Studies of psychological condition of patients suffering from nonalcoholic fatty liver disease are rather equivocal about the results: while some claim that NAFLD patients suffer from anxiety and depression more than non-NAFLD controls, others do not withstand those findings. Lower cognitive potentials have also been reported, both in patient related and in animal model-based investigations, and correlated with assessed brain tissue changes. We hypothesized that NAFLD, as a condition, affects the brain tissue and, subsequently, the cognitive state. So we compared findings in 40 NAFLD positive and 36 NAFLD negative patients and correlated their brain tissue volumes with the results of Montreal Cognitive Assessment (MoCA) test. Binomial logistic regression verified the influence of NAFLD state leading to lower cognitive potentials: odds ratio 0.096; 95% confidence interval (CI) 0.032–0.289;p<0.001. Patients with NAFLD had a greater risk to suffer from the cognitive impairment and depression: RR = 3.9; 95% CI 1.815–8.381;p=0.0005and RR = 1.65; 95% CI 1.16–2.36;p=0.006. NAFLD significantly influenced the cognitive deficit and tissue volume reduction and patients suffering from NAFLD had about four times higher risk of having a cognitive impairment.

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease which is characterized by extremely complex pathogenetic mechanisms and multifactorial etiology. Some of the many pathophysiological mechanisms involved in the development of NAFLD include oxidative stress, impaired mitochondrial metabolism, inflammation, gut microbiota, and interaction between the brain-liver-axis and the regulation of hepatic lipid metabolism. The new therapeutic approaches in the treatment of NAFLD are targeting some of these milestones along the pathophysiological pathway and include drugs like agonists of peroxisome proliferator-activated receptors (PPARs), glucagon-like peptide-1 (GLP-1) agonists, sodium/glucose transport protein 2 (SGLT2) inhibitors, farnesoid X receptor (FXR) agonists, probiotics, and symbiotics. Further efforts in biomedical sciences should focus on the investigation of the relationship between the microbiome, liver metabolism, and response to inflammation, systemic consequences of metabolic syndrome.

Metabolic-associated liver disease (MAFLD) affects up to 70% of overweight and more than 90% of morbidly obese people, and its pathogenesis is rather complex and multifactorial. The criteria for MAFLD include the presence of hepatic steatosis in addition to one of the following three criteria: overweight or obesity, presence of type 2 diabetes mellitus (T2DM), or evidence of metabolic dysregulation. If the specific criteria are present, the diagnosis of MAFLD can be made regardless of alcohol consumption and previous liver disease. The pathophysiological mechanisms of MAFLD, including inflammation, lipotoxicity, mitochondrial disfunction, and oxidative stress, as well as the impact of intestinal gut microbiota, are constantly being elucidated. Treatment strategies that are continually emerging are based on different key points in MAFLD pathogenesis. Yet, the ideal therapeutic option has still not been found and future research is of great importance, as MAFLD represents a multisystemic disease with numerous complications.

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer’s ability to achieve human cognitive performance, which is accomplished through enabling computer “learning”. This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett’s esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.