Artificial intelligence-driven patient monitoring for adverse event detection in clinical trials


  • Sai Bhargavi Vampana Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India
  • Emani Sai Sri Jayanthi Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India
  • D. Aashritha Mary Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India
  • Chakravarthi Sriniketh Department of Pharmacy Practice, Pulla Reddy Institute of Pharmacy, Hyderabad, Telangana, India



Artificial intelligence, Patient monitoring, Adverse event detection, Advanced algorithms, Real-time surveillance, Regulatory compliance, Healthcare innovation


Artificial intelligence (AI) keeps an eye on people in clinical studies to find out when bad things happen. This is a big way that AI is changing healthcare. It goes into a lot of detail about how AI has changed this field and stresses how important it is to use complicated formulas, always keep an eye on things, and follow the rules. These days, we have tools like deep learning frameworks, controlled and unsupervised learning models, and others that help us find bad things faster and more accurately. Tracking in real time is possible with early warning systems and constant data analysis. It helps make sure the experiment is done right and puts the safety of the people being tested first. AI-driven tracking systems can only work in an honest and reliable way if they follow the rules set by regulatory bodies such as the FDA and the EMA. The fact that AI has the ability to change the way medical research is done today, with benefits like making it faster and more accurate, makes its problems even more important. The report comes to the conclusion that more research, better teamwork, and a wider use of AI technologies are needed to make it more reliable to find bad events in clinical studies over time.


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How to Cite

Vampana, S. B., Jayanthi, E. S. S., Mary, D. A., & Sriniketh, C. (2024). Artificial intelligence-driven patient monitoring for adverse event detection in clinical trials. International Journal of Basic & Clinical Pharmacology, 13(4), 543–550.



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