Artificial Intelligence (AI) is rapidly transforming medical education by enabling adaptive tutoring, interactive simulation, diagnostic enhancement, and competency-based assessment. This narrative review explores how AI has influenced learning processes in undergraduate and postgraduate medical training, focusing on methodological rigor, educational impact, and implementation challenges. The literature reveals promising results: large language models can generate didactic content and foster academic writing; AI-driven simulations enhance decision-making, procedural skills, and interprofessional communication; and deep learning systems improve diagnostic accuracy in visually intensive tasks such as radiology and histology. Despite promising findings, the existing literature is methodologically heterogeneous. A minority of studies use controlled designs, while the majority focus on short-term effects or are confined to small, simulated cohorts. Critical limitations include algorithmic opacity, generalizability concerns, ethical risks (e.g., GDPR compliance, data bias), and infrastructural barriers, especially in low-resource contexts. Additionally, the unregulated use of AI may undermine critical thinking, foster cognitive outsourcing, and compromise pedagogical depth if not properly supervised. In conclusion, AI holds substantial potential to enhance medical education, but its integration requires methodological robustness, human oversight, and ethical safeguards. Future research should prioritize multicenter validation, longitudinal evaluation, and AI literacy for learners and educators to ensure responsible and sustainable adoption.

A soluble mesothelin-related peptide (SMRP) is the only FDA-approved biomarker for diagnosis of pleural mesothelioma (PM) and the most used for monitoring treatment. Radiological assessment of PM, based on modified RECIST (mRECIST) criteria, is challenging. This pilot study was designed to evaluate whether SMRP levels correlated over time with mRECIST score. Serial serum samples from PM patients were collected and SMRP levels were measured and compared with the mRECIST score obtained through centralized CT scans by blinded review. The within-patient SMRP-mRECIST relationship over time was estimated through a normal random-effects regression approach applied to the log-transformed mRECIST score. Overall, 58 PM patients were included (46 males and 12 females) with a median age at diagnosis of 67 years (min–max = 48–79), 44 (76%) with epithelioid and 14 (24%) with non-epithelioid histology. The total number of SMRP measurements and CT scans considered for analysis was 183. There was a statistically significant correlation between SMRP and mRECIST score in the 2 cohorts considered both separately and jointly. These results, although exploratory, suggest that SMRP measurement might be considered as an adjunct to monitor PM patients in order to delay CT scans time interval, thus warranting further investigation.

Given the ongoing COVID-19 pandemic, there is a need to identify SARS-CoV-2 and to differentiate it from other respiratory viral infections, especially influenza A and B, in various critical settings. Since their introduction, the use of rapid antigen tests has spread worldwide, but there is variability in their diagnostic accuracy. In the present study, we evaluated the clinical performance of the ID NOW™ COVID-19 2.0, a molecular point-of-care test (POCT) based on enzymatic isothermal amplification for the differential diagnosis of SARS-CoV-2 and influenza A/B in a pediatric emergency setting. A cohort of pediatric patients admitted between December 2022 and February 2023 were simultaneously tested with the POCT and standard laboratory molecular assay. Our findings showed high negative agreement of the POCT assay across the different age groups for SARS-CoV-2, influenza A, and influenza B (more than 98.0%), while its positive agreement varied significantly for the abovementioned viral species from 50.0% to 100%. These results highlight the potential of the ID NOW™ COVID-19 2.0 POCT assay as a reliable and rapid tool for excluding SARS-CoV-2 and influenza A/B infections in symptomatic pediatric patients, although its variable positive agreement suggests a need for confirmatory RT-qPCR testing in certain clinical and epidemiological settings in order to ensure accurate diagnosis and appropriate patient management.

Research development led to an implementation of the safety and efficacy evaluation processes of clinical trials. Sponsors planned and implemented organizational procedures for the different phases of the study, which are called Standard Operating Procedures (SOPs). Also centers where the research activity is done have the same need. SS Antonio e Biagio e Cesare Arrigo Hospital in Alessandria, involved in research activity since time, developed SOPs in order to optimize not only the management but also clinical trials' activation.

Asbestos, consisting of six natural mineral fibrous silicate phases, was widely utilized in industrial development during the 20th century and has left a global legacy of health, environmental, and regulatory challenges. Its remarkable properties (e.g., heat resistance, sound absorption, and tensile strength) made it a useful material in numerous applications. However, scientific research revealed its serious health risks in the early 1900s, with growing evidence during the 1960s, and nowadays its role in the development of different diseases (e.g., respiratory diseases, such as lung cancer, mesothelioma, and asbestosis) is well defined. Mapping this complex history requires integrating heterogeneous and often inconsistent information from nearly 200 countries. In this study, we tested the use of generative artificial intelligence (AI) tools as exploratory and comparative instruments to support the collection of asbestos-related data worldwide. Using Google Gemini (version 2.5 flash) and OpenAI ChatGPT (GPT-4-turbo variant), we gathered historical, medical, and regulatory information and then systematically verified and contextualized it with expert analysis. This dual approach allowed us to assess both the global asbestos situation and the reliability, advantages, and limitations of AI-assisted research. Our results highlight how AI can accelerate data collection and provide useful first drafts while underscoring the necessity of human expertise for validation, interpretation, and critical integration. This study, therefore, contributes a dual perspective: a comprehensive overview of the asbestos legacy across countries and a methodological reflection on the opportunities and pitfalls of employing AI in geoscientific and environmental research.

GLARE-Edu is an AI-powered, adaptive platform supporting healthcare professionals and students in learning clinical guidelines and improving decision-making through personalized training and realistic case simulations. Two case studies demonstrated significant improvements in guideline application and user satisfaction.