Journal of Pharmaceutical Research
Year: 2026, Volume: 25, Issue: 3, Pages: 146-155
Review Article
E Nikitha1, K Lara Supriya1, K Rakesh1, Beena Devi Maddi1*, M V Naga Bhushanam1
1Department of Pharmaceutical Regulatory Affairs, Hindu College of Pharmacy, Guntur, Andhra Pradesh, India
*Corresponding Author
Email: [email protected]
AI is revolutionising clinical trials by improving efficiency, accuracy, and decision-making throughout the study process. Traditional clinical trials are frequently time-consuming, expensive, and constrained by factors such as patient recruiting delays, protocol complexity, data management concerns, and high dropout rates. To solve these limitations, AI technologies such as machine learning, natural language processing, predictive analytics, and deep learning are increasingly being integrated into clinical trials. AI-powered solutions offer quick patient identification and recruitment by analysing electronic health records, real-world data, and genomic databases. Predictive modelling enhances trial design by identifying the best outcomes, stratifying patient populations, and projecting potential dangers. During trial execution, AI improves real-time data monitoring, anomaly detection, and adverse event prediction, resulting in improved patient safety and regulatory compliance. Furthermore, decentralised and virtual trials benefit from AI-powered wearable devices and remote monitoring systems, which provide continuous data collection and increased participant involvement. It will be necessary to fully grasp the advantages of AI in clinical research. Despite its benefits, AI deployment in clinical trials poses problems such as data privacy concerns, algorithmic bias, regulatory uncertainties, and the necessity for uniform validation methods. Ethical issues and openness in AI models are essential for preserving confidence and achieving equitable healthcare results. Overall, AI has the potential to significantly accelerate medication development, cut costs, and improve clinical trial success rates. Continued collaboration between researchers, regulatory agencies, technology developers, and healthcare institutions will be required to fully exploit the benefits of AI in clinical research.
Keywords: AI, Clinical Trials, Machine Learning, Predictive Analytics, Patient Recruitment, Real-World Data, Deep Learning, Natural Language Processing (NLP), Drug Development, Digital Health, Decentralised Trials, Data Monitoring, Healthcare Innovation
1. Getz KA, Campo RA. Trends in clinical trial design complexity. Nature Reviews Drug Discovery. 2017; 16 (5). Available from: https://doi.org/10.1038/nrd.2017.65
2. Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019; 20 (2). Available from: https://doi.org/10.1093/biostatistics/kxx069
3. Wouters OJ, McKee M, Luyten J. Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018. JAMA. 2020; 323 (9). Available from: https://doi.org/10.1001/jama.2020.1166
4. Woo M. An AI boost for clinical trials. Nature. 2019; 573 (7775). Available from: https://doi.org/10.1038/d41586-019-02871-3
5. Beck JT, Rammage M, Jackson GP, Preininger AM, Dankwa-Mullan I, Roebuck MC, et al. Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center. JCO Clinical Cancer Informatics. 2020; (4). Available from: https://doi.org/10.1200/cci.19.00079
6. Mayo RC, Leung J. Artificial intelligence and deep learning – Radiology's next frontier?. Clinical Imaging. 2018; 49 Available from: https://doi.org/10.1016/j.clinimag.2017.11.007
7. Goldman DP, Berry SH, McCabe MS, Kilgore ML, Potosky AL, Schoenbaum ML, et al. Incremental Treatment Costs in National Cancer Institute–Sponsored Clinical Trials. JAMA. 2003; 289 (22). Available from: https://doi.org/10.1001/jama.289.22.2970
8. Highlights from CISCRP’s 2017 Perceptions & Insights Study - Centre for Information & Study on Clinical Research Participation. Available from: https://www.ciscrp.org/headline-perceptions-insights-study-march-2018/
9. Schork NJ. Personalized medicine: Time for one-person trials. Nature. 2015; 520 (7549). Available from: https://doi.org/10.1038/520609a
