Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Diagnostics, Journal Year: 2024, Volume and Issue: 14(11), P. 1103 - 1103

Published: May 26, 2024

Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In medical field, there are numerous applications AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, fact being supported by exponential increase number publications which algorithms play an important role data analysis, pattern discovery, identification anomalies, therapeutic decision making. Furthermore, with technological development, have appeared new models machine learning (ML) deep (DP) that capable exploring various cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional many others. sense, present article aims provide general vision current state use cardiology. Results: We identified included subset 200 papers directly relevant research covering wide range applications. Thus, paper presents arithmology, clinical or emergency procedures summarized manner. Recent studies from highly scientific literature demonstrate feasibility advantages using different branches Conclusions: The integration cardiology offers promising perspectives for increasing accuracy decreasing error rate efficiency practice. From predicting risk sudden death ability respond cardiac resynchronization therapy diagnosis pulmonary embolism early detection valvular diseases, shown their potential mitigate feasible solutions. At same limits imposed small samples studied highlighted alongside challenges presented ethical implementation; these relate legal implications regarding responsibility making processes, ensuring patient confidentiality security. All constitute future directions will allow

Language: Английский

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Artificial intelligence in cardiovascular procedures: a bibliometric and visual analysis study

Annals of Medicine and Surgery, Journal Year: 2025, Volume and Issue: 87(4), P. 2187 - 2203

Published: Feb. 27, 2025

Background: The integration of artificial intelligence (AI) into cardiovascular procedures has significantly advanced diagnostic accuracy, outcome prediction, and robotic-assisted surgeries. However, a comprehensive bibliometric analysis AI’s impact in this field is lacking. This study examines research trends, key contributors, emerging themes AI-driven interventions. Methods: We retrieved relevant publications from the Web Science Core Collection analyzed them using VOSviewer, CiteSpace, Biblioshiny to map trends collaborations. Results: AI-related grown substantially 1993 2024, with sharp increase 2020 2023, peaking at 93 2023. USA (127 papers), China (79), England (31) were top Harvard University leading institutional output (17 papers). Frontiers Cardiovascular Medicine was most prolific journal. included “machine learning,” “mortality,” “cardiac surgery,” “association,” “implantation,” “aortic stenosis,” underscoring expanding role predictive modeling surgical outcomes. Conclusion: AI demonstrates transformative potential procedures, particularly imaging, modeling, patient management. highlights growing interest applications provides framework for integrating clinical workflows enhance treatment strategies,

Language: Английский

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Assessing the performance of large language models (GPT-3.5 and GPT-4) and accurate clinical information for pediatric nephrology

Pediatric Nephrology, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant advancements providing accurate clinical information. However, the performance and applicability of AI models specialized fields such pediatric nephrology remain underexplored. This study is aimed at evaluating ability two AI-based language models, GPT-3.5 GPT-4, to provide reliable information nephrology. The were evaluated on four criteria: accuracy, scope, patient friendliness, applicability. Forty specialists with ≥ 5 years experience rated GPT-4 responses 10 questions using 1-5 scale via Google Forms. Ethical approval was obtained, informed consent secured from all participants. Both demonstrated comparable across criteria, no statistically differences observed (p > 0.05). exhibited slightly higher mean scores parameters, but negligible (Cohen's d < 0.1 for criteria). Reliability analysis revealed low internal consistency both (Cronbach's alpha ranged between 0.019 0.162). Correlation indicated relationship participants' professional their evaluations (correlation coefficients - 0.026 0.074). While provided foundational level support, neither model superior addressing unique challenges findings highlight need domain-specific training integration updated guidelines enhance reliability fields. underscores potential while emphasizing importance human oversight further refinements applications.

Language: Английский

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Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e68509 - e68509

Published: March 6, 2025

Background Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for personalized prevention. Objective This study aims to develop validate predictive models assess the gastrointestinal after (GIBCG) guide Methods Participants were recruited from 4 medical centers, including prospective cohort Medical Information Mart Intensive Care IV (MIMIC-IV) database. From an initial 18,938 patients, 16,440 included in final analysis applying exclusion criteria. Thirty combinations machine learning algorithms compared, optimal model was selected based on integrated performance metrics, area under receiver operating characteristic curve (AUROC) Brier score. then developed into web-based prediction calculator. The Shapley Additive Explanations method used provide both global local explanations predictions. Results using data 3 centers (n=13,399) validated Drum Tower (n=2745) MIMIC (n=296). model, 15 easily accessible admission features, demonstrated AUROC 0.8482 (95% CI 0.8328-0.8618) derivation cohort. In external validation, 0.8513 0.8221-0.8782) 0.7811 0.7275-0.8343) indicated that high-risk patients identified by had significantly increased mortality (odds ratio 2.98, 95% 1.784-4.978; P<.001). For these populations, preoperative use proton pump inhibitors independent protective factor against occurrence GIBCG. By contrast, dual antiplatelet therapy oral anticoagulants as factors. However, low-risk (χ21=0.13, P=.72), (χ21=0.38, P=.54), (χ21=0.15, P=.69) not associated with Conclusions Our accurately at high GIBCG, who poor prognosis. approach can aid early stratification Trial Registration Chinese Clinical Registry Center ChiCTR2400086050; http://www.chictr.org.cn/showproj.html?proj=226129

