TY - JOUR
T1 - A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis
AU - Salihu, Adil
AU - Meier, David
AU - Noirclerc, Nathalie
AU - Skalidis, Ioannis
AU - Mauler-Wittwer, Sarah
AU - Recordon, Frédérique
AU - Kirsch, Matthias
AU - Roguelov, Christan
AU - Berger, Alexandre
AU - Sun, Xiaowu
AU - Abbe, Emmanuel
AU - Marcucci, Carlo
AU - Rancati, Valentina
AU - Rosner, Lorenzo
AU - Scala, Emmanuelle
AU - Rotzinger, David C.
AU - Humbert, Marc
AU - Muller, Olivier
AU - Lu, Henri
AU - Fournier, Stephane
N1 - Publisher Copyright:
© Europa Digital & Publishing 2024. All rights reserved.
PY - 2024/4/15
Y1 - 2024/4/15
N2 - BACKGROUND: Multidisciplinary Heart Teams (HTs) play a central role in the management of valvular heart diseases. However, the comprehensive evaluation of patients’ data can be hindered by logistical challenges, which in turn may affect the care they receive. AIMS: This study aimed to explore the ability of artificial intelligence (AI), particularly large language models (LLMs), to improve clinical decision-making and enhance the efficiency of HTs. METHODS: Data from patients with severe aortic stenosis presented at HT meetings were retrospectively analysed. A standardised multiple-choice questionnaire, with 14 key variables, was processed by the OpenAI Chat Generative Pre-trained Transformer (GPT)-4. AI-generated decisions were then compared to those made by the HT. RESULTS: This study included 150 patients, with ChatGPT agreeing with the HT’s decisions 77% of the time. The agreement rate varied depending on treatment modality: 90% for transcatheter valve implantation, 65% for surgical valve replacement, and 65% for medical treatment. CONCLUSIONS: The use of LLMs offers promising opportunities to improve the HT decision-making process. This study showed that ChatGPT’s decisions were consistent with those of the HT in a large proportion of cases. This technology could serve as a failsafe, highlighting potential areas of discrepancy when its decisions diverge from those of the HT. Further research is necessary to solidify our understanding of how AI can be integrated to enhance the decision-making processes of HTs.
AB - BACKGROUND: Multidisciplinary Heart Teams (HTs) play a central role in the management of valvular heart diseases. However, the comprehensive evaluation of patients’ data can be hindered by logistical challenges, which in turn may affect the care they receive. AIMS: This study aimed to explore the ability of artificial intelligence (AI), particularly large language models (LLMs), to improve clinical decision-making and enhance the efficiency of HTs. METHODS: Data from patients with severe aortic stenosis presented at HT meetings were retrospectively analysed. A standardised multiple-choice questionnaire, with 14 key variables, was processed by the OpenAI Chat Generative Pre-trained Transformer (GPT)-4. AI-generated decisions were then compared to those made by the HT. RESULTS: This study included 150 patients, with ChatGPT agreeing with the HT’s decisions 77% of the time. The agreement rate varied depending on treatment modality: 90% for transcatheter valve implantation, 65% for surgical valve replacement, and 65% for medical treatment. CONCLUSIONS: The use of LLMs offers promising opportunities to improve the HT decision-making process. This study showed that ChatGPT’s decisions were consistent with those of the HT in a large proportion of cases. This technology could serve as a failsafe, highlighting potential areas of discrepancy when its decisions diverge from those of the HT. Further research is necessary to solidify our understanding of how AI can be integrated to enhance the decision-making processes of HTs.
KW - aortic stenosis
KW - innovation
KW - TAVI
UR - https://www.scopus.com/pages/publications/85190902052
U2 - 10.4244/EIJ-D-23-00643
DO - 10.4244/EIJ-D-23-00643
M3 - Article
C2 - 38629422
AN - SCOPUS:85190902052
SN - 1774-024X
VL - 20
SP - E496-E503
JO - EuroIntervention
JF - EuroIntervention
IS - 8
ER -