AIMC Topic: Reproducibility of Results

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An academic evaluation of ChatGpt's ability and accuracy in creating patient education resources for rare cardiovascular diseases.

Scientific reports
Generative Pre-trained Transformer (ChatGPT) is a web-based artificial intelligence assistant with the potential to provide information, answer questions, and make recommendations on various topics. Rare cardiovascular diseases (rCVD) are among the h...

Deep learning models for deriving optimised measures of fat and muscle mass from MRI.

Scientific reports
Fat and muscle mass are potential biomarkers of wellbeing and disease in oncology, but clinical measurement methods vary considerably. Here we evaluate the accuracy, precision and ability to track change for multiple deep learning (DL) models that qu...

Multimodal deep learning improving the accuracy of pathological diagnoses for membranous nephropathy.

Renal failure
OBJECTIVES: Renal biopsy is the gold standard for the diagnosis of glomerular diseases including membranous nephropathy (MN), however, it faces challenges in accuracy, objectivity, and reproducibility of tissue evaluation. This study aims to develop ...

Large language models in medical education: a comparative cross-platform evaluation in answering histological questions.

Medical education online
Large language models (LLMs) have shown promising capabilities across medical disciplines, yet their performance in basic medical sciences remains incompletely characterized. Medical histology, requiring factual knowledge and interpretative skills, p...

A deep learning-based clinical decision support system for glioma grading using ensemble learning and knowledge distillation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Gliomas are the most common malignant primary brain tumors, and grading their severity, particularly the diagnosis of low-grade gliomas, remains a challenging task for clinicians and radiologists. With advancements in deep learning and medical image ...

Automated assessment of laparoscopic pattern cutting skills using computer vision and deep learning.

Surgery
BACKGROUND: Pattern cutting assessment in Fundamentals of Laparoscopic Surgery currently relies on manual measurement, which can be time-consuming and prone to variability and human error. An automated, objective assessment system could enhance the e...

Attention-based multimodal deep learning for interpretable and generalizable prediction of pathological complete response in breast cancer.

Journal of translational medicine
BACKGROUND: Accurate prediction of pathological complete response (pCR) to neoadjuvant chemotherapy has significant clinical utility in the management of breast cancer treatment. Although multimodal deep learning models have shown promise for predict...

Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Scientific reports
Detecting skin melanoma in the early stage using dermoscopic images presents a complex challenge due to the inherent variability in images. Utilizing dermatology datasets, the study aimed to develop Automated Diagnostic Systems for early skin cancer ...

Machine learning-assisted early detection of keratoconus: a comparative analysis of corneal topography and biomechanical data.

Scientific reports
Keratoconus is a progressive eye disease characterized by the thinning and bulging of the cornea, leading to visual impairment. Early and accurate diagnosis is crucial for effective management and treatment. This study investigates the application of...

AI in Qualitative Health Research Appraisal: Comparative Study.

JMIR formative research
BACKGROUND: Qualitative research appraisal is crucial for ensuring credible findings but faces challenges due to human variability. Artificial intelligence (AI) models have the potential to enhance the efficiency and consistency of qualitative resear...