AI in Clinical Research: Four (4) Listener Questions

Inclusion Criteria: a Clinical Research podcast

John Reites and Jeremy Franz delve into the practical applications of AI in clinical research, focusing on large language models (LLMs), how to select the right model for specific tasks, and the challenges posed by AI hallucinations. They address common questions from listeners, providing insights into the workings of LLMs, the importance of model testing, and strategies to ensure data integrity in clinical trials.
Key insights include:
(1) There's a growing interest in AI applications in clinical research
(2) LLMs are trained to predict the next word based on massive data sets
(3) Choosing the right AI model requires testing for specific tasks
(4) The most advanced model isn't always the best for every task
(5) Hallucinations in AI can lead to incorrect data outputs
(6) Validation steps are crucial to ensure AI outputs are accurate
(7) AI models can be overconfident in their responses
(8) Maintaining data integrity is essential in clinical research
(9) AI should reference actual data to avoid hallucinations
(10) Continuous learning and adaptation are necessary in AI development

Inclusion Criteria is created, produced, and hosted by John Reites.
© 2025 Inclusion Criteria: a Clinical Research podcast. All rights reserved.

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The views and opinions expressed by John Reites and guests are provided for informational purposes only. Nothing discussed constitutes medical, legal, regulatory, or financial advice.

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