Abstract Background Clinical research is essential for advancing pediatric care, yet pediatricians often face unique structural, organizational, and methodological barriers that may hinder their participation in scientific activities. Understanding these obstacles is crucial to designing effective strategies that strengthen pediatric research capacity in Italy, a country with a leading role in European pediatric output. This study aimed to investigate Italian pediatricians’ perceptions of barriers, facilitators, and needs related to conducting research, applying evidence in clinical practice, and publishing scientific work. Methods We conducted a cross-sectional online survey of Italian pediatricians using an 18-item questionnaire administered between April and May 2025. Data were analyzed descriptively, and multilevel logistic regression assessed associations between perceived barriers and respondents’ characteristics. Results A total of 717 pediatricians involved in clinical and/or research activities were included. Among those engaged in research ( n = 385), the most frequently reported personal barriers were difficulties balancing clinical and research duties (79.3%) and limited access to funding (67.9%). Operational issues such as delayed Ethics Committee approvals (80.8%) and challenges in multicenter data sharing (55.9%) were also prominent. Many respondents expressed dissatisfaction with current research evaluation systems: 78.2% believed that metrics should reflect long-term patient impact, and 73.1% reported that existing criteria penalize pediatric researchers in low-resource settings. Regarding scientific publishing, major obstacles included limited time (75%), complexity of data analysis (58.7%), and publication costs (55%). Interest in support initiatives was high, particularly for monthly evidence summaries (88.5%) and training in artificial intelligence tools (74.3%). Multivariate analysis revealed significant differences by age, gender, and professional role, especially concerning perceived research training adequacy and data analysis difficulties. Conclusions Italian pediatricians face substantial structural, organizational, and methodological barriers to conducting research. Despite this, they show strong motivation to engage in scientific activities and express clear needs for training, infrastructural support, and improved evaluation systems. Strengthening funding opportunities, enhancing institutional support, and streamlining ethical and administrative procedures are essential to advancing pediatric research capacity and improving future child health outcomes.

INTRODUCTION:Immunotherapies such as CAR T-cell therapy and immune checkpoint inhibitors (ICIs) have transformed pediatric cancer treatment but are increasingly associated with severe central nervous system (CNS) immune-related adverse events (irAEs), which remain poorly understood in children. AREAS COVERED:This review summarizes current knowledge on CNS irAEs in children receiving CAR T-cells, ICIs, and monoclonal antibodies. Based on a narrative literature review from PubMed, Scopus, and Web of Science (2010-2025), it focuses on pediatric neurotoxicity, immunopathogenesis, and age-specific vulnerabilities, including an immature blood-brain barrier, reduced naive T-cells, and chronic inflammation. Common irAEs include ICANS, autoimmune encephalitis, Guillain-Barré syndrome, and hypophysitis. EXPERT OPINION:Managing CNS immune toxicity in pediatric immunotherapy is a critical challenge that requires early detection, ongoing monitoring, and age-appropriate interventions. Progress depends on identifying predictive biomarkers and tailoring immunomodulatory strategies to enhance safety while preserving efficacy.

This study explores lymphocyte profiles as non-invasive biomarkers for classification of pediatric nephrotic syndrome (NS). Using retrospective clinical and immunological data from 205 patients, the aim is to develop a predictive model based on Long Short-Term Memory to identify NS subtypes. By comparing models with and without immunological data, the study will assess the value of immune profiles. The goal is to support personalized management while reducing the need for invasive procedures.

This study presents a two-phase AI-based model to predict surgical wait times in paediatric oncology patients. Using real-world data from 1478 patients and 6145 surgeries, the model first classifies surgical urgency, then estimates wait times for urgent cases. Random Forest emerged as the best-performing algorithm in both phases, and SHAP analysis identified similar key predictive features. Results support AI's role in improving surgical planning, resource allocation, and clinical decision-making.

