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Inductive reasoning with large language models: a simulated randomized controlled trial for epilepsy.

Daniel M Goldenholz^1,2, Shira R Goldenholz², Sara Habib^1,2

¹Department of Neurology, Harvard Medical School, Boston USA.

Medrxiv : the Preprint Server for Health Sciences

|April 2, 2024

Summary

This summary is machine-generated.

Large language models (LLMs) can accurately simulate and analyze clinical trials, mirroring human performance in evaluating drug efficacy and patient symptoms for epilepsy treatment.

Keywords:

artificial intelligence epilepsy large language models randomized clinical trials

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Area of Science:

Artificial Intelligence in Clinical Research
Medical Data Analysis and Simulation
Neurology and Clinical Trials

Background:

Analyzing electronic medical records at scale presents significant challenges due to data limitations and biases.
Foundational large language models (LLMs) offer a potential solution to overcome current limitations in clinical data analysis.

Conclusions:

LLMs show significant potential for accurately simulating and analyzing clinical trials, reconstructing essential trial components.
This approach offers a scalable and efficient alternative to traditional data mining methods in clinical research.
LLMs can identify treatment effects and patient-reported outcomes effectively, without requiring specialized medical language training.