Artificial Intelligence-powered Virtual Assistant for Emergency Triage in Neurology

Summary

This study examines the use of an AI-powered virtual assistant for quickly identifying and handling neurological emergencies, particularly in places with limited medical resources. The research aimed to check if this AI tool is safe and accurate enough to move on to more advanced testing stages. In a first-of-its-kind trial, the virtual assistant was tested with patients having urgent neurological issues. Neurologists first reviewed the AI's recommendations using clinical records and then assessed its performance directly with patients. The findings were as follows: neurologists agreed with the AI's decisions nearly all the time, and the AI outperformed earlier versions of Chat GPT in every tested aspect. Patients and doctors found the AI to be highly effective, rating it as excellent or very good in most cases. This suggests the AI could significantly enhance how quickly and accurately neurological emergencies are dealt with, although further trials are needed before it can be widely used.

Conditions

• Stroke
• Guillain-Barre Syndrome
• Facial Palsy
• Migraine
• Status Epilepticus
• Vertigo Benign Positional
• Delirium
• Trigeminal Neuralgia
• Meningitis
• Subarachnoid Hemorrhage

Interventions

DIAGNOSTIC_TEST

Virtual Assistant

Stage 1 focused on safety, using only medical information from clinical records for the virtual assistant. In Stage 2, which evaluated accuracy, participants interacted with the virtual assistant post-medical stabilization. Additionally, participants also provided initial symptom details for Chat-GPT input. Nine neurologists specializing in emergency participated in the study. In Stage 1, they assessed the virtual assistant's performance using clinical history information. In Stage 2, they analyzed the results from participant interactions with the assistant and performed a comparative evaluation of Chat-GPT. The virtual assistant functioned as a chatbot on WhatsApp and Telegram, using Spanish and incorporating advanced algorithms, decision trees, and large language models for interaction. For comparison, we utilized Chat-GPT versions 3.5 and 4, employing two prompt types in natural Spanish: one incorporating clinical record data and the other based on participant narratives.

Sponsors & Collaborators

• Entelai

  collaborator UNKNOWN

• Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia

  lead OTHER

Principal Investigators

• Mauricio F Farez, MD MPH · Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia

Study Design

Allocation

NON_RANDOMIZED

Purpose

DIAGNOSTIC

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2023-10-01

Primary Completion

2024-01-01

Completion

2024-01-01

Countries

• Argentina

Study Locations