Unleashing the Potential of AI and Automation in Clinical Trials

As per the research and analysis, the average clinical trial process lasts from 7 to 12 years, with studies of costs ranging from approx. $161M to $2.6B per drug. Above all, only 15% of clinical trial studies are successful and only 1 out of 10 drugs that enter Phase1 ends up being approved by the FDA.

On the other hand, the creation of new drugs is quite slow and expensive. So, these days, people are using computation techniques to accelerate the process of drug discovery pipeline to save the rest of the time.

Still, the clinical trials remain slow, expensive, and tedious. This stage of the clinical trial system hampers not only the quick development of new medicines but also the reproduction of the existing ones which is much more important because existing drugs have already gone through multiple stages for receiving the approval from the FDA and are safe to apply and consume. Thus using them to treat other diseases is considered to be more promising than developing new drugs from scratch.

Moreover, leveraging existing drugs for off-label usage is identified for some drugs, and additional clinical trials are performed to confirm the hypothesis and fetch more reliable information on efficacy and preferred dosage. Hence, the process remains longer and more tedious.

Thanks to our modern medical databases, we can easily get enough information to identify prospective off-label usages and avoid additional clinical studies to stretch the process further. Additionally, we now have diverse real-world data such as –

• Patient Electronic Health Records
• Patient Surveys
• Insurance Claims
• Bills and
• Prescriptions for Buying Medicines, etc.

This data is linked to the patient profiles to find out hidden correlations between on-label drug usage and their off-label influence on various comorbidities. Clearly, implementing Artificial Intelligence in Clinical Trials is an ideal job. Using AI in Clinical Trials helps to accomplish various tasks in the medical sector and facilitates further work and procedures to go smoothly.

Understanding Artificial Intelligence (AI) and How It Brings Automation in Clinical Trials!

Artificial Intelligence is the new technology that helps detect human wisdom or emotions using machines and empowers human problem-solving abilities using various algorithms.

The capabilities of machine learning and deep learning added to any system brings automation in which AI performs any tasks at their best by getting the relevant information from unstructured data, images, and texts in the system inputs and leveraging them to create the magic.

AI in Clinical Trials

Whether it’s about healthcare or any other industry, research and invention always play a crucial role in the dynamics of any field. However, in medical sciences, such research and clinical studies can help decide which drugs and treatments are good for the patients and to cure their diseases.

Hence, from the identification of drug tests to the repurposing of old drugs and all other phases, it’s preferable to use Artificial Intelligence in clinical studies to accelerate and enhance the entire process. Not just using the AI in clinical trials would be great to improve the quality of data usage, but it’ll also be great to keep a track of the journey of the patients and drug success in real-time. AI algorithms merged with an outstanding digital infrastructure can ensure more accurate and relevant data from clinical trials. In addition, clinical trial systems can also lower the impact of manual errors during the data collection and ensure seamless integration with other databases.

Let’s have a look at how using AI in Clinical Trials can make an impact –

1. Clinical Trial System Design

Many healthcare companies and pharmacies are adopting a wide range of strategies to innovate the clinical trial system design. For example – by increasing the amount of scientific and research data fetched from the current and past clinical trials, patient support programs, as well as post-market surveillance, the clinical trial system design can be improvised.

Hence, AI-enabled technologies have an unmatched potential to collect, organize, and analyze the data generated through clinical trials.

2. Patient Enrolment

Implementing clinical trials machine learning algorithms can enhance the patient selection criteria and increase the effectiveness of clinical studies, through mining, analysis, and interpretation of multiple data sources such as electronic health records (EHRs), etc.

3. Site Selection

One of the key aspects of clinical research is to identify high-functioning investigator sites depending on their quality and unique aspects such as –

• Administrative procedures
• Resource availability
• Experienced clinicians, and
• The ability to detect the disease quickly and efficiently.

These all can influence the research timelines, data quality, and integrity.

Using AI in clinical trials can help pharmaceutical companies to identify their target geo-locations, qualified investigators, and priority candidates. It will also help in collecting and collating evidence to satisfy regulators that the trial process meets all relevant Clinical Practice requirements.

4. Patient Monitoring

Leveraging the power of Artificial Intelligence in Clinical Trials can help monitor and manage patients by automating the entire process of data capture, digitalizing standard clinical assessments, and sharing data across systems. On the other hand, in Clinical trials machine learning algorithms combined with wearable technology can also streamline the = patient monitoring with real-time insights.

5. AI-driven clinical trial analytics

Through clinical trials, companies generate a lot of immense operational data but the functional data silos and disparate systems can still trouble them from having a comprehensive view of their data used over multiple global sites.

Incorporating AI and Automation in Clinical Trials can help them improve predictions and prescriptions over time along with effective data visualization to proactively deliver reliable analytics insights to their target users.

Conclusion

In the future, the use of AI-enabled clinical trial systems and patient support platforms will continue to revolutionize clinical research with unmatched results.

All stakeholders and investors involved in the clinical trial studies will align their decisions as per the patient’s needs. If you’re looking to empower your clinical trial system with AI, connect with us and learn what benefits you can have. Connect with us to get a deeper understanding of AI-driven clinical trials straight from the experts.