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Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
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Inductive reasoning with large language models: A simulated randomized controlled trial for epilepsy.

Daniel M Goldenholz¹, Shira R Goldenholz², Sara Habib¹

¹Department of Neurology, Harvard Medical School, Boston, USA; Department of Neurology, Beth Israel Deaconess Medical Center, Boston, USA.

Epilepsy Research

|February 28, 2025

Summary

This summary is machine-generated.

Artificial intelligence, specifically large language models, can effectively analyze simulated clinical trial data to determine drug efficacy and safety. This AI approach offers a scalable and efficient method for future clinical research, mirroring human analysis closely.

Keywords:

Artificial intelligence Epilepsy Large language models Randomized clinical trials

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Area of Science:

Artificial Intelligence in Clinical Research
Medical Data Synthesis
Natural Language Processing in Healthcare

Background:

Investigating artificial intelligence (AI), particularly large language models (LLMs), for synthesizing information within a simulated randomized clinical trial (RCT).
Focus on an anti-seizure medication, cenobamate, to demonstrate AI's inductive reasoning capabilities through simulated medical chart review.

Purpose of the Study:

To assess the feasibility and accuracy of using LLMs for analyzing simulated clinical trial data.
To compare AI-driven data synthesis with human analysis in evaluating drug efficacy and safety.

Main Methods:

Conducted an LLM-generated simulated RCT with 240 patients (placebo vs. cenobamate).
Simulated seizure counts and generated clinical notes using LLMs with varied neurologist styles and extraneous details.
Employed a secondary LLM pipeline for data synthesis and evaluated efficacy/safety using both AI and human readers.

Main Results:

AI analysis closely matched human analysis, with <3% difference in identifying drug efficacy and reported symptoms.
AI accurately identified seizure counts, symptom reports, and treatment efficacy.
Statistical analysis confirmed AI's ability to compare responder rates and side effect profiles between arms.

Conclusions:

AI, specifically LLMs, can accurately analyze noisy clinical notes to inductively generate clinical knowledge.
AI successfully inferred treatment effect sizes and symptom frequencies from unstructured simulated data, despite distractors.
AI presents a scalable, efficient alternative to traditional data mining in clinical research.