In Italy, the growing enthusiasm for artificial intelligence (AI) in healthcare contrasts with significant infrastructural, cultural, and trust-related barriers hindering its real-world adoption. Moving beyond the hype requires a systems thinking approach, proposing the learning health system (LHS) framework as a structured path for integration. We highlight the complementary roles of AI models: traditional machine learning (ML) is proven for diagnostics and prognostics, while large language models (LLMs) excel at administrative tasks and can structure unstructured data to train robust ML tools. The LHS cycle reveals key challenges for Italy: moving from Practice-to-Data requires overcoming data fragmentation; from Data-to-Knowledge involves transforming data into insights while mitigating bias; and from Knowledge-to-Practice necessitates bridging the gap between evidence and clinical workflow by building trust and AI literacy. Ultimately, successful and equitable AI implementation depends on a holistic strategy combining infrastructure development, multidisciplinary collaboration, and robust governance to enhance the quality and sustainability of the national healthcare system.

Precision medicine seeks to tailor care by integrating genetic, clinical, and environmental data. Digital twins, dynamic, virtual replicas of patients that are updated with longitudinal information, represent a significant step in this direction. Enabled by artificial intelligence, they allow in silico experimentation to simulate therapies, disease trajectories, and adverse events, reducing risk and sharpening personalization. By bridging data and decisions, digital twins can promote earlier diagnosis, targeted treatments, and faster drug discovery, supporting a shift from reactive to predictive and participatory care. Nonetheless, challenges surrounding data integration, privacy, regulation, and equity persist and necessitate collaborative solutions. This viewpoint examines the opportunities and system-level requirements to integrate digital twins into Italian healthcare. Digital twins redefine medicine by turning episodic encounters into continuous, adaptive care. They can anticipate clinical events, simulate individualized treatments, and support shared decision-making, advancing the vision of predictive, preventive, personalized, and participatory medicine. Realizing this potential requires robust governance, interoperable infrastructures, and clinician training, alongside ethical frameworks that protect autonomy and fairness. Public-private partnerships and international collaboration will be crucial for the responsible, inclusive, and transparent adoption of these initiatives. Ultimately, digital twins inaugurate a paradigm in which simulation and clinical reality converge, fostering innovation that is both scientifically rigorous and deeply human.

Clinical research is increasingly regulated. Despite growing artificial intelligence (AI) use in healthcare, there is a lack of adequate tools to support researchers in non profit (AI or not) studies. To assist with the classification of clinical software, ClinEthix, a prototype conversational tool, has been developed to help researchers with regulatory qualification. A survey of 20 researchers found it highly useful, clear and user-friendly. Future developments will integrate LLMs and human feedback to improve accuracy.

Background: A resurgence of pertussis has been observed in several geographic areas in the post-COVID-19 era. Macrolides are the first-choice antibiotics for the treatment of pertussis. Limited data exist on the impact of the early administration of clarithromycin or azithromycin on infants’ pertussis symptoms. Methods: This retrospective cohort study analyzed infants enrolled in an enhanced surveillance program for pertussis at a single Italian clinical reference center between 2015 and 2020. All cases were laboratory-confirmed. This study compared outcomes based on the timing of macrolide antibiotic treatment: early administration (within 7 days of cough onset) versus late administration (8 days or later). Key outcomes included cough duration, symptom frequency, and complication rates. Results: We studied 148 infants with confirmed pertussis. The median duration of coughing was 14 days in infants with early administration and 24 days in those with late administration. The occurrence of symptoms differed for apnea (62.6% for early administration; 84.6% for late administration). In a multivariable Cox model, the duration of the cough was lower in infants receiving antibiotics within 7 days from the beginning of the cough compared with those starting later (HR = 0.36, 95% CI: 0.25–0.53, p < 0.001). Clarithromycin was associated with a shorter duration of coughing (HR = 0.42, 95% CI: 0.19–0.92, p = 0.030) independently from other factors. Regarding the occurrence of symptoms, children receiving antibiotics later were three times more likely to experience apnea compared to those treated early (p = 0.008). Conclusions: Early treatment with clarithromycin or azithromycin for infants with pertussis improves clinical symptoms. Clarithromycin may be more effective than azithromycin in shortening coughing. The early administration of antibiotics may also help prevent the spread of disease during the resurgence of pertussis and should be considered regardless of the laboratory confirmation, while taking into account the potential side effects of an unnecessary therapy.

