Concordance between cardiological interventions and artificial intelligence recommendations for patients presenting with chest pain, based on consultations from the emergency department to cardiology
Authors: Gil Mancebo N , Mata Caballero R , Izquierdo Coronel B , Fraile Sanz A , Carnicero Carreno J A , Rodriguez Montes P , Humanes Ybanez S , De La Serna Real De Asua M , Martin Munoz M , Alvarez Bello M , Perela Alvarez C , Nieto Ibanez D , Olsen Rodriguez R , Awamleh Garcia P , Alonso Martin J J 
Published: October 2024

Abstract

Introduction
The management of chest pain in emergency departments is a critical challenge. The integration of advanced artificial intelligence (AI) tools, such as pre-trained generative systems like ChatGPT, may present an opportunity to optimize this diagnostic process. This study investigates the effectiveness of AI in recommending diagnostic tests for patients presenting with chest pain, evaluating its concordance with clinical decisions by experienced cardiologists.

Methods
Retrospective observational study where 102 cases of patients with chest pain treated in the ED and assessed by Cardiology between January and March 2023 were reviewed. Chest pain was classified into high, medium and low probability. Data from emergency department to cardiology consultations were presented to ChatGPT, asking which diagnostic test it would recommend: conventional ergometry, coronary CT, imaging stress test, coronary angiography, or discharge. Various concordance indices, such as Simple Match and Tanimoto, among others, were used to evaluate the consensus between ChatGPT recommendations and cardiologists' decisions (table 2).

Results
Out of the 102 cases collected, 54% presented with high, 35% with medium and 11% with low probability chest pain. Table 1 summarizes the different proposals between the cardiologist and the AI. For patients with high probability chest pain, coronary angiography was the most commonly requested test, with a positive concordance of 77% according to the Simple Match index and 72% as per the Tanimoto coefficient. For other tests such as ergometry or imaging stress test, there were a 77% and 74% agreement, respectively, in not performing these tests, and there was an almost unanimous agreement (close to 100%) against discharging patients without conducting tests. Medium probability chest pain was more frequent and complex in its management. The decision to perform a coronary angiography agreed in 70% according to the Simple Match index and 72% with Tanimoto. The decision not to discharge patients directly without conducting tests showed a higher agreement at 89%. Other results were less precise. For low probability chest pain, there was a notable concordance (90%) in the decision not to perform diagnostic tests, such as imaging stress test, according to the Simple Match index.

Conclusions
- The AI achieves a notable concordance with cardiologists' decisions in the diagnostic approach of chest pain, particularly for patients with high probability chest pain, where the coronary angiography demonstrated good agreement.
- Although AI cannot fully replace the clinical judgment of cardiologists in cases based on self-reported information, its utility as a supportive tool underscores the potential for its careful integration with medical expertise to enhance the diagnostic process. This implies the need for the development of specialized AI tools to better assist cardiologists in patient diagnosis.Table 1Table 2