Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{197689,
        author = {Darshan KR and Rutika Hemant Danavale and Khushi Gupta and Darsana Gopinatham and Cheruku Akshith and Achha Harshitha and K.Hamsika Sri},
        title = {ARTIFICIAL INTELLIGENCE IN CLINICAL TRIALS: ENHANCING CLINICAL TRIAL DESIGN AND PATIENT STRATIFICATION FOR IMPROVED EFFICIENCY AND OUTCOMES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {7472-7482},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197689},
        abstract = {Clinical trials are a critical component of drug development but are often associated with high costs, long durations, and high failure rates. These challenges highlight the need for improved efficiency in clinical trial design and execution. This study explores the role of advanced computational techniques in drug development, with a particular focus on improving clinical trial design and patient stratification.
Computational approaches, including predictive modelling, simulation methods, and optimization algorithms, play an important role in enhancing various aspects of clinical trials. These methods enable more efficient patient recruitment, cohort formation, and trial simulation, allowing researchers to evaluate multiple study scenarios and reduce required sample sizes while maintaining statistical accuracy. Predictive modelling techniques further support the forecasting of treatment responses, adverse events, and overall trial outcomes, enabling improved decision-making and trial planning.
Patient stratification remains a key strategy in addressing variability within clinical populations. Data-driven stratification methods using clinical and molecular data allow the identification of patient subgroups that are more likely to respond to specific treatments. This improves the statistical power of clinical studies and supports more precise and effective therapeutic interventions.
Overall, the study highlights how the integration of computational techniques, patient stratification, and predictive approaches can improve clinical trial efficiency, reduce costs, and enhance outcomes. Key findings emphasize the importance of optimized trial design, effective patient selection, and adaptive methodologies in advancing pharmaceutical research and drug development.},
        keywords = {Clinical trial design; Patient stratification; Drug development; Precision medicine; Biomarkers; Clinical outcomes; Trial efficiency; Predictive modelling; Adaptive clinical trials; Computational biology; Bioinformatics; Patient heterogeneity},
        month = {April},
        }