CLINICAL RESEARCH USING AI MACHINE LEARNING

AI and machine learning tools have emerged as modern clinical research instruments that enhance data performancemeasures and develop individualized healthcare and predictive system models. Standard medical trials generateelevated expenses, take extensive time to complete, and struggle to find sufficient research participants. The processof AI-driven analytical model development achieves better research practice by selecting candidates while breakinglarge datasets to enhance trial outcome predictions. Advanced medical diagnostic patterns can be made detectable bymachine learning tools that improve healthcare treatment results and diagnostic accuracy. The elimination ofconventional trial-and-error methods makes AI-based biochemistry modeling and adverse drug reaction forecastingspeed up medical research. The ability to process unstructured medical documentation, especially EHRs containingclinical notes and scientific publications, is enabled by NLP technology, thus making research more efficient. AIsystems enable regulators to evaluate diverse populations of patient data during evidence-generation processes anduse these findings to make decisions. Although artificial intelligence brings many advantages to the field, it facesessential ethical issues stemming from imprisoned data, system result bias, and the inability to understand systemdecision processes. Obtaining clinical and regulatory approval for AI models requires a straightforward interpretationof their functioning. Additionally, the reliance on high-quality datasets necessitates robust data governanceframeworks. The advancement of Py coin depends on technological development, which results in enhanced clinicalresearch execution using accurate methods and efficient procedures accessible to a broader range of researchparticipants. The clinical implementation of AI drives a total healthcare change based on data, leading to improvedpatient outcomes while pushing medical science ahead