Background Artificial intelligence (AI) holds promise in pediatric oncology, yet its full potential faces challenges. We undertook a survey aimed at assessing the viewpoints of European pediatric oncologists delving into their perceptions and expectations regarding the potential influence of AI in their clinical workflows. Method We conducted a survey by means of four hypothetical scenarios using AI and the Shinners Artificial Intelligence Perception (SHAIP) tool to assess healthcare professionals' perceptions of AI in pediatric oncology. We performed multinomial logistic regression to explore associations of responses to clinical scenarios with age and SHAIP scores. Results We obtained 140 responses and the analysis was performed on 108. The SHAIP questionnaire mean total score was 3.29 (SD 0.93) for the professional impact, and 2.37 (SD 0.61) for preparedness. Regarding the clinical scenarios, 34.9% of respondents would ask for a procedure for confirming their diagnosis in case of discrepancy between AI decision support and human diagnosis; 55.8% would be concerned about the generalizability an AI decision support system in case of lack of data from certain geographic areas during algorithm training; 47.6% would feel uncomfortable in the informed consent process for an AI intervention; 10.2% would no longer trust AI in case of a cyberattack affecting AI support for diagnosis. Discussion This survey underscores the importance of AI tools in pediatric oncology that incorporate human oversight in clinical decision-making and training AI algorithms with diverse and representative data. Our findings suggest that pediatric oncologists may not be adequately prepared for the seamless integration of AI in clinical practice.

Background: Monitoring effectiveness of pertussis vaccines is necessary to adapt vaccination strategies. PERTINENT, Pertussis in Infants European Network, is an active sentinel surveillance system implemented in 35 hospitals across six EU/EEA countries. We aim to measure pertussis vaccines effectiveness (VE) by dose against hospitalisation in infants aged <1 year. Methods: From December 2015 to December 2019, participating hospitals recruited all infants with pertussis-like symptoms. Cases were vaccine-eligible infants testing positive for Bordetella pertussis by PCR or culture; controls were those testing negative to all Bordetella spp. For each vaccine dose, we defined an infant as vaccinated if she/ he received the corresponding dose >14 days before symptoms. Unvaccinated were those who did not receive any dose. We calculated (one-stage model) pooled VE as 100*(1-odds ratio of vaccination) adjusted for country, onset date (in 3-month categories) and age-group (when sample allowed it). Results: Of 1,393 infants eligible for vaccination, we included 259 cases and 746 controls. Median age was 16 weeks for cases and 19 weeks for controls (p < 0.001). Median birth weight and gestational age were 3,235 g and week 39 for cases, 3,113 g and week 39 for controls. Among cases, 119 (46 %) were vaccinated: 74 with one dose, 37 two doses, 8 three doses. Among controls, 469 (63 %) were vaccinated: 233 with one dose, 206 two doses, 30 three doses. Adjusted VE after at least one dose was 59 % (95 %CI: 36-73). Adjusted VE was 48 % (95 %CI: 5-71) for dose one (416 eligible infants) and 76 % (95 %CI: 43-90) for dose two (258 eligible infants). Only 42 infants were eligible for the third dose. Conclusions: Our results suggest moderate one -dose and two -dose VE in infants. Larger sample size would allow more precise estimates for dose one, two and three.

In recent years, there has been an exponential increase in the generation and accessibility of electronic healthcare data, often referred to as “real-world data”. The landscape of data sources has significantly expanded to encompass traditional databases and newer sources such as the social media, wearables, and mobile devices. Advances in information technology, along with the growth in computational power and the evolution of analytical methods relying on bioinformatic tools and/or artificial intelligence techniques, have enhanced the potential for utilizing this data to generate real-world evidence and improve clinical practice. Indeed, these innovative analytical approaches enable the screening and analysis of large amounts of data to rapidly generate evidence. As such numerous practical uses of artificial intelligence in medicine have been successfully investigated for image processing, disease diagnosis and prediction, as well as the management of pharmacological treatments, thus highlighting the need to educate health professionals on these emerging approaches. This narrative review provides an overview of the foremost opportunities and challenges presented by artificial intelligence in pharmacology, and specifically concerning the drug post-marketing safety evaluation.

