Optimize your clinical studies
and clinical trial centers visits with AI

Clinical trials, reaching $44 billion in 2024 and estimated at $69 billion in 2029, are growing at an annual rate of over 5.9%.

This growth is driven by the search for new products in the face of generics and high R&D costs, and the expansion of early drug development services facilitates access for small and medium-sized companies.

Artificial intelligence (AI) has the potential to revolutionize the way clinical trial center (CTC) visits are conducted during clinical trials. Here are some concrete examples of how AI can optimize CRA visits:

1. Optimized visit planning and scheduling:

Intelligent appointment scheduling system: an AI system can analyze historical patient and clinical trial center data to identify patterns and optimize visit scheduling. This can include taking into account factors such as patient availability, travel times and Clinical trial center workloads, to minimize waiting times and optimize resource utilization.

Prediction of cancellations and delays: AI can analyze past data to identify patients and clinical trial centers that are more likely to cancel or delay appointments. This enables clinical trial centers to take proactive measures, such as reconfirming appointments or proposing alternative time slots, to minimize disruption and maximize study program efficiency.

2. Enhanced data collection and analysis:

Electronic assessment forms (ePRO): AI-based ePROs can guide patients
through assessment questionnaires, asking relevant questions and tailoring
the experience based on their answers. This can improve the accuracy
and completeness of collected data, reducing the burden on patients
and investigators.

Automatic Data Extraction (AED): AED can be used to automatically
extract key information from clinical notes, medical records and other
textual data sources. This can automate tedious and error-prone tasks,
allowing investigators to concentrate on more in-depth analysis
and patient interaction.

3. Optimized patient follow-up and monitoring:

Remote monitoring tools: wearable devices and connected sensors can be used to collect real-time data on patients' health, such as heart rate, blood pressure and physical activity. AI can analyze this data to identify potential anomalies and alert investigators to problems requiring immediate attention.

Chatbots and virtual assistants: AI-based chatbots and virtual assistants can provide patients with 24/7 support and information. This can improve patient adherence to treatment, address their questions and concerns, and identify potential problems before they escalate.

4. Improved communication
and collaboration :

Machine translation: AI can translate conversations between patients,
investigators and other study team members in real time, facilitating
communication and collaboration in international teams.

Automatic summary tools: AI can generate concise summaries of
medical records, test results and other complex data, enabling
investigators to make rapid, informed decisions. The use of AI in
clinical trials is still in its infancy, but the potential for optimizing
clinical trial center visits and improving overall trial efficiency is immense.
As technology continues to evolve, we can expect to see even more
innovative applications of AI that will transform the way clinical research is conducted.

Revolutionizing clinical studies with AI:
Optimization, Efficiency and Ethics

Importantly, the use of AI in clinical studies also raises important
ethical and regulatory issues that need to be carefully considered.
It is essential to ensure that patient data is protected, that AI
systems are transparent and explainable, and that algorithms are not
biased or discriminatory.

At iRevolution, we've been developing solutions to manage clinical
studies for years. We have also set up healthcare
AI infrastructures that can be adapted to optimize clinical studies.

The use of AI promises significant optimization of clinical studies, while raising ethical and regulatory issues. At iRevolution, we develop AI solutions to improve the efficiency of clinical trials while ensuring data protection and system transparency.