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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 (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
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Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
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Shiraga T, Makimoto H, Kohlmann B, Magnisali CE, Imai Y, Itani Y, Makimoto A, Schölzel F, Bejinariu A, Kelm M, Rana O. Improving Valvular Pathologies and Ventricular Dysfunction Diagnostic Efficiency Using Combined Auscultation and Electrocardiography Data: A Multimodal AI Approach. SENSORS (BASEL, SWITZERLAND) 2023; 23:9834. [PMID: 38139680 PMCID: PMC10748155 DOI: 10.3390/s23249834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/07/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Simple sensor-based procedures, including auscultation and electrocardiography (ECG), can facilitate early diagnosis of valvular diseases, resulting in timely treatment. This study assessed the impact of combining these sensor-based procedures with machine learning on diagnosing valvular abnormalities and ventricular dysfunction. Data from auscultation at three distinct locations and 12-lead ECGs were collected from 1052 patients undergoing echocardiography. An independent cohort of 103 patients was used for clinical validation. These patients were screened for severe aortic stenosis (AS), severe mitral regurgitation (MR), and left ventricular dysfunction (LVD) with ejection fractions ≤ 40%. Optimal neural networks were identified by a fourfold cross-validation training process using heart sounds and various ECG leads, and their outputs were combined using a stacking technique. This composite sensor model had high diagnostic efficiency (area under the receiver operating characteristic curve (AUC) values: AS, 0.93; MR, 0.80; LVD, 0.75). Notably, the contribution of individual sensors to disease detection was found to be disease-specific, underscoring the synergistic potential of the sensor fusion approach. Thus, machine learning models that integrate auscultation and ECG can efficiently detect conditions typically diagnosed via imaging. Moreover, this study highlights the potential of multimodal artificial intelligence applications.
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Affiliation(s)
| | - Hisaki Makimoto
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke-City 329-0498, Japan
| | - Benita Kohlmann
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Christofori-Eleni Magnisali
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Yoshie Imai
- Mitsubishi Electric Inc., Kamakura 247-0056, Japan
| | - Yusuke Itani
- Mitsubishi Electric Inc., Kamakura 247-0056, Japan
| | - Asuka Makimoto
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Fabian Schölzel
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Alexandru Bejinariu
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
| | - Malte Kelm
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- CARID—Cardiovascular Research Institute Düsseldorf, 40225 Düsseldorf, Germany
| | - Obaida Rana
- Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany; (B.K.); (O.R.)
- CARID—Cardiovascular Research Institute Düsseldorf, 40225 Düsseldorf, Germany
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Guillen-Grima F, Guillen-Aguinaga S, Guillen-Aguinaga L, Alas-Brun R, Onambele L, Ortega W, Montejo R, Aguinaga-Ontoso E, Barach P, Aguinaga-Ontoso I. Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine. Clin Pract 2023; 13:1460-1487. [PMID: 37987431 PMCID: PMC10660543 DOI: 10.3390/clinpract13060130] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/15/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023] Open
Abstract
The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. MATERIAL AND METHODS We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4's new image analysis capability. RESULTS GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as "error requiring intervention to sustain life" and "error resulting in death", had a 0% rate. CONCLUSIONS GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implications for patient safety.
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Affiliation(s)
- Francisco Guillen-Grima
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain
- Department of Preventive Medicine, Clinica Universidad de Navarra, 31008 Pamplona, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Institute of Health Carlos III, 46980 Madrid, Spain
| | - Sara Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | - Laura Guillen-Aguinaga
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Department of Nursing, Kystad Helse-og Velferdssenter, 7026 Trondheim, Norway
| | - Rosa Alas-Brun
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
| | - Luc Onambele
- School of Health Sciences, Catholic University of Central Africa, Yaoundé 1100, Cameroon;
| | - Wilfrido Ortega
- Department of Surgery, Medical and Social Sciences, University of Alcala de Henares, 28871 Alcalá de Henares, Spain;
| | - Rocio Montejo
- Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg, 413 46 Gothenburg, Sweden;
- Department of Obstetrics and Gynecology, Sahlgrenska University Hospital, 413 46 Gothenburg, Sweden
| | | | - Paul Barach
- Jefferson College of Population Health, Philadelphia, PA 19107, USA;
- School of Medicine, Thomas Jefferson University, Philadelphia, PA 19107, USA
- Interdisciplinary Research Institute for Health Law and Science, Sigmund Freud University, 1020 Vienna, Austria
- Department of Surgery, Imperial College, London SW7 2AZ, UK
| | - Ines Aguinaga-Ontoso
- Department of Health Sciences, Public University of Navarra, 31008 Pamplona, Spain; (S.G.-A.); (L.G.-A.); (R.A.-B.)
- Healthcare Research Institute of Navarra (IdiSNA), 31008 Pamplona, Spain
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