Explore the vast range of titles available.

This paper aims to highlight the potential applications and limits of a large language model (LLM) in healthcare. ChatGPT is a recently developed LLM that was trained on a massive dataset of text for dialogue with users. Although AI-based language models like ChatGPT have demonstrated impressive capabilities, it is uncertain how well they will perform in real-world scenarios, particularly in fields such as medicine where high-level and complex thinking is necessary. Furthermore, while the use of ChatGPT in writing scientific articles and other scientific outputs may have potential benefits, important ethical concerns must also be addressed. Consequently, we investigated the feasibility of ChatGPT in clinical and research scenarios: (1) support of the clinical practice, (2) scientific production, (3) misuse in medicine and research, and (4) reasoning about public health topics. Results indicated that it is important to recognize and promote education on the appropriate use and potential pitfalls of AI-based LLMs in medicine.

[...]physicians’ assessments improved significantly after incorporating feedback from the ML model. According to their approach, multi-task learning can improve computational efficiency without sacrificing predictive performance. Features of machine learning and artificial neural architectures Feature Machine learning Deep learning Application for postoperative risk prediction Definition Algorithms that learn patterns from data to make predictions or decisions Implement neural networks with multiple layers to learn complex patterns Both can analyze patient data to predict the risk of postoperative complications Data requirements Works well with smaller datasets Requires large datasets for optimal performance Both can use hospital EHRs, although DL can leverage large, multi-center EHR datasets Feature engineering Manual preprocessing Can automatically learn features from raw data ML needs clinical variables chosen by experts; DL can extract patterns directly from raw EHR signals Advantages They provide interpretable risk factors DL models can capture complex nonlinear relationships, but are less interpretable ML performs well on structured EHR features; DL can capture subtle interactions across multiple data modalities (labs, vitals, notes). Both can be integrated into hospital dashboards Training time Faster to train Requires longer training and more computational power ML can quickly retrain new data; DL may need GPUs and more time Interpretability High (e.g., feature importance) Low, requires specialized XAI techniques DL needs extra tools to explain

Unlike traditional video games developed solely for entertainment purposes, game-based learning employs intentionally crafted approaches that seamlessly merge entertainment and educational content, resulting in captivating and effective learning encounters. These pedagogical methods include serious video games and gamification . Serious games are video games utilized as tools for acquiring crucial (serious) knowledge and skills. On the other hand, gamification requires integrating gaming elements (game mechanics) such as points, leaderboards, missions, levels, rewards, and more, into a context that may not be associated with video gaming activities. They can be dynamically (game dynamics) combined developing various strategic approaches. Operatively, gamification adopts simulation elements and leverages the interactive nature of gaming to teach players specific skills, convey knowledge, or address real-world issues. External incentives stimulate internal motivation. Therefore, these techniques place the learners in the central role, allowing them to actively construct knowledge through firsthand experiences. Anesthesia, pain medicine, and critical care demand a delicate interplay of technical competence and non-technical proficiencies. Gamification techniques can offer advantages to both domains. Game-based modalities provide a dynamic, interactive, and highly effective opportunity to learn, practice, and improve both technical and non-technical skills, enriching the overall proficiency of anesthesia professionals. These properties are crucial in a discipline where personal skills, human factors, and the influence of stressors significantly impact daily work activities. Furthermore, gamification can also be embraced for patient education to enhance comfort and compliance, particularly within pediatric settings (game-based distraction), and in pain medicine through stress management techniques. On these bases, the creation of effective gamification tools for anesthesiologists can present a formidable opportunity for users and developers. This narrative review comprehensively examines the intricate aspects of gamification and its potentially transformative influence on the fields of anesthesiology. It delves into theoretical frameworks, potential advantages in education and training, integration with artificial intelligence systems and immersive techniques, and also addresses the challenges that could arise within these contexts.

