Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology.

Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance in gastroenterology remains untested. This study assesses ChatGPT-4's performance in interpreting gastroenterology images.

Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.

An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician's trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached.

The Role of Atmospheric Composition in Defining the Habitable Zone Limits and Supporting Growth.

Studying exoplanet atmospheres is essential for assessing their potential to host liquid water and their capacity to support life (their habitability). Each atmosphere uniquely influences the likelihood of surface liquid water, defining the habitable zone (HZ)-the region around a star where liquid water can exist. However, being within the HZ does not guarantee habitability, as life requires more than just liquid water.

Application of Genomic Selection in Beef Cattle Disease Prevention.

Genomic applications in beef cattle disease prevention have gained traction in recent years, offering new strategies for improving herd health and reducing economic losses in the livestock industry. Advances in genomics, including identification of genetic markers linked to disease resistance, provide powerful tools for early detection, selection, and management of cattle resistant to infectious diseases. By incorporating genomic technologies such as whole-genome sequencing, genotyping, and transcriptomics, researchers can identify specific genetic variants associated with resistance to pathogens like bovine respiratory disease and Johne's disease.

Machine-learning-assisted Preoperative Prediction of Pediatric Appendicitis Severity.

Purpose: This study evaluates the effectiveness of machine learning (ML) algorithms for improving the preoperative diagnosis of acute appendicitis in children, focusing on the accurate prediction of the severity of disease.

Methods: An anonymized clinical and operative dataset was retrieved from the medical records of children undergoing emergency appendectomy between 2014 and 2021. We developed an ML pipeline that pre-processed the dataset and developed algorithms to predict 5 appendicitis grades (1 - non-perforated, 2 - localized perforation, 3 - abscess, 4 - generalized peritonitis, and 5 - generalized peritonitis with abscess).