Using AI to Improve Success Rates of Clinical Trials Through Advanced Patient Stratification Techniques

Abstract

Clinical investigations are harder and costlier, requiring creative recruiting and cohorting. Subgroup patients by therapy response for clinical trial success. Weak and essential factors make traditional patient selection fail. This causes resource loss, high dropout, and confused research. This study uses AI algorithms to stratify patients for clinical trial success. ML, DL, and NLP group patients, predict therapy responses, and customise medications. This work uses supervised, unsupervised, and reinforcement learning AI models to demonstrate how AI-driven patient stratification might enhance clinical trial outcomes and solve old issues. 

AI may enhance patient profiles by assessing enormous genetic, phenotypic, and clinical data. They show genetic predispositions, biomarkers, and treatment response. AI can classify patients to boost clinical trial SNR. Diversity reduces in experiments. AI models may choose the best patients and construct the experiment using EHRs, clinical trial registries, and omics data. Predicting patient response before research may speed up and minimise medication development costs. 

The findings suggests AI may help attract and retain clinical trial patients. A patient's treatment history, comorbidities, and genetic indications may assist AI models predict clinical trial participants' benefits. This customised patient monitoring technology may speed up the process and reduce patient dropout, improving retention and trial results. Early prediction of patient outcomes allows for more flexible trial designs that may be altered in real time based on patient responses, making the clinical trial framework more adaptive and responsive. 

AI may categorise patients and improve clinical trials. AI may enable adaptive trial designs alter treatment tactics and patient groups using real-time data. Constant AI feedback may enhance trial decisions. Trial efficiency and success increase. Secure patient data and real-time monitoring using AI, blockchain, and IoT. This would increase clinical trial reliability and efficiency. 

AI may enhance clinical studies, but implementation is hard. Data privacy, regulatory compliance, and AI model transparency must be addressed for widespread deployment. Medical assessments must be unbiased, but AI models are too simplistic. Clinical trial AI hazards may be reduced by explainable AI (XAI) models and tight data management.