What Lies Ahead? Artificial Intelligence (AI) and Clinical Trials

By: Rich Wzorek   Director, New Products and Services

By: Cheryl Kole Vice President, Solution Strategy and Commercialization

By: Jeremy Jakubowski Director, Technical Strategy and Delivery

The use of AI in clinical trials is a popular topic in the current global environment, but it is not the click-of-a-button solution that it appears. AI requires human input for it to be effective. A framework must be developed and the AI model needs to be trained to do the task as intended. Most importantly, AI should be used as a tool, not the end-all and be-all to the problem looking to be solved.

What to consider when using AI?

AI models need a human component so that it can provide the information being looked for:

• Setting the stage: What do you want the AI to accomplish?
• Determine the types of data to provide the AI for it to function properly.
• Monitor feedback to ensure the AI is kept up to date.
• Detect bias to ensure responses are not inappropriately skewed.

Training is required for end users to properly interact with the AI tool:

• Stakeholders need to understand the capabilities, limitations, and implications of using AI.
• How does the end user know if the insights being provided are accurate, relevant, and useful?

Users must consider ethical considerations of using AI:

• AI requires human insight to govern and police it to ensure proper use and avoid improper exploitation.
• How does the end user know if the insights being provided are accurate, relevant, and useful?

When Using AI; What are the Security and Privacy considerations?

• It is important to establish trust between the end users and the AI. Business users need to trust that the AI operation is reliable, secure, private, and transparent.
• The AI needs to safeguard commercially sensitive information and protect classifications of data.
• Stakeholders may need to be cautious with their queries as some AI tools store previous inputs unless deleted by the end user or enterprise settings.
• Ensure that all parties involved develop a set of acceptable terms and conditions for the use of AI.

How does AI impact clinical trials?

AI can aid clinical trials in several ways:

Data Processing and Data Analysis

• Clinical trials generate a massive amount of data. AI algorithms can analyze this data more quickly and accurately than humans, identifying patterns and trends that might be missed by the human eye. This can lead to more accurate results and faster conclusions.

Patient Recruitment

• One of the biggest challenges in conducting clinical trials is finding suitable participants. AI can analyze vast amounts of data from electronic health records (EHRs), genetic databases, and other data sources to identify potential candidates who meet the specific criteria for a trial.  This can speed up the recruitment process.

Monitoring and Safety

• AI can continuously monitor trial data for compliance and safety issues, providing real time alerts for any deviations or suspected adverse events.

How can AI be used in eClinical solutions?

We are seeing an increase in use of AI in eClinical software, such as IRT (Interactive Response Technology), to enhance various aspects of clinical trial management:

• Natural Language Processing (NLP): AI-powered NLP can assist in extracting relevant information from unstructured data sources. This can streamline the documentation process and improve accuracy.
• Virtual Assistants and Chatbots: these can aid in engaging with end users to provide information and answer questions. This enhances the user experience and speeds resolution to queries on system use.
• Analytics: AI can analyze these systems’ large data sets and provide trends, patterns, insights, and predictions that aid in informed decision making.

The Future of AI in Clinical Trials

As AI continues to evolve, its impact on clinical trials is likely to grow. We may see AI being used to design trials, predict outcomes, and even determine the optimal dosage of a drug. However, the use of AI in clinical trials also raises ethical and regulatory questions. For instance, how do we ensure the privacy and security of patient data? How do we validate the accuracy of AI algorithms? These issues must be addressed as we move forward.