AI-enhanced patient-centric clinical trial design Available to Purchase

Artificial intelligence (AI) has become a potent catalyst in clinical research, completely altering how clinical trials are conducted. The numerous functions that AI plays in clinical trials are explored in this abstract. The cutting-edge innovation in patient recruitment is AI-driven, which quickly and precisely matches patients with trial requirements. This expedites trial efficiency, ensures the correct participants, and cuts recruitment time. Machine learning-driven predictive analytics provide the basis for successful trial outcomes. Predictive analytics can monitor patient data continually to find safety issues and unfavorable events, offering early warnings that allow quick action and enhanced patient safety. As real-world data is incorporated into clinical trials, a new level of understanding is revealed. AI interprets patient histories from electronic health records and directs researchers to prospective medication candidates. AI is bringing innovative medications to market swiftly for patients who need novel therapies with lower costs and better resource allocation. By leveraging AI and personalization in clinical trials, researchers can identify the most suitable participants, optimize treatment strategies, and enhance the likelihood of treatment success, popularly known as the Personalized medicine strategy. A more patient-centric approach is made possible by the merging of Real-World Data with AI. Researchers can learn more about the traits, preferences, and treatment outcomes of patients in the real world. This review provides a look into a more promising and individualized future for medical research by highlighting the multidimensional function of AI in clinical trials.