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New Innovation and Idea | Computers in Biology and Medicine | India | Volume 14 Issue 4, April 2025 | Popularity: 4.9 / 10
Revolutionizing Clinical Trials: How AI is Transforming Drug Development into a Faster, Smarter, and Patient - Focused Process
Ravi Raj
Abstract: The traditional drug development pipeline is time - consuming, costly, and frequently challenged by inefficiencies in clinical trial design and participant recruitment. The integration of artificial intelligence (AI) into clinical trials and drug testing platforms is revolutionizing this space by introducing automation, predictive modeling, and real - time analytics. This paper explores how AI technologies such as machine learning, natural language processing, and big data analytics are streamlining every stage of clinical research from trial design to post - market surveillance. By leveraging electronic health records (EHRs), genomics, and patient - reported data, AI is enabling more targeted patient recruitment, adaptive trial protocols, and predictive safety profiling. Case studies in oncology, neurology, and infectious diseases highlight the measurable impact of AI in reducing trial timelines and increasing success rates. Ethical concerns, including data integrity, patient consent, and algorithmic transparency, are also critically discussed. Furthermore, the paper examines the technical and regulatory challenges hindering widespread adoption, such as interoperability and compliance with FDA and EMA standards. Finally, it looks toward the future of AI - powered virtual trials, decentralized platforms, and integration with wearable technologies, emphasizing their role in making clinical research faster, safer, and more inclusive. This comprehensive analysis affirms AI?s transformative potential in reshaping clinical trials into agile, data - driven, and patient - centric models.
Keywords: Artificial Intelligence, Clinical Trials, Drug Development, Predictive Modeling, Patient Recruitment
Edition: Volume 14 Issue 4, April 2025
Pages: 892 - 895
DOI: https://www.doi.org/10.21275/SR25407172432
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