10. Cascini F, Santaroni F, Lanzetti R, Failla G, Gentili A, Ricciardi W. Developing a Data-Driven Approach in Order to Improve the Safety and Quality of Patient Care. Frontiers in Public Health. 2021; 9 Available from: https://doi.org/10.3389/fpubh.2021.667819
11. Spadaccini M, Iannone A, Maselli R, Badalamenti M, Desai M, Chandrasekar VT, et al. Computer-aided detection versus advanced imaging for detection of colorectal neoplasia: a systematic review and network meta-analysis. The Lancet Gastroenterology & Hepatology. 2021; 6 (10). Available from: https://doi.org/10.1016/s2468-1253(21)00215-6
12. Zhang Y, Zhang X, Wu Q, Gu C, Wang Z. Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. Journal of Laparoendoscopic & Advanced Surgical Techniques. 2021; 31 (10). Available from: https://doi.org/10.1089/lap.2020.0777
13. Deliwala SS, Hamid K, Barbarawi M, Lakshman H, Zayed Y, Kandel P, et al. Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis. International Journal of Colorectal Disease. 2021; 36 (11). Available from: https://doi.org/10.1007/s00384-021-03929-3
14. Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointestinal Endoscopy. 2021; 93 (1). Available from: https://doi.org/10.1016/j.gie.2020.06.059
15. Park SH, Choi J, Byeon JS. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence. Korean Journal of Radiology. 2021; 22 (3). Available from: https://doi.org/10.3348/kjr.2021.0048
16. Páez A, Rovers M, Hutchison K, Rogers W, Vasey B, McCulloch P. Beyond the RCT: When are Randomized Trials Unnecessary for New Therapeutic Devices, and What Should We Do Instead?. Annals of Surgery. 2022; 275 (2). Available from: https://doi.org/10.1097/sla.0000000000005053
17. Frieden TR. Evidence for Health Decision Making — Beyond Randomized, Controlled Trials. New England Journal of Medicine. 2017; 377 (5). Available from: https://doi.org/10.1056/nejmra1614394
18. Preventable Adverse Drug Reactions: A Focus on Drug Interactions. FDA. Available from: https://www.fda.gov/drugs/drug-interactions-labeling/preventable-adverse-drug-reactions-focus-druginteractions
19. Singal AG, Higgins PDR, Waljee AK. A Primer on Effectiveness and Efficacy Trials. Clinical and Translational Gastroenterology. 2014; 5 (1). Available from: https://doi.org/10.1038/ctg.2013.13
20. Martin LR, Williams SL, Haskard KB, et al. The challenge of patient adherence. TTherapeutics and Clinical Risk Management 2005;1:189–6.
21. Medication Adherence: The Elephant in the Room. Available from: https://www.uspharmacist.com/article/medication-adherence-theelephant-in-the-room
22. Brown MT, Bussell JK. Medication Adherence: WHO Cares?. Mayo Clinic Proceedings. 2011; 86 (4). Available from: https://doi.org/10.4065/mcp.2010.0575
23. Li L, Cheng WY, Glicksberg BS, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Science Translational Medicine. 2015; 7 (311). Available from: https://doi.org/10.1126/scitranslmed.aaa9364
24. de Jong AJ, van Rijssel TI, Zuidgeest MGP, et al. Opportunities and Challenges for Decentralized Clinical Trials: European Regulators’ Perspective. Clinical Pharmacology & Therapeutics. 2022; 112 (2). Available from: https://doi.org/10.1002/cpt.2628
25. Petrini C, Mannelli C, Riva L, et al. Decentralized clinical trials (DCTs): A few ethical considerations. Frontiers in Public Health. 2022; 10 Available from: https://doi.org/10.3389/fpubh.2022.1081150
26. Chen H, Gomez C, Huang CM, et al. Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review. npj Digital Medicine. 2022; 5 (1). Available from: https://doi.org/10.1038/s41746-022-00699-2
27. Woo M. An AI boost for clinical trials. Nature. 2019; 573 (7775). Available from: https://doi.org/10.1038/d41586-019-02871-3
28. Stumpf MP. Biosimulation in drug development. Human Genomics. 2008;3:98.
29. Welcome to BIOiSIM. Drug Discovery & Development Technology. Available from: https://www.verisimlife.com/our-platform
30. Leveraging Real World Data to Measure Disease Severity – IQVIA. Available from: https://www.iqvia.com/locations/united-states/
© 2026 Published by Krupanidhi College of Pharmacy. This is an open-access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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