Using AI and ML-based applications in clinical trials will reduce the gap between the existing clinical processes and the desired accuracy for more efficient and safer operation standards.
FREMONT, CA: Clinical technologies are critical to the examination and diagnosis of diseases. Professionals working in laboratory setups have conventionally been involved in numerous manual processes. As a result, it is always a challenge to maintain precision, which is critical as the professionals are dealing with human health and conditions. Thus a swift transition to automated systems will facilitate the clinical trials while also offsetting the time and money required for the purpose.
Artificial intelligence (AI) and machine learning (ML) have dug deep inroads into several industries. They also have potentially numerous use cases that are yet to be leveraged for enhancing designs and executions of the clinical trials.
AI and ML
With enhancements in the capabilities of big data and computing power, AI is moving toward more sophisticated and practical applications. It is increasingly being used to identify insights and trends due to its ability to derive meaning by connecting the dots. For instance, serious adverse event (SAE) reconciliation or site initiation in a specific therapeutic area, study design, or disease indication.
Thus AI model evolution with inherent ML allows it to unlock the hidden value within and across data sets. The clinical researchers can leverage AI’s ability to find relationships across data and its resulting use cases that utilize data across and within their sources.
External data sources delve into public and subscription data while internal data sources are affiliate data stored in clinical systems such as electronic data capture (EDC), clinical trial management system (CTMS), electronic patient-reported outcome (ePRO), and laboratory information management system/ laboratory notebooks (LIMS/LN).
After the identification of the potential interventions, such as new technologies through the clinical study model, the point of sale (POS) of the possible intervention with the help of machine learning powered search can be accessed to recognize the trends and insights from the real world data.
The goal of AI and ML-based applications in clinical trials will be to reduce the gap between the existing clinical processes and the desired accuracy for more efficient and safer operation standards.
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