Clinical trial designers are leveraging the power of AI and social media to streamline Veristat clinical trial design. This has been integral in transforming traditional clinical studies into modern clinical trials. But what exactly are the impacts of social media and AI on drug development? This article reveals the influence of artificial intelligence and social media in clinical trials. They’ve positively impacted, trial planning spreading awareness, and monitoring research processes.
The impact of social media on clinical trials
The use of social media in a clinical trial is today becoming a popular trend. Social media alleviates the shortcomings of conventional trials and makes modern clinical research super-efficient. For years, clinical trials have been struggling to find qualified patients. Social media makes it easier to reach out to qualified trial participants with targeted ads. Social ads allow researchers to target people based on age, location, gender, and other qualifying parameters to inform and easily acquire participants. Generally, social media allows clinical trials to save time on clinical trial planning, patent recruitment, and reaching potential patients in remote areas.
The impact of Artificial Intelligence (AI) on clinical trials
AI entails a variety of techniques: natural language learning (NLP), machine learning (ML), deep learning (DL), and optical character recognition. These elements work together to create data analysis algorithms and powerful mathematical models that extract essential details from sample data. These details are useful in making data-driven predictions and decisions in clinical trials. AI is powerful such that it’s increasingly finding its way in all stages of drug development. For example, it enhances patient selection during clinical trials and is being used to reduce population heterogeneity.
AI leverages large EMR (Electronic Medical Record) data from diverse formats, electronic phenotyping, and different levels of data accuracy to easily target the right patient population. Also, machine learning techniques enable prognostic enrichment. They enable trial managers to select patients with a higher likelihood of having an outcome of interest during the trial. Artificial intelligence enables predictive enrichment in clinical trials. This feature enables clinical trials to target patients that are likely to respond to treatment. This is possible because AI-powered programs can simultaneously analyze and compare many data sets.
Artificial intelligence and wearable technology in clinical trials
AI programs work together with wearable technology to streamline clinical trials. The combination of the two makes clinical trials more efficient. It enables remote and continuous participant monitoring tailored to each individual, which boosts patient engagement and facilitates reliable assessments of clinical processes. Investigators can collect and monitor data in real-time to make informed decisions quickly. For example, AI models can analyze data from video monitoring and wearable sensors to generate patient-specific disease diaries based on disease expression and behavioral changes. According to the National Library of Medicine, AI-based programs have once been used to determine the fewest doses necessary to reduce and shrink a brain tumor in simulated trials. Generally, the world of clinical trials increasingly embraces technology to streamline processes in research.
AI and social media are taking clinical trials to the next level. Leverage them to enjoy the efficiency and convenience of your clinical study.