AIMC Topic: Reproducibility of Results

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Digital pathology and image analysis of p53 biomarker in lymphomas using two algorithms: correlation with genotype and visual inspection.

Journal of clinical pathology
p53 immunohistochemistry (IHC) is widely used as a rapid surrogate for detecting mutations, with mutations being a key biomarker for poor outcomes in lymphomas. We developed two algorithms using digital quantification tools to assess p53 expression...

A comparative analysis of automatic and manual scoring methods in polysomnography.

Sleep
The objective of this study was to compare twenty-six polysomnography (PSG) parameters between the groups utilizing automatic scoring (AS) software and manual scoring (MS) technique. Two MS groups, each comprising technicians with sleep-scoring exper...

MLDAAPP: machine learning data acquisition for assessing population phenotypes.

G3 (Bethesda, Md.)
Collecting phenotypic data from many individuals is critical to answering fundamental biological questions, particularly in genetics. Yet, whole organismal phenotypic data are still often collected manually; limiting the scale of data generation, pre...

Show and tell: A critical review on robustness and uncertainty for a more responsible medical AI.

International journal of medical informatics
This critical review explores two interrelated trends: the rapid increase in studies on machine learning (ML) applications within health informatics and the growing concerns about the reproducibility of these applications across different healthcare ...

Deep Learning-Augmented Sleep Spindle Detection for Acute Disorders of Consciousness: Integrating CNN and Decision Tree Validation.

IEEE transactions on bio-medical engineering
Sleep spindles, which are key biomarkers of non-rapid eye movement stage 2 sleep, play a crucial role in predicting outcomes for patients with acute disorders of consciousness (ADOC). However, several critical challenges remain in spindle detection: ...

Can Artificial Intelligence Language Models Effectively Address Dental Trauma Questions?

Dental traumatology : official publication of International Association for Dental Traumatology
BACKGROUND/AIM: Artificial intelligence (AI) chatbots, also known as large language models (LLMs), have become increasingly common educational tools in healthcare. Although the use of LLMs for emergency dental trauma is gaining popularity, it is cruc...

Bridging Health Literacy Gaps in Spine Care: Using ChatGPT-4o to Improve Patient-Education Materials.

The Journal of bone and joint surgery. American volume
BACKGROUND: Patient-education materials (PEMs) are essential to improve health literacy, engagement, and treatment adherence, yet many exceed the recommended readability levels. Therefore, individuals with limited health literacy are at a disadvantag...

AI for Extracting Pre-Analytical Variability Data from Biomedical Literature: Feasibility and Validation.

Studies in health technology and informatics
INTRODUCTION: The quality and reproducibility of research results from biological samples are significantly influenced by the pre-analytical variability resulting from different conditions during sample collection, storage and processing. Although nu...

Can AI Outperform Human Aspirants? Evaluating 3 ChatGPT Models on the Spanish FIR and BIR Specialized Health Examinations.

The journal of applied laboratory medicine
BACKGROUND: Artificial intelligence (AI) models are increasingly used in academic and clinical settings that require information synthesis and decision-making. This study explores the performance, accuracy, and reproducibility of 3 OpenAI models-GPT-...

Transforming Skin Quality Evaluation With AI: From Subjective Grading to Data-Driven Precision.

Journal of cosmetic dermatology
BACKGROUND: Skin quality has a significant influence on aesthetic perception, yet its clinical evaluation remains subjective and inconsistent. Traditional assessments, such as visual grading and manual scoring, lack reproducibility and fail to captur...