Clinical trials are vital for developing drugs, and so is artificial intelligence’s (AI) potential. In addition, its prospects in clinical studies are significant. Although it’s widely acknowledged that technological advancement has the potential to help overcome the obstacles presented by clinical trials, the industry as a whole has been rather slow to adopt innovations.
Nevertheless, the use of AI in clinical trials isn’t a new concept. Moreover, discussions regarding how to adopt new developments and the influence they have on trials have been going on for years.
On the other hand, AI is the science of creating computer programs and technology that simulates human intelligence. By using AI tools, unstructured text, images, and sounds can be understood more efficiently.
Explore how AI and technology can speed up and improve clinical trials:
Data Aggregation From All Sources
Trials produce a massive amount of data, and it frequently comes from a variety of different systems. Hence, it’s difficult for researchers to combine it in a way that enables comprehensive analysis across several different locations around the world. A self-learning AI system can help with predictive analytics, efficiency, and dependability beyond aggregating and organizing data.
AI’s ability to integrate data from all sources onto an open-data analytics cloud platform can promote collaborative efforts and deliver insights across important parameters. By using AI technology, data aggregation from all sources is now possible, especially with advanced software solutions like those from Formedix and others.
Evaluation Of Risks
Analyzing site risks and creating ‘action items’ to mitigate them takes plenty of human work. AI and machine learning (ML) can relieve these challenges by analyzing the risk environment and providing predictive analytics for better clinical monitoring insights.
Meanwhile, advanced analytics enables site rankings for holistic risk evaluations, enabling more detailed risk detection and the eradication of false positives. As a result, the composite evaluation of site risks across the study produces a straightforward result showing high-risk locations, key risk indicators (KRIs), and site risk ranking.
Furthermore, AI can also be used to proactively identify sites with recruitment and performance concerns or patients at higher risk for adverse events. With this knowledge, researchers can respond faster and prevent complications.
Helps With Patient Recruitment
The successful completion of a clinical trial relies on several factors, one of which is recruitment. On the other hand, the failure of some clinical studies is often due to a lack of participants or a high number of dropouts. Nevertheless, the right technologies can boost a company’s competitive advantage and save a failed trial.
AI, robots, and other technologies are improving clinical trial performance. Clinical trial automation has many benefits. These include:
- Clinical trial recruiting might be long. Organizers arrange and coordinate resources, documentation, and interactions with potential participants during the recruitment and selection process. The use of automation results in recruitment time, financial, and resource savings.
Most spend time, funds, and resources on clinical trial recruitment just to identify the right people. AI can search patient forums to assess if a medical concern is widespread in specific areas.
Significantly, social media analysis is one of the most advanced AI patient identification techniques. This method could help organizations design better clinical trials by accelerating cohort identification.
- Tracking and compliance are both made easier by automation. After selecting the best trial participants, an organization must track and assess the selection process. Automation software can help with this process, offering insights for future recruitment.
Improve Patients’ Engagement
AI could be used more in clinical studies for patient engagement. AI can greatly improve the experience for patients who sign up and participate in clinical research by assigning them a health care professional who will treat them throughout the trial.
This could be visiting a doctor to discuss their ailment, attending group sessions led by a clinician, or attending workshops to learn about specific condition treatment methods.
Clinical Trial Overall Costs Reduction
There’s a likelihood that the cost of clinical trials could be reduced. This can be done by utilizing data-driven protocols and strategies that are powered by advanced algorithms. These advanced algorithms, on the other hand, will process the data obtained from sensing devices and apps, electronic medical and administrative records, and other equipment and sources.
In addition, these algorithms enhance the quality of the data collected. Hence, they boost patient compliance and retention rates. Furthermore, they can determine the treatment’s effectiveness in a more time- and resource-effective manner than ever before.
These ideas are just a fraction of what’s possible with AI in healthcare. Cooperation between the public and private sectors is crucial to realizing the potential.
Conclusion
AI could be a gamechanger in personalized medicine. The potential improvements in creating novel treatments, as well as the revenue-generating potential for AI software companies, will likely form the right mix for AI’s continuous integration into clinical trials.
