Clinical Trials are increasingly becoming a research-dependent process that produces a growing amount of data. In general, there is no subtle alternative to this lengthy and laborious approach. However, artificial intelligence can improve its effectiveness by offering acceptable alternatives and improvisations by evaluating and drawing conclusions from data. Using artificial intelligence, a clinical research organization like Veristat can select appropriate patients without compromising their health-related information, monitor real-time updates including the help of many other specific devices, and keep a track record of drug or device usage.
By tracking the patient’s pre-trial state and post-trial symptoms, AI algorithms should make the most of the data. Real-time Veristat safety monitoring is accomplished during the trials by utilizing wearable sensors. Real-time safety tracking and effect monitoring with an accurate assessment of the hazards is possible when employing wearable sensors during clinical trials. Additionally, all this can be automatically recorded by the AI-based system linked to the devices continually throughout the trial period and a few hours after the drug is administered whenever they identify a change in body temperature or pulse, etc. In the event of additional study and analysis, such recorded data can be quickly transported across the system with fewer risks of discrepancy.
The AI programs can then suggest acceptable sites and sites for testing based on the local geography and climate. Advanced analytics systems can integrate with systems such as maps and population count data to deliver the best options for a proper site for conducting clinical tests by a search post-activation. Sites with a sufficient number of professionals to meet the patients, available resources, and emergency facilities are preferred.
Clinical trial design
This is significantly altering the dynamics of conventional trials, which were conducted with the scant data that was available. Artificial intelligence can gather and use information from a variety of sources. The trial designs are refueled by research and scientific data from prior trials, patient support initiatives, case studies of unique medical circumstances, etc. Regardless of the failure or success rate of previous experiments, AI-enabled technologies and systems possess a steadfast ability to gather, organize, and evaluate the data produced by those trials. These algorithms can determine the trial timings and settings that are most appropriate. Additionally, intelligent systems can aid in overcoming the shortcomings of earlier studies.
Improved patient selection
AI-based algorithms that search through the available database can enhance patient selection and increase the efficacy of the trials. Before deciding if a person is fit for clinical trials, this should be done by assessing numerous data sources readily accessible with digital health records, diagnostic imaging, and also omics data. Additionally, study systems based on artificial intelligence can use its algorithms to identify the best group of participants for trials. That aids in lowering population heterogeneity, which is necessary to lower variability beforehand and boost study power. AI systems are capable of performing predictive enrichment, which helps in determining the patients most likely to benefit from treatment, as well as prognostic enrichment, which selects patients with observable clinical endpoints so that only the most qualified patients participate in the trials per the criteria.
AI-enabled data collecting and management can speed up the medication development process and assist businesses in bringing novel therapies to market more quickly by lowering the time and effort needed for clinical trials.