. The incidence of mild cognitive impairment increases with disease progression, and Parkinson's dis...