APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN CLINICAL TRIALS AND DRUG RESPONSE PREDICTION

Artificial intelligence (AI) has emerged as a game-changing tool in clinical research and medication development, providing novel techniques to improve the efficiency, accuracy, and personalisation of therapeutic decision-making. Its applications in clinical trials, such as patient identification and recruitment, eligibility assessment, trial design, data management, safety monitoring, endpoint evaluation, and treatment outcome prediction, have the potential to overcome several limitations of traditional clinical research methods. Specifically, AI-driven analysis of clinical, genetic, imaging, and real-world data can enhance patient classification and identification of factors associated with therapy response. Drug response prediction is one of the most promising AI applications in precision medicine. Machine learning and deep learning techniques can combine various biological and clinical factors to find complex patterns related to therapeutic efficacy, toxicity, resistance, and inter-individual variability.⁴,⁵ These approaches may aid in the selection of relevant patient populations, prediction of treatment results, identification of patients at high risk of adverse events, and optimisation of individualised treatment plans. The combination of AI, pharmacogenomics, multi-omics, and real-world data may aid in the creation of personalised medicinal methods. Despite these potential benefits, various obstacles must be overcome before AI may be widely used in clinical trials and drug development. Data quality and heterogeneity, dataset representativeness, algorithmic bias, interpretability, data privacy, cybersecurity, and external validation challenges can all have an impact on AI model dependability and generalisability.⁹-¹¹ Importantly, excellent performance in retrospective datasets does not always indicate clinical success. Establishing clinical utility requires prospective validation, standardised reporting, transparent model construction, and continuous assessment of model performance.9 As AI-based systems become more integrated into clinical decision-making and medication research, regulatory and ethical concerns will become increasingly essential. Appropriate governance structures are essential to promote openness, accountability, patient safety, data security, and equal performance across populations.¹³,¹⁴ As a result, AI should be seen as a tool to supplement rather than replace clinical skill, pharmaceutical knowledge, or established scientific procedures. Overall, incorporating AI into clinical trials and medication response prediction has enormous potential for improving patient selection, accelerating drug development, improving therapeutic outcome prediction, and enabling more individualised treatment methods. Future research should focus on developing interpretable, reproducible, robust, and clinically validated AI models that can integrate multimodal clinical and biological data. AI has the potential to significantly contribute to a more efficient, safer, and personalised drug development paradigm if validated, regulated, and implemented ethically.10

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