Artificial intelligence and machine learning in clinical development: a translational perspective.

Area of Science:

• Computational biology and bioinformatics
• Clinical trial methodology
• Regulatory science

Background:

• Clinical development is undergoing a significant transformation driven by the convergence of digital data, advanced computing power, and regulatory adaptation.
• Artificial intelligence (AI) and machine learning (ML) algorithms are key to identifying meaningful patterns in large datasets.
• Collaboration between academia, industry, regulators, and technology sectors is crucial for this evolution.

Purpose of the Study:

• To summarize insights, developments, and recommendations for integrating computational evidence into clinical development and healthcare.
• To outline strategies for modernizing clinical development using AI/ML and secure computing.
• To discuss the impact of digital algorithmic evidence on improving patient medical care.

Main Methods:

• Analysis of publicly available biomedical and clinical trial datasets.
• Utilizing real-world evidence from sensors and health records.
• Application of machine learning architectures for data analysis and pattern identification.

Main Results:

• Identification of key trends and challenges in adopting computational evidence.
• Development of strategies for integrating AI/ML into clinical development workflows.
• Outlining of new regulatory pathways for digital health technologies.

Conclusions:

• The integration of AI and ML offers a powerful approach to enhance clinical development and healthcare outcomes.
• Actionable computational evidence is essential for future medical advancements.
• Regulatory bodies are increasingly supportive of innovative digital methods in drug development and patient care.