Machine Learning and Observational Data for Clinical Trial Optimization

Clinical research has long rested on a familiar triad: the randomized trial, the carefully curated cohort, and the cross-sectional survey. Each has its strengths, and each has its blind spots. Trials give us causality but often at great cost and with narrow generalizability. Cohorts offer longitudinal depth but can suffer from attrition and evolving practice patterns. Surveys provide breadth and currency but limited temporal insight. For decades, these data streams have largely run in parallel, occasionally informing one another but rarely being analyzed together with the tools now at our disposal.With the growth of real-world data, electronic medical records, multi-center cohorts, digital health platforms, and clinical study datasets, machine learning and AI-based statistical frameworks are creating new opportunities to connect these evidence streams. These approaches can help researchers identify patient subgroups, predict treatment response, support clinical trial planning, evaluate real-world effectiveness, and improve the design and interpretation of prospective and retrospective clinical studies.This Research Topic invites contributions that apply AI and machine learning to retrospective cohorts, cross-sectional screening data, real-world clinical datasets, digital health data, multi-center studies, and prospective clinical studies, either singly or in combination, with clear implications for how clinical research is planned, optimized, and executed.The aim is to highlight methods, case studies, and clinical applications that show how observational data and machine learning can strengthen clinical evidence generation, improve study design, and support more efficient, generalizable, and patient-centered clinical research.We welcome original research, systematic reviews, methodological papers, brief reports, perspectives, and code or data articles on topics including predictive modeling for patient stratification and outcome forecasting; machine learning for disease subtyping and treatment response prediction; causal inference and target trial emulation from observational data; retrospective EMR and registry-based data mining; integration of clinical, multi-omics, digital health, and trial-related data; adaptive and reinforcement learning approaches for dynamic treatment allocation; missing data handling and sensitivity analysis in longitudinal and cross-sectional settings; transfer learning for improving generalizability across populations and centers; simulation-based design optimization using real-world data; and reporting standards for AI-informed clinical research.Both single-source and multi-source analyses are encouraged. Submissions should demonstrate methodological rigor, clinical relevance, reproducibility where possible, and a clear connection to improved clinical study design, trial optimization, real-world evidence generation, or clinical decision support. Topic editors: Zhiming Lin, Runyue Huang Source: https://www.frontiersin.org/research-topics/84417/machine-learning-and-observational-data-for-clinical-trial-optimization