Evaluation of the prediagnosis and management of ChatGPT-4.0 in clinical cases in cardiology.

Aim: Evaluation of the performance of ChatGPT-4.0 in providing prediagnosis and treatment plans for cardiac clinical cases by expert cardiologists. Methods: 20 cardiology clinical cases developed by experienced cardiologists were divided into two groups according to preparation methods. Cases were reviewed and analyzed by the ChatGPT-4.0 program, and analyses of ChatGPT were then sent to cardiologists. Eighteen expert cardiologists evaluated the quality of ChatGPT-4.0 responses using Likert and Global quality scales. Results: Physicians rated case difficulty (median 2.00), revealing high ChatGPT-4.0 agreement to differential diagnoses (median 5.00). Management plans received a median score of 4, indicating good quality. Regardless of the difficulty of the cases, ChatGPT-4.0 showed similar performance in differential diagnosis (p: 0.256) and treatment plans (p: 0.951). Conclusion: ChatGPT-4.0 excels at delivering accurate management and demonstrates its potential as a valuable clinical decision support tool in cardiology.

Have you ever wondered if an artificial intelligence (AI) program could help doctors figure out what the problem is when someone has heart complaints? Our research examined this by testing an AI program called ChatGPT-4.0 on clinical cases. We wanted to see if it could help doctors by giving good advice on what might be wrong with patients who have heart issues and what should be done to help them. To test this, we used ChatGPT-4.0 to look at 20 different stories about patients with heart problems. These stories were made to cover a variety of common heart conditions faced by heart doctors. Then, we asked 18 heart doctors to check if the advice from ChatGPT-4.0 was good and made sense. What we found was quite interesting! Most of the time, the doctors agreed that the computer gave good advice on what might be wrong with the patients and how to help them. This means that this smart computer program could be a helpful tool for doctors, especially when they are trying to figure out tricky heart problems. But, it's important to say that computers like ChatGPT-4.0 are not ready to replace doctors. They are tools that can offer suggestions. Doctors still need to use their knowledge and experience to make the final call on what's best for their patients. In simple terms, our study shows that with more development and testing, AI like ChatGPT-4.0 could be a helpful assistant to doctors in treating heart disease, making sure patients get the best care possible.

ERRATUM: Genetic testing for inherited cardiovascular diseases. A position statement of the Polish Cardiac Society endorsed by Polish Society of Human Genetics and Cardiovascular Patient Communities.

According to the latest guidelines of European and American medical societies, genetic testing (GT) is essential in cardiovascular diseases for establishing diagnosis, predicting prognosis, enabling initiation of disease-modifying therapy, and preventing sudden cardiac death. The GT result may be relevant for cascade GT in the patient's relatives, for planning his/her profession and physical activity, and for procreative counseling. This position statement has been prepared due to the scarcity of GT in cardiovascular diseases in Poland and the need to expand its availability. We give a concise description of the genetic background of cardiomyopathies, channelopathies, aortopathies, familial hypercholesterolemia, pheochromocytomas, and paragangliomas. The article discusses various aspects of GT in specific populations, such as children or athletes, and also presents prenatal genetic diagnostics. We propose recommendations for GT and counselling, which take into account Polish needs and capabilities. We give an outline of legal regulations, good clinical practice in GT with respect for patient rights, the role of cardiologists and clinical geneticists in GT planning and post-test counseling, and the requirements for laboratories performing genetic tests. The Polish Cardiac Society and Polish Society of Human Genetics experts speak with one voice with cardiovascular patient communities to underline the need for a law on GT and increasing the availability of GT for cardiovascular patients.

Assessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts.

Background: Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse. Objective: This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses. Methods: We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio. Results: GPT-4 and human experts displayed comparable efficacy in medical accuracy ("GPT-4 is better" at 132/251, 52.6% vs "Human expert is better" at 119/251, 47.4%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1% vs 30/238, 12.6%) but also a greater proportion of low-accuracy responses (11/237, 4.6% vs 1/238, 0.4%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience. Conclusions: GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions.

Evaluation of Procedure Doses and Staff Attitudes in Interventional Cardiology in Terms of Radiation Safety.

OBJECTIVE: Ionizing radiation has long been used in the medical field. Catheter laboratories (cath labs) are recognized as areas where radiation exposure is notably high. This study aims to examine the levels of radiation exposure during various interventional procedures to raise awareness of this issue in Türkiye. METHODS: This study evaluated the procedure radiation doses (n = 2804) in the cath labs of four public hospitals with distinct characteristics. Radiation dose evaluation was conducted using Cumulative Air Kerma (CAK). The Kolmogorov-Smirnov test, Kruskal-Wallis H test, independent T-test, and Pearson correlation coefficient were utilized to analyze the data. A p-value of < 0.05 was considered statistically significant. Data were analyzed using IBM® Statistical Package for the Social Sciences (SPSS®) STATISTICS Version 26.0.0.0 (IBM Corporation, Armonk, New York, USA). RESULTS: The procedure radiation doses in the cath labs were documented. The findings are largely consistent with the literature. Notably, several outlier cases with extremely high radiation doses were identified [CAK (min-max) = 0.12 - 9.9 Gy]. Procedures such as chronic total occlusion (CTO) [Mean CAK: 3.8 (± 1.5) Gy] and percutaneous coronary interventions (PCI) [Mean CAK: 1.5 (± 1.4) Gy] were associated with high doses. Additionally, personnel attitudes toward radiation optimization in cath labs were found to be inadequate. CONCLUSION: The incidence of high radiation exposure during interventional procedures may be higher than expected in Türkiye. Further research is necessary to identify predictors and implement preventive measures to reduce these rates. For this purpose, establishing diagnostic radiation reference levels (DRLs) could help monitor national radiation levels.

Global research progress on radiofrequency ablation in cardiology: A bibliometric analysis (2004-2023).

In recent years, significant advancements in radiofrequency ablation technology have notably enhanced arrhythmia treatment in cardiology. Technological advancements and increasing clinical adoption have made radiofrequency ablation a key therapy in improving life quality for patients with conditions like atrial fibrillation (AF). Consequently, there has been a marked increase in research output, underscoring the technology's significance and its potential in cardiology. Aims to comprehensively analyze cardiology's radiofrequency ablation research trends, identifying leading countries and institutions in international collaborations, key researchers' contributions, and evolving research hotspots. The study, based on the Web of Science Core Collection database, reviewed the literatures from 2004 to 2023. CiteSpace 6.2.R7 Basic was used for bibliometric analysis, which examined annual publication trends, international collaboration networks, key authors, leading research institutions, major journals, keyword co-occurrence and clustering trends. Analyzing 3423 relevant articles, this study reveals a consistent growth in cardiology radiofrequency ablation research since 2004. The analysis shows that the United States, Germany, and France hold central roles in the international collaboration network, with leading authors from premier US and European institutions. Keyword cluster analysis identifies "atrial flutter" and "ventricular tachycardia" as current research focal points. Cardiology radiofrequency ablation research shows a growth trend, led by the United States and European countries. Research hotspots are concentrated on the diverse applications of radiofrequency ablation technology and the treatment of AF. Future studies may increasingly focus on technological innovation and the deepening of clinical applications.