AI/ML in Clinical Trials

The integration of artificial intelligence (AI) and machine learning (ML) into healthcare and clinical research has raised important regulatory considerations. Below are official guidance documents issued by the U.S. Food and Drug Administration (FDA) outlining their approach to AI/ML in medical and clinical settings:

🔹 2018 – FDA Discussion Paper

Artificial Intelligence and Machine Learning in Software as a Medical Device

This foundational document provides early insights into how the FDA views AI/ML technologies used in medical devices, including expectations around safety, transparency, and continuous learning systems.

👉 Read the paper (PDF)

Update (May 2025)

🔹 2025 – FDA Draft Guidance

Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products

This newly released draft outlines how AI/ML may be used to support regulatory decisions in clinical trials and drug development. It introduces a risk-based framework for assessing model credibility, focusing on transparency, context of use, and data quality.

👉 Read the draft guidance

References:

🔹 2018 – Peer-Reviewed Article

Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States

This article analyzes the state of AI regulation in the context of medical device development and strategies to ensure AI applications are safe and effective. It examines legal frameworks and ethical considerations pertinent to AI in radiology.

👉 Read the article

🔹 2017 – Research Study

Deep learning architectures for multi-label classification of intelligent health risk prediction

This study explores the application of deep neural networks to predict multiple chronic diseases using physical examination data, highlighting the potential of deep learning in multi-label health risk classification.

👉 Read the article

 🔹 2018 – Philosophical Perspective

Governing artificial intelligence: ethical, legal and technical opportunities and challenges

This article provides a cross-sector view of AI governance, including regulatory tensions, transparency, and the role of public institutions. Though not specific to clinical trials, it offers useful context on how societies may govern AI development responsibly.

👉 Read the article

🔹 2018 – Overview Article

Big Data Analytics in Medicine and Healthcare

This article provides a foundational overview of how big data analytics is applied in medicine and healthcare, discussing key characteristics and various applications that enhance decision-making and patient care.

👉 Read the article