Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision...

Area of Science:

• Computational biology
• Biomedical informatics
• Regulatory science

Background:

• Precision medicine aims to tailor treatments using individual variability.
• Artificial intelligence (AI) and machine learning (ML) offer powerful tools for analyzing complex health data.
• Technological advancements present both opportunities and challenges for AI in healthcare.

Purpose of the Study:

• To discuss the goals and applications of AI in precision medicine.
• To identify challenges associated with AI implementation in healthcare.
• To propose strategies for trustworthy AI and data protection in precision medicine.

Main Methods:

• Review of innovative AI applications in precision medicine from a 2023 FDA workshop.
• Discussion of challenges in AI, focusing on trustworthiness.
• Exploration of privacy-preserving techniques like differential privacy, synthetic data, and federated learning.
• Consideration of risk-based management and agile regulatory frameworks.

Main Results:

• AI/ML applications show promise in predicting tumor growth, identifying biomarkers, and stratifying patient populations.
• Key challenges include ensuring AI trustworthiness and protecting sensitive data.
• Differential privacy, synthetic data generation, and federated learning are viable strategies for data protection.
• A risk-based management approach and agile regulatory ecosystem are crucial for AI integration.

Conclusions:

• AI and ML are vital for advancing precision medicine.
• Addressing AI trustworthiness and data privacy is paramount.
• Collaborative efforts, including data sharing and regulatory guidance, are essential for the responsible advancement of AI in precision medicine.