Increasing acceptance of AI-generated digital twins through clinical trial applications

Area of Science:

• Biomedical Informatics
• Computational Biology
• Health Informatics

Background:

• Current medical treatment selection involves extensive trial and error, increasing costs and time.
• Advancements in artificial intelligence (AI) have not yet optimized treatment development, despite benefits in other fields.
• Rich historical clinical trial and real-world data are increasingly available.

Purpose of the Study:

• To explore the application of AI-generated digital twins for clinical trial participants.
• To advocate for the use of AI-generated digital twins in drug development due to a favorable regulatory outlook.
• To demonstrate how AI-generated digital twins can accelerate personalized medicine and improve healthcare outcomes.

Main Methods:

• Leveraging AI models with historical datasets to create holistic forecasts of individual patient health outcomes.
• Developing AI-generated digital twins representing clinical trial participants.
• Analyzing the regulatory landscape for AI in drug development.

Main Results:

• AI-generated digital twins can provide holistic forecasts of future health outcomes.
• The drug development sector presents an ideal environment for the safe implementation of AI-generated digital twins.
• This technology supports rapid in silico evaluation of intervention strategies.

Conclusions:

• AI-generated digital twins offer a pathway to making personalized medicine a reality.
• The regulatory environment in drug development supports the safe application of AI-generated digital twins in healthcare.
• Continued research and regulatory acceptance will drive trust and widespread adoption of AI-generated digital twins in clinical practice.