BACKGROUND:Respiratory syncytial virus (RSV) infection in children under 5 years have a significant clinical burden, also in primary care settings. This study investigates the epidemiology and burden of RSV in Italian children during the 2019/20 pre-pandemic winter season.METHODS:A prospective cohort study was conducted in two Italian regions. Children with Acute Respiratory Infection (ARI) visiting pediatricians were eligible. Nasopharyngeal swabs were collected and analyzed via multiplex PCR for RSV detection. A follow-up questionnaire after 14 days assessed disease burden, encompassing healthcare utilization and illness duration. Statistical analyses, including regression models, explored associations between variables such as RSV subtype and regional variations.RESULTS:Of 293 children with ARI, 41% (119) tested positive for RSV. Median illness duration for RSV-positive cases was 7 days; 6% required hospitalization (median stay: 7 days). Medication was prescribed to 95% (110/116) of RSV cases, with 31% (34/116) receiving antibiotics. RSV subtype B and regional factors predicted increased healthcare utilization. Children with shortness of breath experienced a 36% longer illness duration.CONCLUSIONS:This study highlights a significant clinical burden and healthcare utilization associated with RSV in pre-pandemic Italian primary care settings. Identified predictors, including RSV subtype and symptomatology, indicate the need for targeted interventions and resource allocation strategies. RSV epidemiology can guide public health strategies for the implementation of preventive measures.

Artificial intelligence (AI) has demonstrated great progress in the detection, diagnosis, and treatment of cardiovascular diseases. The US Food and Drug Administration and European Committee regulate AI through diverse frameworks. The FDA approval process mandates that a device be proved efficacious compared with a control or be substantially equivalent to a predicate device, whereas the European Union approval process mandates that the device perform its intended function. We are introducing AI regulation frameworks for medical devices and the most common AI applications for cardiovascular disease management.

Artificial intelligence (AI) is experiencing advances and integration in all medical specializations, and this creates excitement but also concerns. This narrative review aims to critically assess the state of the art of AI in the field of endometriosis and adenomyosis. By enabling automation, AI may speed up some routine tasks, decreasing gynecologists’ risk of burnout, as well as enabling them to spend more time interacting with their patients, increasing their efficiency and patients’ perception of being taken care of. Surgery may also benefit from AI, especially through its integration with robotic surgery systems. This may improve the detection of anatomical structures and enhance surgical outcomes by combining intra-operative findings with pre-operative imaging. Not only that, but AI promises to improve the quality of care by facilitating clinical research. Through the introduction of decision-support tools, it can enhance diagnostic assessment; it can also predict treatment effectiveness and side effects, as well as reproductive prognosis and cancer risk. However, concerns exist regarding the fact that good quality data used in tool development and compliance with data sharing guidelines are crucial. Also, professionals are worried AI may render certain specialists obsolete. This said, AI is more likely to become a well-liked team member rather than a usurper.

1. Unicef, Press release: Immunization. Available at: https://www.unicef.org/immuniz.... Accessed November 2022 Google Scholar

Weight restoration is the primary goal of treatment for patients with Anorexia Nervosa (AN). This observational pilot study aims to describe adherence to the Mediterranean Diet (MD) and the consequent process of weight and functional recovery in outpatient adolescents diagnosed with AN. Eight patients with a median age of 15.1 (14.0–17.1) years were seen at baseline and after six months. Anthropometrics, body composition, and resting energy expenditure (REE) were assessed. The KIDMED questionnaire, the 24 h recall, and a quantitative food frequency questionnaire were used to evaluate adherence to the MD. The median KIDMED score increased from 5.5 (T0) to 10 (T1), which was not significant. Intakes of grams of carbohydrates, lipids, mono-unsaturated fatty acids, and fiber increased (p = 0.012, p = 0.036, p = 0.036, p = 0.025). Weight significantly increased (p = 0.012) as well as lean body mass (p = 0.036), with a resulting improvement of the REE (p = 0.012). No association between anthropometrics and body composition and the KIDMED score was found. The MD could represent an optimal dietary pattern for weight gain and nutritional restoration in patients with AN, and it could lead to an improvement in body composition and resting energy expenditure.

