Bringing innovative therapies to patients is a complex and time-consuming process. However, Clinical trials are getting notoriously more time-consuming and costly. But artificial intelligence (AI) is changing the game, offering a promising solution to these age-old clinical trial challenges.
A recent study by Nature Digital Medicine revealed that AI-powered patient recruitment can slash clinical trial costs by 70% and expedite timelines by up to 40%. Beyond recruitment, AI is transforming clinical data management, driving faster trial completion and quicker access to life-changing therapies.
Overcoming Inefficiencies with AI in Clinical Trials
Inefficiencies in data management are a significant hurdle in clinical trials, leading to costly delays. The manual analysis of vast amounts of clinical data is not only time-consuming but also prone to errors. This is where AI comes into play, transforming data management and streamlining the entire trial process.
Accelerating Study Setup
AI significantly accelerates the study setup phase, reducing months to days. For example, AI automates tasks such as protocol writing and identifying suitable demographics for trials. From creating custom CRFs to analyzing existing ones, AI efficiently handles various aspects of initial study setup requirements.
Enhancing Predictive Analytics in Clinical Trials
Predictive analytics helps to make real-time decisions during the execution of clinical trials. It can study the pattern of these roadblocks and predict their occurrence in the future. This helps in mitigation of most risks or side effects and allows stakeholders to plan recruitment and trial design according to the prediction, preventing expensive delays and mid-study changes.
Revolutionizing Clinical Trial Medical Coding With AI
Medical coding is a crucial part of clinical trials and is a time-consuming process. Enter AI, and now hours worth of coding can now be accomplished in a matter of minutes. AI medical coding algorithms can also be trained with millions of biomedical terms for medical context learning using machine learning models.
Automating Remote Source Data Verification (rSDV)
One of AI’s biggest contributions to clinical trials is remote Source Data Verification. Site monitoring is an expensive and time consuming process. CRAs can now verify source data without travelling to the sites. Sites can upload source data to the AI engine, which extracts this data and compares it with data entered into the EDC. Matched data items are marked as ‘SDV’. The system automatically generates queries on unmatched data and assigns to sites.
Harmonizing Real World Data (RWD) in Clinical Trials
AI plays a crucial role in the analysis of Real World Data (RWD) and harmonization of data. With the growing importance of RWD in clinical trials, the harmonization of the same is also becoming a need of the hour. Artificial intelligence automates processes like data cleaning and offers next gen features like NLP and pattern recognition which helps in making most of the large data sets that are created in the process of clinical trials.
Beyond Cost-Cutting: Additional Benefits of AI in Clinical Trials
The implementation of AI in clinical trials is not only reducing the cost; it has its impact on many other areas of clinical trials as well. From optimizing drug doses to identifying treatment protocols, AI has changed the clinical trial scene positively. With reduced errors and personalized treatment allocation to patients, AI contributes to accelerated trials at reduced cost.
Clinion is the industry’s first AI-enabled eClinical platform that ensures faster, accurate and affordable clinical trials ensuring life-saving treatments reach the patients quickly. Our platform is a powerful amalgamation of technology and healthcare promising a revolution in the life science segment.
