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Abstract Gallbladder (GB) disease is a common pathology that needs correct and early diagnosis for the optimum medical treatment. Early diagnosis is crucial as any delay or misdiagnosis can worsen the patient situation. Incorrect diagnosis could also lead to an escalation in patient symptoms and poorer clinical outcomes. The use of Artificial Intelligence (AI) techniques, ranging from Machine Learning (ML) to Deep Learning (DL) to predict disease progression, identify abnormalities, and estimate mortality rates associated with GB disorders has increased over the past decade. To this end, this paper provides a comprehensive overview of the AI approaches used in the diagnosis of GB illnesses. This review compiles and compares relevant papers from the last decade to show how AI might enhance diagnostic precision, speed, and efficiency. Therefore, this survey gives researchers the opportunity to find out both the diagnosis of GB diseases and AI techniques in one place. The maximum accuracy rate by ML was when using SVM with 96.67%, whilst the maximum accuracy rate by DL was by utilising a unique structure of VGG, GoogleNet, ResNet, AlexNet and Inception with 98.77%. This could provide a clear path for further investigations and algorithm’s development to boost diagnostic results to improve the patient’s condition and choose the appropriate treatment.

Artificial Intelligence (AI) allows computers to self-develop decision-making algorithms through huge data analysis. In medical investigations, using computers to automatically diagnose diseases is a promising area of research that could change healthcare strategies worldwide. However, it can be challenging to reproduce or/and compare various approaches due to the often-limited datasets comprising medical images. Since there is no open access dataset for the Gallbladder (GB) organ, we introduce, in this study, a large dataset that includes 10,692 GB Ultrasound Images (UI) acquired at high resolution from 1,782 individuals. These UI include many disease types related to the GB, and they are organized around nine important anatomical landmarks. The data in this collection can be used to train machine learning (ML) and deep learning (DL) models for computer-aided detection of GB diseases. It can also help academics conduct comparative studies and test out novel techniques for analyzing UI to explore the medical domain of GB diseases. The objective is then to help move medical imaging forward so that patients get better treatment.

Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.

On 30 January 2020, the World Health Organization announced a new coronavirus, which later turned out to be very dangerous. Since that date, COVID-19 has spread to become a pandemic that has now affected practically all regions in the world. Since then, many researchers in medicine have contributed to fighting COVID-19. In this context and given the great growth of scientific publications related to this global pandemic, manual text and data retrieval has become a challenging task. To remedy this challenge, we are proposing CovBERT, a pre-trained language model based on the BERT model to automate the literature review process. CovBERT relies on prior training on a large corpus of scientific publications in the biomedical domain and related to COVID-19 to increase its performance on the literature review task. We evaluate CovBERT on the classification of short text based on our scientific dataset of biomedical articles on COVID-19 entitled COV-Dat-20. We demonstrate statistically significant improvements by using BERT.