Ambulatory anesthesia is increasing its activity, both in terms of numbers and complexity. It represents a possible solution for containing costs in healthcare, and for increasing the comfort of patients and families. AI (artificial intelligence) is a possible resource to further implement the activity of ambulatory anesthesia. Clinical trial number Not applicable.

Given the lack of scientific evidence, chronic pain represents an arduous challenge, especially in the pediatric field. In this complex scenario, artificial intelligence (AI) could support diagnosis, therapy, and research. However, the great potential of AI must be combined with the protection of data and the most fragile patients.

The emergence from anesthesia is the stage of general anesthesia featuring the patient's progression from the unconsciousness status to wakefulness and restoration of consciousness. This complex process has precise neurobiology which differs from that of induction. Despite the medications commonly used in anesthesia allow recovery in a few minutes, a delay in waking up from anesthesia, called delayed emergence, may occur. This phenomenon is associated with delays in the operating room, and an overall increase in costs. Together with the emergence delirium, the phenomenon represents a manifestation of inadequate emergence. Nevertheless, in delayed emergence, the transition from unconsciousness to complete wakefulness usually occurs along a normal trajectory, although slowed down. On the other hand, this awakening trajectory could proceed abnormally, possibly culminating in the manifestation of emergence delirium. Clinically, delayed emergence often represents a challenge for clinicians who must make an accurate diagnosis of the underlying cause to quickly establish appropriate therapy. This paper aimed at presenting an update on the phenomenon, analyzing its causes. Diagnostic and therapeutic strategies are addressed. Finally, therapeutic perspectives on the \"active awakening\" are reported. Keywords: general anesthesia, anesthesia emergence, delayed emergence, emergence delirium

Postoperative cognitive dysfunction （POCD） is a multifactorial process with a huge number of predisposing, causal, and precipitating factors. In this scenario, the neuroinflammation and the microglial activation play a pivotal role by triggering and amplifying a complex cascade involving the immuno-hormonal acti- vation, the micro circle alterations, the hippocampal oxidative stress activation and, finally, an increased blood-brain barrier＇s permeability. While the role of anesthetics in the POCD＇s genesis in humans is debated, a huge number of preclinical studies have been conducted on the topic and many mechanisms have been proposed to explain the potential neurodegenerative effects of general anesthetics. Probably, the problem concerns on what we are searching for and how we are searching and, surprisingly, preclinical studies showed that anesthetics may also manifest neuroprotective properties. The aim of this paper is to offer an overview on the potential impact of general anesthetics on POCD. Mechanisms of hippocampal and extra-hippocampal dysfunction due to neuroinflammation are discussed, whereas further research perspectives are also given.

Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common form of dementia characterized by cognitive and memory impairment. One of the mechanism involved in the pathogenesis of AD, is the oxidative stress being involved in AD‘s development and progression. In addition, several studies proved that chronic viral infections, mainly induced by Human herpesvirus 1 (HHV-1), Cytomegalovirus (CMV), Human herpesvirus 2 (HHV-2), and Hepatitis C virus (HCV) could be responsible for AD’s neuropathology. Despite the large amount of data regarding the pathogenesis of Alzheimer’s disease (AD), a very limited number of therapeutic drugs and/or pharmacological approaches, have been developed so far. It is important to underline that, in recent years, natural compounds, due their antioxidants and anti-inflammatory properties have been largely studied and identified as promising agents for the prevention and treatment of neurodegenerative diseases, including AD. The ester of epigallocatechin and gallic acid, (−)-Epigallocatechin-3-Gallate (EGCG), is the main and most significantly bioactive polyphenol found in solid green tea extract. Several studies showed that this compound has important anti-inflammatory and antiatherogenic properties as well as protective effects against neuronal damage and brain edema. To date, many studies regarding the potential effects of EGCG in AD’s treatment have been reported in literature. The purpose of this review is to summarize the in vitro and in vivo pre-clinical studies on the use of EGCG in the prevention and the treatment of AD as well as to offer new insights for translational perspectives into clinical practice.

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.