ChatGPT4’s diagnostic accuracy in inpatient neurology: A retrospective cohort study

Our findings suggest that CG4 can serve as a valuable diagnostic tool within the domain of inpatient neurology, providing comprehensive and accurate initial diagnoses comparable to those of consultant neurologists. The use of CG4 might contribute to better patient outcomes by serving as an aid in diagnosis and treatment recommendations, potentially leading to reduced missed diagnoses and quicker diagnostic processes. Continuous strategies and evaluations to improve LLMs' accuracy remain crucial. Further studies with larger sample sizes and independent third-party evaluations are recommended to confirm these findings and assess the impact of LLMs on patient health.

Authors

Institutions

Publication Details

Journal
Heliyon
Published
2024-12-01
DOI
https://doi.org/10.1016/j.heliyon.2024.e40964
Citations
5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ChatGPT4’s diagnostic accuracy in inpatient neurology: A retrospective cohort study

Michael Neeki, Joseph Dhahbi, Leo Issagholian, Sebastian Cano-Besquet et al.
5 citations
Heliyon
Artificial Intelligence in Healthcare and Education
0.60
article

ChatGPT4’s diagnostic accuracy in inpatient neurology: A retrospective cohort study

Michael Neeki, Joseph Dhahbi, Leo Issagholian, Sebastian Cano-Besquet, Tyler Rice-Canetto, Hadi Abou-El-Hassan, Simon Alarcon, Jason Zimmerman, Nasser Salomon, Ivan Rojas
article en
5 citations

Abstract

Our findings suggest that CG4 can serve as a valuable diagnostic tool within the domain of inpatient neurology, providing comprehensive and accurate initial diagnoses comparable to those of consultant neurologists. The use of CG4 might contribute to better patient outcomes by serving as an aid in diagnosis and treatment recommendations, potentially leading to reduced missed diagnoses and quicker diagnostic processes. Continuous strategies and evaluations to improve LLMs' accuracy remain crucial. Further studies with larger sample sizes and independent third-party evaluations are recommended to confirm these findings and assess the impact of LLMs on patient health.

HeliyonVol. 10(24)
Arrowhead Regional Medical Center (US), University of California System (US)
Openalex Percentile: Top 28%
Artificial Intelligence in Healthcare and Education
0.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.