Artificial Intelligence and medical specialties: support or substitution?

The rapid advancement of artificial intelligence (AI) in healthcare has spurred extensive debate regarding its potential to replace human expertise across various medical specialties. This narrative review critically examines the integration of AI within diverse medical specialties to discern its role as a substitute or supporter. The analysis encompasses AI's impact on diagnostic precision, treatment planning, and patient care.

Gemini-Assisted Deep Learning Classification Model for Automated Diagnosis of High-Resolution Esophageal Manometry Images.

To develop a deep learning model for esophageal motility disorder diagnosis using high-resolution manometry images with the aid of Gemini. Gemini assisted in developing this model by aiding in code writing, preprocessing, model optimization, and troubleshooting. The model demonstrated an overall precision of 0.

Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence.

Background And Aims: High-resolution esophageal manometry (HREM) is the gold standard procedure used for the diagnosis of esophageal motility disorders (EMD). Artificial intelligence (AI) might provide an efficient solution for the automatic diagnosis of EMD by improving the subjective interpretation of HREM images. The aim of our study was to develop an AI-based system, using neural networks, for the automatic diagnosis of HREM images, based on one wet swallow raw image.

Deep Learning and Antibiotic Resistance.

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics.

Automated Chicago Classification for Esophageal Motility Disorder Diagnosis Using Machine Learning.

The goal of this paper is to provide a Machine Learning-based solution that can be utilized to automate the Chicago Classification algorithm, the state-of-the-art scheme for esophageal motility disease identification. First, the photos were preprocessed by locating the area of interest-the precise instant of swallowing. After resizing and rescaling the photos, they were utilized as input for the Deep Learning models.