Language: Английский

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Comparative Analysis of ChatGPT and Google Gemini in Generating Patient Educational Resources on Cardiac Health: A Focus on Exercise-Induced Arrhythmia, Sleep Habits, and Dietary Habits

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

Patient education is crucial in cardiovascular health, aiding shared decision-making and improving adherence to treatments. Artificial intelligence (AI) tools, including ChatGPT (OpenAI, San Francisco, CA) Google Gemini (Google LLC, Mountain View, CA), are revolutionizing patient by providing personalized, round-the-clock access information, enhancing engagement, health literacy. The paper aimed compare the responses generated for creating guides on exercise-induced arrhythmia, sleep habits cardiac "dietary health. A comparative observational study was conducted evaluating three AI-generated guides: "exercise-induced arrhythmia," "sleep health," using Gemini. Responses were evaluated word count, sentence grade level, ease score, readability Flesch-Kincaid calculator QuillBot (QuillBot, Chicago, IL) plagiarism tool similarity score. Reliability assessed with modified DISCERN Statistical analysis R version 4.3.2 (The Core Team, Foundation Computing, Vienna, Austria). ChatGPT-generated had an overall higher average count when compared Gemini; however, difference not statistically significant (p = 0.2817). scored of understanding, though this also 0.7244). There no differences or words per sentence. tended produce more complex content certain topics, whereas Gemini's generally easier read. Similarity scores across all while reliability varied topic, performing better arrhythmia found between a cardiology disorders brochure. Future research should explore AI techniques various disorders, ensuring up-to-date reliable public information.

Language: Английский

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A chat with ChatGPT about hypertension: the future of preventive cardiology

Minerva Cardiology and Angiology, Journal Year: 2024, Volume and Issue: 72(4)

Published: June 1, 2024

Language: Английский

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

Future Cardiology, Journal Year: 2024, Volume and Issue: 20(4), P. 197 - 207

Published: March 11, 2024

Evaluation of the performance ChatGPT-4.0 in providing prediagnosis and treatment plans for cardiac clinical cases by expert cardiologists.

Language: Английский

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Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy

Gene, Journal Year: 2024, Volume and Issue: 934, P. 149015 - 149015

Published: Oct. 18, 2024

Language: Английский

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Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI Innovations

Bioengineering, Journal Year: 2024, Volume and Issue: 11(12), P. 1239 - 1239

Published: Dec. 7, 2024

Cardiovascular diseases are some of the underlying reasons contributing to relentless rise in mortality rates across globe. In this regard, there is a genuine need integrate advanced technologies into medical realm detect such accurately. Moreover, numerous academic studies have been published using AI-based methodologies because their enhanced accuracy detecting heart conditions. This research extensively delineates different conditions, e.g., coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, and myocardial infarction, symptoms subsequently introduces detection for precisely classifying diseases. The review shows that incorporation artificial intelligence exhibits accuracies along with plethora other benefits, like improved diagnostic accuracy, early prevention, reduction errors, faster diagnosis, personalized treatment schedules, optimized monitoring predictive analysis, efficiency, scalability. Furthermore, also indicates conspicuous disparities between results generated by previous algorithms latest ones, paving way researchers ascertain these through comparative analysis practical conditions patients. conclusion, AI disease holds paramount significance transformative potential greatly enhance patient outcomes, mitigate healthcare expenditure, amplify speed diagnosis.

Language: Английский

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Feature Dimensions of Artificial Intelligences Challenges and Techniques - A Survey

International Journal of Electronics and Communication Engineering, Journal Year: 2024, Volume and Issue: 11(12), P. 107 - 122

Published: Dec. 31, 2024

Artificial Intelligence (AI) is rapidly transforming sectors such as healthcare, education, and public services, contributing new solutions that advance efficiency, management, overall outcomes. However, despite its vast potential, AI adoption faces numerous challenges, including ethical concerns (e.g., algorithmic bias), data privacy issues, integration difficulties with legacy systems. This paper provides a comprehensive survey of applications across these sectors, analyzing over 60 recent studies from 2019 to 2024 after the PRISMA methodology. The study identifies key factors influencing successful implementation by highlighting sector-specific challenges shared barriers. framework was applied for systematic selection, inclusion exclusion criteria, screening, extraction, ensuring only relevant, high-quality were reviewed. These experimental results reveal models consistently outperform state-of-the-art techniques in critical domains, medical diagnosis, personalised service optimisation. hybrid approach, which combines Convolutional Neural Networks (CNNs) Recurrent (RNNs), outperforms existing addressing preprocessing, model architecture, hyperparameter Additionally, explores future up-and-coming technologies quantum computing, blockchain, metaverse while providing strategies overcome legal, cultural, infrastructural barriers adoption. findings offer actionable insights researchers, practitioners, policymakers, emphasising need both technical innovation considerations growth execution.

Language: Английский

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