⚡ Quick Summary
This study investigates the use of large language models like ChatGPT as a decision-support tool for multidisciplinary heart teams (MDHT) in coronary revascularization. The findings reveal that ChatGPT-4 demonstrates a high concordance rate with MDHT recommendations, suggesting its potential to enhance clinical decision-making.
🔍 Key Details
- 📊 Dataset: 86 consecutive coronary angiography cases
- 🧩 Models used: ChatGPT-3.5 and ChatGPT-4
- ⚙️ Evaluation metrics: Accuracy, sensitivity, specificity, and kappa
- 🏆 Performance of ChatGPT-4: Accuracy 0.82, Sensitivity 0.8, Specificity 0.83
🔑 Key Takeaways
- 🤖 ChatGPT-4 shows a strong ability to align with MDHT recommendations.
- 📈 High accuracy (>0.9) was noted in specific patient subgroups, including those with left main disease and diabetic patients.
- 🔍 Detailed case presentations enhance the model’s predictive accuracy.
- 💡 ChatGPT-3.5 demonstrated lower concordance compared to ChatGPT-4.
- 🌟 High reliability of ChatGPT-4 indicated by low entropy and high Fleiss kappa values.
- 🏥 Potential for integration of AI tools in clinical settings to standardize decision-making.
- 📅 Study period: March 2023 to July 2023.
📚 Background
In the realm of coronary revascularization, the importance of multidisciplinary heart team (MDHT) discussions is well recognized. However, variability in how these discussions are implemented across different healthcare settings poses a challenge. The advent of language learning models like ChatGPT offers a promising avenue to standardize and enhance decision-making processes in this critical area of cardiac care.
🗒️ Study
Conducted between March and July 2023, this study analyzed 86 consecutive coronary angiography cases referred for revascularization. The researchers compared the recommendations generated by ChatGPT-3.5 and ChatGPT-4 against those made by an MDHT. The cases included comprehensive details such as demographics, medical history, angiographic findings, and SYNTAX scores, presented in various formats to assess the models’ performance.
📈 Results
The results indicated that ChatGPT-4 achieved an impressive accuracy of 0.82, with a sensitivity of 0.8 and specificity of 0.83. In contrast, ChatGPT-3.5 showed lower performance metrics (accuracy 0.67). The study highlighted that the best correlation between ChatGPT-4 and MDHT recommendations occurred when clinical cases were presented in a detailed context, emphasizing the model’s capability to process complex information effectively.
🌍 Impact and Implications
The findings from this study suggest that advanced language learning models like ChatGPT-4 could play a significant role in enhancing decision-making in coronary artery disease revascularization. By providing reliable recommendations, these models may help standardize practices across various healthcare settings, ultimately improving patient outcomes and streamlining clinical workflows. The integration of AI tools in healthcare could pave the way for more consistent and evidence-based approaches to patient care.
🔮 Conclusion
This study underscores the potential of large language models as valuable decision-support tools in the field of cardiology. With demonstrated accuracy and reliability, models like ChatGPT-4 could significantly aid multidisciplinary heart teams in making informed decisions regarding coronary revascularization. As we continue to explore the integration of AI in healthcare, further research is essential to fully realize its benefits in clinical practice.
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Enhancing Coronary Revascularization Decisions: The Promising Role of Large Language Models as a Decision-Support Tool for Multidisciplinary Heart Team.
Abstract
BACKGROUND: While clinical practice guidelines advocate for multidisciplinary heart team (MDHT) discussions in coronary revascularization, variability in implementation across health care settings remains a challenge. This variability could potentially be addressed by language learning models like ChatGPT, offering decision-making support in diverse health care environments. Our study aims to critically evaluate the concordance between recommendations made by MDHT and those generated by language learning models in coronary revascularization decision-making.
METHODS: From March 2023 to July 2023, consecutive coronary angiography cases (n=86) that were referred for revascularization (either percutaneous or surgical) were analyzed using both ChatGPT-3.5 and ChatGPT-4. Case presentation formats included demographics, medical background, detailed description of angiographic findings, and SYNTAX score (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery; I and II), which were presented in 3 different formats. The recommendations of the models were compared with those of an MDHT.
RESULTS: ChatGPT-4 showed high concordance with decisions made by the MDHT (accuracy 0.82, sensitivity 0.8, specificity 0.83, and kappa 0.59), while ChatGPT-3.5 (0.67, 0.27, 0.84, and 0.12, respectively) showed lower concordance. Entropy and Fleiss kappa of ChatGPT-4 were 0.09 and 0.9, respectively, indicating high reliability and repeatability. The best correlation between ChatGPT-4 and MDHT was achieved when clinical cases were presented in a detailed context. Specific subgroups of patients yielded high accuracy (>0.9) of ChatGPT-4, including those with left main disease, 3 vessel disease, and diabetic patients.
CONCLUSIONS: The present study demonstrates that advanced language learning models like ChatGPT-4 may be able to predict clinical recommendations for coronary artery disease revascularization with reasonable accuracy, especially in specific patient groups, underscoring their potential role as a supportive tool in clinical decision-making.
Author: [‘Sudri K’, ‘Motro-Feingold I’, ‘Ramon-Gonen R’, ‘Barda N’, ‘Klang E’, ‘Fefer P’, ‘Amunts S’, ‘Attia ZI’, ‘Alkhouli M’, ‘Segev A’, ‘Cohen-Shelly M’, ‘Barbash IM’]
Journal: Circ Cardiovasc Interv
Citation: Sudri K, et al. Enhancing Coronary Revascularization Decisions: The Promising Role of Large Language Models as a Decision-Support Tool for Multidisciplinary Heart Team. Enhancing Coronary Revascularization Decisions: The Promising Role of Large Language Models as a Decision-Support Tool for Multidisciplinary Heart Team. 2024; (unknown volume):e014201. doi: 10.1161/CIRCINTERVENTIONS.124.014201