Introduction: Clinical trials are indispensable for advancing drug discovery, yet Phase 2 and Phase 3 studies are frequently hampered by escalating costs, high attrition rates, and complex operational hurdles. This review will examine current issues, specifically those related to cost, safety, efficacy, and operational inefficiencies. It also analyses the importance of novel approaches like decentralized trials, adaptive designs, digital endpoints, predictive biomarkers, and Artificial Intelligence/ Machine Learning (AI/ML) to improve trial efficiency, participant recruitment, data quality, and overall success rates in drug development. Methods: A mixed-method strategy was adopted to study the various aspects and challenges of clinical trial designs, including adaptive trial designs, decentralized trials, trial programs by regulatory authorities, and the use of AI/ML models that evaluate large datasets to improve enrollment, enhance design strategies, and forecast outcomes. Results: Although there are still issues with data integrity, adoption of DCTs, adaptive trials, and digital endpoints has increased enrolment, speed, and precision in drug development. Studies are now more effective because of AI/ML-based optimization of recruitment, trial designs, and outcome predictions. Discussion: The findings reveal that while transformative opportunities are emerging, their full potential hinges on overcoming persistent challenges related to standardization, regulatory alignment, ethical considerations, and the digital divide. Conclusion: While challenges remain, innovative trial approaches enhance trial efficiency, reduce costs, and improve the likelihood of successful drug development. Multi-faceted strategies are needed to foster a more agile, patient-centric, and ethically sound clinical trial ecosystem, emphasizing the synergistic benefits of integrated technological and methodological advancements.
Sai et al. (2026) studied this question.