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Ethical Considerations in AI-Based Clinical Trials and Drug Development
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Artificial intelligence (AI) augments the drug discovery process and clinical trials through faster drug discovery times, better predictive modeling, and efficient patient recruitment protocols. Advanced artificial intelligence systems integrate machine learning with deep learning and natural language processing technology to handle big data at lower costs and higher operational speeds. The application of AI in clinical trials leads to ethical issues arising from biased training data and introducing patient privacy risks, and requiring accountability in the application of AI in making clinical decisions. The need for large quantities of health data, as well as sensitive patient data, creates privacy concerns, which merge with algorithmic bias to form problems in unbalanced treatment process outcomes for enrolled patients. One of the most significant obstacles to using AI is its inability to explain decision-making procedures, so it fails to meet regulatory and doctor-patient trust requirements. The EMA and FDA, along with the WHO, are developing guidelines that attempt to address the issues with the use of AI in clinical trials. The structure put forward for implementing AI establishes standards regarding transparency as well as safeguarding data together with equitable deployment systems. Ethical regulation of AI requires government representatives along with doctors and researchers having to work together for the utilization of the right working framework. The research analyzes ethical problems during AI application in clinical trials and evaluates current regulatory frameworks and outlines guidelines for acceptable AI implementation in drug development.
Keywords
The sectors of Artificial intelligence, clinical trials, drug development, ethical AI, bias in AI, data privacy, AI regulation, FDA, machine learning and transparency in AI require analysis.
References
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How to cite this paper
@article{1707645,
author = {Yaswanth Sai Gampa, Pavan Venkata Sai Suhas Kota, Revanth Krishna, Somasekhar Sai Gutti, Kevin Barasa Sitati},
title = {Ethical Considerations in AI-Based Clinical Trials and Drug Development},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {9},
pages = {1376-1385},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707645.pdf},
abstract = {Artificial intelligence (AI) augments the drug discovery process and clinical trials through faster drug discovery times, better predictive modeling, and efficient patient recruitment protocols. Advanced artificial intelligence systems integrate machine learning with deep learning and natural language processing technology to handle big data at lower costs and higher operational speeds. The application of AI in clinical trials leads to ethical issues arising from biased training data and introducing patient privacy risks, and requiring accountability in the application of AI in making clinical decisions. The need for large quantities of health data, as well as sensitive patient data, creates privacy concerns, which merge with algorithmic bias to form problems in unbalanced treatment process outcomes for enrolled patients. One of the most significant obstacles to using AI is its inability to explain decision-making procedures, so it fails to meet regulatory and doctor-patient trust requirements. The EMA and FDA, along with the WHO, are developing guidelines that attempt to address the issues with the use of AI in clinical trials. The structure put forward for implementing AI establishes standards regarding transparency as well as safeguarding data together with equitable deployment systems. Ethical regulation of AI requires government representatives along with doctors and researchers having to work together for the utilization of the right working framework. The research analyzes ethical problems during AI application in clinical trials and evaluates current regulatory frameworks and outlines guidelines for acceptable AI implementation in drug development.},
keywords = {The sectors of Artificial intelligence, clinical trials, drug development, ethical AI, bias in AI, data privacy, AI regulation, FDA, machine learning and transparency in AI require analysis.},
month = {March},
}