The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature
Abstract
:1. Introduction
2. Methods
3. Accelerating Patient Benefits in Cardiology Using Artificial Intelligence
4. Decision Support Systems in Cardiovascular Health
5. Personalized Cardiology Using Machine Learning
6. Challenges and Future Directions
7. Conclusions
8. Key Points
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Study | AI Application | Diagnostic Modality | Performance Metrics | Future Considerations |
---|---|---|---|---|
[27] | Deep neural networks for ECG analysis | ECG | AUC: 0.97 and F1 score: 0.837 | Interpretability and collaboration between AI systems and clinical expertise |
[28] | AI-enabled ECG for atrial fibrillation prediction | Wearable ECG monitors | AUC: 0.87, sensitivity: 79%, specificity: 79.5%, and accuracy: 79.4% | Considerations for undetected atrial fibrillation and prospective calibration before widespread application to a broader population |
[33] | AI in coronary angiography and TAVR | Coronary angiography and TAVR | Procedure time and complication rate reduction | Enhancing AI algorithms’ adaptability to diverse procedural scenarios |
Study | AI Application | CDSS in Clinical Settings | Performance Metrics | Future Considerations |
---|---|---|---|---|
[55] | AI-driven CDSS in clinical practice | Real-time recommendations based on patient data | 68% improvement in clinical practice | Disparity in assessing various AI-driven CDSS models |
[56] | AI-driven CDSS for sepsis prediction | Predicting sepsis outcomes | Potential in early sepsis detection | Challenges in EHR data quality and standardization Prospective validation studies for clinical impact assessment. |
[57] | AI-driven CDSS for myocardial infarction prediction | Predicting myocardial infarction outcomes | Moderate improvement over traditional methods; F1 Score: 0.092 and AUC: 0.835 | Calibration challenges due to overfitting from low-event frequency Adequate discrimination despite poor calibration |
Study | Focus Area | Machine Learning Application | Performance Metrics | Future Considerations |
---|---|---|---|---|
[73] | Risk prediction in resource-limited countries | STEMI | Improved mortality prediction following STEMI Extra Tree ML model demonstrated best predictive ability (sensitivity: 85%, AUC: 79.7%, and accuracy: 75%) | Clinical applicability Generalizability across diverse patient populations Reducing biases in training data |
[75] | Automated volume-derived cardiac functional evaluation | CMR imaging and automated strain assessment | GLS and GCS best predicted MACE with high accuracy | Time-consuming post-processing Validation in broader populations |
[77] | (Semi)Automatic CAC identification in cardiac CT | Cardiac CT and automated CAC scoring | 1. Detection of 52% to 94% of CAC lesions. Positive predictive values between 65% and 96%. 2. Linearly weighted Cohen’s kappa for patient CVD risk categorization ranged from 0.80 to 1.00. | Missed lesions in distal coronary arteries False positive errors near coronary ostia Challenges in ambiguous locations |
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Gala, D.; Behl, H.; Shah, M.; Makaryus, A.N. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare 2024, 12, 481. https://doi.org/10.3390/healthcare12040481
Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare. 2024; 12(4):481. https://doi.org/10.3390/healthcare12040481
Chicago/Turabian StyleGala, Dhir, Haditya Behl, Mili Shah, and Amgad N. Makaryus. 2024. "The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature" Healthcare 12, no. 4: 481. https://doi.org/10.3390/healthcare12040481