Background This systematic review has been conducted with the aim of characterizing cognitive deficits and analyzing their frequency in survivors of paediatric Central Nervous System tumours. Materials and methods All literature published up to January 2023 was retrieved searching the databases “PubMed”, “Cochrane”, “APA PsycInfo” and “CINAHL”. The following set of pre-defined inclusion criteria were then individually applied to the selected articles in their full-text version: i) Retrospective/prospective longitudinal observational studies including only patients diagnosed with primary cerebral tumours at ≤ 21 years (range 0-21); ii) Studies including patients evaluated for neuro-cognitive and neuro-psychological deficits from their diagnosis and/or from anti-tumoral therapies; iii) Studies reporting standardized tests evaluating patients’ neuro-cognitive and neuro-psychological performances; iv) Patients with follow-ups ≥ 2 years from the end of their anti-tumoral therapies; v) Studies reporting frequencies of cognitive deficits. Results 39 studies were included in the analysis. Of these, 35 assessed intellectual functioning, 30 examined memory domains, 24 assessed executive functions, 22 assessed attention, 16 examined visuo-spatial skills, and 15 explored language. A total of 34 studies assessed more than one cognitive function, only 5 studies limited their analysis on a single cognitive domain. Attention impairments were the most recurrent in this population, with a mean frequency of 52.3% after a median period post-treatment of 11.5 years. The other cognitive functions investigated in the studies showed a similar frequency of impairments, with executive functions, language, visuospatial skills and memory deficits occurring in about 40% of survivors after a similar post-treatment period. Longitudinal studies included in the systematic review showed a frequent decline over time of intellectual functioning. Conclusions Survivors of paediatric Central Nervous System tumours experience cognitive sequelae characterized by significant impairments in the attention domain (52.3%), but also in the other cognitive functions. Future studies in this research field need to implement more cognitive interventions and effective, but less neurotoxic, tumour therapies to preserve or improve neurocognitive functioning and quality of life of this population.

Vaccine safety is a concern that continues to drive hesitancy and refusal in populations in low-and-middle income countries (LMICs). Communicating about vaccine safety is a strategy that can successfully change personal and community perceptions and behaviors toward vaccination. The COVID-19 infodemic emergency with the rapid rollout of new vaccines and new technology, demonstrated the need for good and effective vaccine safety communication. The Vaccine Safety Net (VSN), a WHO-led global network of websites that provide reliable information on vaccine safety offers the ideal environment for gathering web and social media analytics for measuring impact of vaccine safety messages. Its members work with a wide range of populations, in different geographic locations and at many levels including national, regional, and local. We propose to undertake a pilot study to evaluate the feasibility of implementing COVID-19 vaccine safety communications with VSN members working in LMICs and to assess the impact of communications on public knowledge, attitudes, and perceptions.

Lo sviluppo dell’intelligenza artificiale per l’assistenza sanitaria promette di essere una rivoluzione che dovrebbe essere guidata dai medici. Questa tecnologia ci consente di riconoscere pattern complessi di dati e aiuta nella classificazione delle malattie, nella previsione della prognosi, nell’interpretazione delle immagini e dei suoni che derivano dall’auscultazione. Per lo sviluppo di strumenti di intelligenza artificiale utili e applicabili, è necessario disporre di enormi quantità di dati di alta qualità. Inoltre, l’implementazione di questa tecnologia comporta profonde implicazioni etiche che devono ancora essere del tutto risolte. Trattandosi di un nuovo strumento da introdurre nella pratica clinica, dovrebbero essere prodotte opportune prove di efficacia, adattando la metodologia della EBM applicata agli studi clinici tradizionali a questa nuova tecnologia. I medici, inoltre, dovrebbero accedere a programmi educativi incentrati sull’intelligenza artificiale con un approccio multidisciplinare. Se i medici assumeranno un ruolo guida nello sviluppo dell’intelligenza artificiale per l’assistenza sanitaria, potremo immaginare uno scenario in cui la qualità dell’assistenza e l’equità nell’accesso ai servizi sanitari saranno notevolmente migliorate.