AIMC Topic: Reproducibility of Results

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Enhancement and optimization of a graphene-based biosensing platform using machine learning for precise breast cancer detection.

Scientific reports
In this study, we introduce a machine learning optimized graphene-based biosensor tailored for the early and accurate detection of breast cancer, aiming to elevate diagnostic reliability and clinical efficacy. The device employs a multilayer Ag-SiO₂-...

[Evaluation of ChatGPT's responses to symptom-oriented questions in otolaryngology].

Orvosi hetilap
Introduction: Chat Generative Pre-Trained Transformer (ChatGPT) is a recently developed artificial intelligence (AI)-based language model that has become an increasingly common source of health-related information due to its accessibility. However, t...

Investigation into the molecular mechanism of obesity: an integrated approach of multi-omics analysis, machine learning and experimental validation.

Journal of translational medicine
BACKGROUND: Obesity has emerged as a major global public health challenge and poses a significant threat to human health. Despite extensive research, the mechanisms underlying its pathological progression remain elusive.

What artificial intelligence (AI) can tell us about Nasoalveolar Molding (NAM)?

BMC oral health
BACKGROUND: The aim of this study was to evaluate the accuracy, reliability and comprehensibility of information about Nasoalveolar Molding (NAM) provided by artificial intelligence (AI).

Reliability of uncertainty quantification methods for deep learning auto-segmentation in head and neck organs at risk.

Physics in medicine and biology
Deep learning auto-segmentation has greatly advanced contouring in radiotherapy. However, quality assurance remains necessary due to performance fluctuation among individual patients. This manual process reintroduces variability and partially reduces...

Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis.

Scientific reports
This study presents an automated system using Convolutional Neural Networks (CNNs) for segmenting FLAIR Magnetic Resonance Imaging (MRI) images to aid in the diagnosis of Multiple Sclerosis (MS). The dataset included 103 patients from Imam Khomeini H...

Evaluating the quality of ChatGPT-generated medical information on major ophthalmic conditions: A comparative assessment against the EQIP tool and guidelines.

PloS one
BACKGROUND: The use of artificial intelligence for creating medical information is on the rise. Nonetheless, the accuracy and reliability of such information require thorough assessment. As a language model capable of generating text, ChatGPT needs a...

Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation.

Renal failure
Diabetic kidney disease (DKD) remains a critical challenge in diabetes management, necessitating a deep understanding of its molecular underpinnings for better diagnosis and treatment strategies. This study was conducted to identify and validate nove...

Validity and reliability analysis of the Turkish life satisfaction scale developed through artificial intelligence.

BMC psychology
This study evaluates the validity and reliability of a Turkish Life Satisfaction Scale developed using artificial intelligence (ChatGPT) to explore AI's potential in creating psychometric tools. The scale was tested on three independent samples of Tu...

Enhanced performance in automated diabetic retinopathy diagnosis achieved through Voronoi diagrams and artificial intelligence.

Scientific reports
Diabetic retinopathy (DR), a serious eye condition in diabetic patients, requires early and precise detection for effective treatment. Late diagnosis and poor blood sugar control exacerbate this condition, highlighting the need for improved diagnosti...