A Review: Artificial Intelligence in Clinical Trails: Applications, Challenges and Future Perspectives

Abstract

Clinical trials are an essential component of drug development and are conducted to establish the safety, efficacy and quality of new medicines and therapeutic interventions. However, conventional clinical trials are often associated with high cost, prolonged duration, complex protocols, difficulties in patient recruitment, large volumes of data and challenges in monitoring. Artificial Intelligence (AI), including machine learning, deep learning, natural language processing and large language models, has emerged as a promising technology for improving different stages of clinical trials. AI can assist in protocol design, patient identification and recruitment, eligibility screening, trial-site selection, data management, patient retention, safety monitoring and prediction of clinical outcomes. Recent research indicates that AI-based approaches may improve the efficiency of recruitment and clinical-trial risk assessment, although their performance depends strongly on data quality, validation and appropriate implementation. Regulatory agencies such as the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) are increasingly developing frameworks for responsible use of AI in medicines development. Despite its potential, AI implementation is associated with challenges including algorithmic bias, data privacy, lack of transparency, interoperability, regulatory uncertainty and the need for human oversight. This review discusses the major applications, advantages, limitations, regulatory considerations and future perspectives of AI in clinical trials.

Introduction

Clinical trials are systematic studies conducted on human participants to evaluate the safety, efficacy, and clinical benefits of medicines, vaccines, medical devices, and other interventions. They are essential for evidence-based medicine and drug development. However, traditional clinical trials face challenges such as high costs, lengthy timelines, complex protocols, patient recruitment difficulties, and extensive data management requirements.

Artificial Intelligence (AI), including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision, and Large Language Models (LLMs), has emerged as a promising technology for improving clinical research. AI can support different stages of clinical trials, including study design, patient recruitment, eligibility screening, monitoring, safety assessment, and data analysis.

Clinical trials generally progress through five stages: Phase 0 (microdosing), Phase I (safety and dosage), Phase II (efficacy and safety), Phase III (confirmation of efficacy), and Phase IV (post-marketing surveillance). AI technologies can be integrated into these phases to improve research efficiency and accuracy.

Major applications of AI in clinical trials include:

• Clinical Trial Design: Optimizing protocols, endpoints, sample sizes, and trial-site selection.

• Patient Recruitment and Selection: Identifying suitable participants through electronic health records and predictive algorithms.

• Eligibility Screening: Automating the comparison of patient records with inclusion and exclusion criteria.

• Patient Retention: Predicting dropout risks and supporting participant adherence.

• Clinical Trial Monitoring: Detecting protocol deviations, missing data, and unusual patterns.

• Safety Monitoring: Identifying potential adverse drug reactions and safety signals.

• Data Management: Automating data cleaning, classification, extraction, and integration.

• Digital Biomarkers: Analysing wearable-device and sensor data to monitor physiological and behavioural changes.

• Digital Twins: Simulating individual patient outcomes to support trial design and treatment prediction.

• Clinical Endpoint Assessment: Improving the consistency and objectivity of medical image analysis and outcome evaluation.

AI-based clinical trials offer several advantages, including faster patient recruitment, reduced manual workload, improved data quality, enhanced risk prediction, efficient monitoring, shorter timelines, and potential cost savings. They may also support personalized and adaptive clinical research.

Despite these benefits, AI implementation faces challenges such as poor data quality, algorithmic bias, lack of transparency, privacy and cybersecurity concerns, interoperability issues, regulatory uncertainty, and limited prospective validation. Therefore, human oversight remains essential in clinical and regulatory decision-making.

Ethical considerations include informed consent, patient privacy, data ownership, fairness, transparency, accountability, explainability, and protection of vulnerable populations. Regulatory authorities such as the FDA and EMA are developing frameworks emphasizing risk-based validation, responsible AI practices, and human supervision.

In the future, clinical trials are expected to increasingly integrate generative AI, multimodal AI, digital twins, real-world data, wearable technologies, digital biomarkers, adaptive trial designs, and predictive analytics.

Conclusion

Artificial Intelligence is becoming an important technology in modern clinical research. It can support clinical-trial design, patient recruitment, eligibility screening, site selection, patient retention, data management, safety monitoring and endpoint assessment. The technology has the potential to make clinical trials more efficient, data-driven and patient-centred. However, successful implementation requires high-quality data, robust validation, transparency, privacy protection, appropriate regulatory oversight and continuous human supervision. AI should therefore be considered a supportive technology rather than a replacement for clinical investigators and scientific judgment. With responsible implementation and appropriate validation, AI may significantly contribute to the future development of safer, faster and more efficient medicines.

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Copyright

Copyright © 2026 Anisha Dhuldev Sarje, Ashwini Aba Sargar, Snehal Govind Khatkale, B. S. Karande, A. B. Waghmode. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.