• Bipasha Chanda
• Dr. Ashutosh Shukla
• Dr. Mayur Savaliya

Background: Artificial intelligence (AI) is playing an increasingly vital role in oncology clinical trials by addressing critical challenges in patient recruitment and treatment optimization. Conventional trial processes often suffer from inefficiencies such as prolonged enrollment periods, high operational costs, and difficulties in tailoring treatments to individual patients. AI-driven technologies offer innovative solutions to streamline these processes, enhance trial design, and improve patient outcomes. Objective: This systematic review explores the impact of AI in refining patient selection, increasing trial efficiency, and advancing personalized treatment strategies in oncology research. It examines AI applications in patient-trial matching, adaptive trial frameworks, and predictive analytics to enhance clinical decision-making. Methods: A systematic search was conducted in databases including PubMed, Scopus, and IEEE Xplore, adhering to PRISMA guidelines. The review focused on peer-reviewed studies published over the past decade that investigated AI-driven methodologies in oncology clinical trials. Emphasis was placed on AI applications in recruitment, data management, and treatment enhancement. Key findings were synthesized to assess AI’s role in improving clinical trial efficiency and patient care. Results: Findings indicate that AI significantly enhances patient enrollment by leveraging machine learning (ML), natural language processing (NLP), and electronic health record (EHR) analytics. Additionally, AI-driven approaches contribute to trial efficiency by predicting treatment responses, optimizing therapeutic strategies, and reducing patient dropout rates. However, concerns related to ethical considerations, algorithmic biases, and regulatory compliance pose challenges to widespread AI adoption. Conclusion: AI has the potential to revolutionize oncology clinical trials by expediting patient recruitment, optimizing study designs, and facilitating treatment innovations. While AI-driven strategies demonstrate promise in enhancing trial effectiveness and reducing costs, overcoming ethical, technical, and regulatory barriers is essential for seamless integration into clinical research. Addressing these challenges will be key to maximizing AI’s impact on future oncology trials

"Artificial Intelligence in Oncology Clinical Trials: A Systematic Review on Enhancing Patient Enrollment and Treatment Advancements", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.65-72, December-2025, Available :http://www.jetir.org/papers/JETIRHE06008.pdf

"Artificial Intelligence in Oncology Clinical Trials: A Systematic Review on Enhancing Patient Enrollment and Treatment Advancements", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. pp65-72, December-2025, Available at : http://www.jetir.org/papers/JETIRHE06008.pdf