Artificial intelligence (AI) is increasingly being integrated into Clinical Decision-Support Systems (CDSSs), shifting attention from algorithmic performance alone to the broader sociotechnical conditions that shape effective human–AI collaboration. In this study, we investigated whether nine displacement-based structured coordination protocols can improve the collective diagnostic decision-making of hybrid human–AI teams (16 board-certified radiologists and a simulated AI model) in a radiological double-reading task for vertebral fracture detection from X-ray images. Among the protocols tested, the Accuracy-Oriented, Confidence-Oriented, and Presumptuous strategies achieved the highest (balanced) accuracy overall, with up to 97% among strong clinicians and 92% among weak ones, significantly outperforming simpler methods like majority voting. Conversely, approaches optimized for a single metric (e.g., sensitivity or specificity) introduced performance trade-offs. Benefits were strongest among less proficient clinicians, which exhibited substantial and consistent improvements, while proficient clinicians showed limited gains and occasional declines. Critically, Kasparov’s Law emerged as a comparative framework for empirically evaluating coordination quality relative to the diagnostic task, clinical objective, and clinician proficiency by identifying situations in which less proficient clinicians supported by superior coordination protocols outperformed more proficient clinicians operating under inferior ones. These findings demonstrate that coordination design is a critical determinant of hybrid human–AI decision-making, highlighting that a well-structured process can be more relevant than individual components’ performance and support process-centered approaches to the development and evaluation of CDSSs.

Neonatal Screening (NS) is a mandatory secondary prevention program established in the 1960s to detect rare genetic disorders in newborns. Early diagnosis through NS enables timely interventions, helping to prevent severe health complications. Despite its long-standing implementation, there remains a scarcity of publicly available datasets to support medical research and academic analysis. In this work, we present a comprehensive dataset containing blood samples and associated information from nearly a decade of Neonatal Screening in Lombardy. This dataset offers valuable insights for medical studies on genetic disorders, early diagnostics, and treatment efficacy. Additionally, it can support academic research in data-driven healthcare analysis and policy development. By making this dataset publicly available, we aim to enhance research opportunities in neonatal health and contribute to the improvement of early intervention strategies.

Background: Machine learning (ML) is increasingly applied in medicine, underscoring the need for transparent and clinically relevant models. In gastrointestinal oncology, most ML studies rely on raw imaging data, which limits clinical adoption due to poor interpretability and the difficulty of collecting high-quality, large-scale video and image datasets in routine practice. Endoscopic ultrasound (EUS) plays a central role in the evaluation of pancreatic cancer; however, structured EUS features remain underused in predictive modeling. Objective: To assess the performance and interpretability of ML models for diagnosing pancreatic ductal adenocarcinoma (PDAC) using routinely collected EUS variables. Methods: We conducted a retrospective multicenter study using data from two Italian hospitals (n = 641) for model training and internal validation and from a third hospital (n = 120) for external validation, collected from 2015 to 2023. Decision trees, random forests, naïve Bayes and other classifiers were developed and evaluated. Model performance was assessed in terms of discriminative ability, calibration, and selective prediction. Results: All models demonstrated high discriminative performance (AUC ≥ 0.90). Decision trees provided the most favorable balance between interpretability and accuracy (balanced accuracy = 0.87; sensitivity = 0.89). Calibration and selective prediction analyses confirmed the robustness of the models. Conclusions: These findings demonstrate the feasibility of implementing interpretable yet high-performing ML models for PDAC diagnosis in real-life endoscopic settings.