HARNESSING ARTIFICIAL INTELLIGENCE: TRANSFORMING CLINICAL TRIALS FOR THE FUTURE
DOI:
https://doi.org/10.22159/ijap.2025v17i6.54181Keywords:
AI, Clinical trial, ML, Regulatory compliance, Real-world evidenceAbstract
To evaluate the impact of artificial intelligence (AI) technologies on clinical trial processes, identify quantitative benefits, and determine areas requiring further research. A comprehensive literature review was conducted examining AI applications across clinical trial phases. The study analysed machine learning (ML), natural language processing (NLP), computer vision, reinforcement learning (RL), and other AI technologies as applied to clinical research processes. AI implementations have delivered substantial quantitative benefits across various aspects of clinical trials (CT). International Business Machine (IBM) Watson enabled an 80% increase in patient accrual to breast cancer trials within just 11 mo. In silico medicine’s generative tensorial reinforcement learning (GENTRL) platform accelerated the drug discovery timeline by a factor of 15, reducing it to just 46 days. Saama Technologies' deep learning (DL) approach detected 30% more anomalous data cases compared to traditional methods. Pfizer’s use of AI-driven quantitative systems pharmacology (QSP) models led to a 60% reduction in Phase 2 dose cohorts. AiCure’s AI-powered monitoring system achieved 25% higher medication adherence and completed trials 30% faster. Meanwhile, Unlearn. AI’s digital twin technology enabled a 30% reduction in control group size without compromising statistical power. These outcomes highlight AI’s powerful role in improving the efficiency, speed, and quality of CT. AI is trans formatively enhancing CT through improved recruitment efficiency, protocol optimization, data quality management, and patient monitoring. However, challenges remain in data quality, algorithm interpretability, regulatory compliance, workflow integration, and bias mitigation. Future research should focus on advanced predictive modelling, explainable AI development, federated learning for privacy preservation, AI-human collaboration models, real-world data integration, and standardized validation procedures. Ethical considerations and regulatory frameworks specifically addressing AI in CT require further development to realize the full potential of these technologies.
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