Artificial Intelligence-powered Virtual Assistant for Emergency Triage in Neurology

Stroke Clinical Trial

— AIDEN  

Official title:

Phase 1 Trial of the Implementation of an Artificial Intelligence-powered Virtual Assistant for Emergency Triage in Neurology

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06334796
• Other study ID #: 10476
• Status: Completed
• Phase: Early Phase 1
• Start date: October 1, 2023
• Est. completion date: January 1, 2024

Study information

• Verified date: March 2024
• Source: Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Description:

Background and Objectives: Neurological emergencies pose significant challenges in medical care, especially in resource-limited countries. Artificial Intelligence (AI), particularly health chatbots, offers a promising solution. However, rigorous validation is required to ensure safety and accuracy. The objective of our work is to evaluate the diagnostic accuracy and resolution effectiveness of an AI-powered virtual assistant designed for the triage of emergency neurological pathologies, to ensure the minimum standard of safety that allows for the progression to successive validation tests.

Methods: This Phase 1 trial evaluates the performance of an AI-powered virtual assistant for emergency neurological triage. Ten patients over 18 years old with urgent neurological pathologies were selected. In the first stage, nine neurologists assessed the safety of the virtual assistant using their clinical records. In the second part, the assistant's accuracy when used by patients was evaluated. Finally, its performance was compared with Chat GPT 3.5 and 4.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 10
• Est. completion date: January 1, 2024
• Est. primary completion date: January 1, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients over 18 years old consulting in the ER due to a neurological emergency

Exclusion Criteria:

• Pregnancy

Related Conditions & MeSH terms

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

Intervention

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.

Locations

Country	Name	City	State
Argentina	Fleni	Buenos Aires

Sponsors (2)

Lead Sponsor	Collaborator
Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia	Entelai

Country where clinical trial is conducted

Argentina, 

References & Publications (3)

Au Yeung J, Wang YY, Kraljevic Z, Teo JTH. Artificial intelligence (AI) for neurologists: do digital neurones dream of electric sheep? Pract Neurol. 2023 Nov 23;23(6):476-488. doi: 10.1136/pn-2023-003757. — View Citation

Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023 Mar 30;388(13):1201-1208. doi: 10.1056/NEJMra2302038. No abstract available. — View Citation

Patel UK, Anwar A, Saleem S, Malik P, Rasul B, Patel K, Yao R, Seshadri A, Yousufuddin M, Arumaithurai K. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2021 May;268(5):1623-1642. doi: 10.1007/s00415-019-09518-3. Epub 2019 Aug 26. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic performance	Refers to the accuracy and effectiveness of medical tests or diagnostic tools in correctly identifying a disease or condition in patients.; Syndromic diagnosis agreement: evaluating neurologists considered a syndromic diagnosis accurate when AI tools could identify a condition based on a set of commonly coexisting signs and symptoms, rather than identifying a specific disease. This method is applied when the precise disease causing the symptoms is not immediately identifiable, allowing healthcare providers to effectively monitor and treat the patient's presenting symptoms.; Differential diagnosis agreement: a differential diagnosis was considered accurate when the differentials provided by each AI tool matched those presented by the participants.; The gold standard for diagnosis was considered to be the one given in the emergency department, unchanged over a one-month period.	The first interaction between participants and the virtual assistant occurred within less than a year after the event. Outcome measures were evaluated immediately after the interaction between patients and the virtual assistant.
Secondary	Appropriate medical conduct or recommendation	Case resolution was evaluated based on appropriate medical conduct or recommendation, categorizing 1) urgency as immediate, 2) short-term (within 48 hours), 3) or non-urgent.; The recommendations provided by each AI tool were assessed based on information gathered from clinical histories and input from participants.; The gold standard of appropriate medical conduct or recommendation was considered to be that given in the emergency department, with no changes over a period of one month.	The first interaction between participants and the virtual assistant occurred within less than a year after the event. Outcome measures were evaluated immediately after the interaction between patients and the virtual assistant.
Secondary	Assessment of Usability and Satisfaction	Usability was measured by the time and number of questions needed for final diagnosis and resolution, both by neurologists and participants. For Chat GPT, we evaluated the time taken to draft the consultation reason.; A satisfaction scale from 1 to 5 was implemented, with 1 indicating a negative experience ("poor", potentially risky for the patient) and 5 highly positive ("excellent", potentially surpassing non-specialized human triage). A simple yes/no survey was also applied to participants, asking about the comprehensibility of the assistant's questions, the adequacy of referral according to urgency, and whether they considered the assistant could replace non-specialized triage or reduce emergency arrival time.	The first interaction between participants and the virtual assistant occurred within less than a year after the event. Outcome measures were evaluated immediately after the interaction between patients and the virtual assistant.