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Artificial Intelligence (AI) in Cardiovascular Medicine

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A special issue of Medical Sciences (ISSN 2076-3271). This special issue belongs to the section "Cardiovascular Disease".

Deadline for manuscript submissions: 1 October 2026 | Viewed by 4795

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Dr. Nilima Rajpal Kundnani

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Guest Editor

1. Prevention and Cardiovascular Recovery, Department VI-Cardiology, University Clinic of Internal Medicine and Ambulatory Care, “Victor Babes” University of Medicine and Pharmacy, 300041 Timisoara, Romania
2. Research Centre of Timisoara Institute of Cardiovascular Diseases, "Victor Babes" University of Medicine and Pharmacy, 300041 Timisoara, Romania.
Interests: preventive medicine; cardiology; AI; evidence based medicine
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Dr. Mihaela Adela Iancu

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IInd Family Medicine Department, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
Interests: cardiology; family medicine; preventive medicine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, underscoring the urgent need for strategies that enable early detection and timely intervention. Recent advances in artificial intelligence (AI) and machine learning offer transformative opportunities to improve cardiovascular health by enabling more accurate, efficient, and scalable diagnostic tools. From the automated analysis of imaging and electrocardiographic data to predictive modeling using electronic health records and wearable technologies, AI is reshaping how clinicians identify risk factors, detect early pathological changes, and guide personalized treatment strategies.

This Special Issue invites contributions that showcase innovative AI methodologies for early CVD detection, risk stratification, and prognosis. Topics of interest include—but are not limited to—deep learning for medical imaging, AI-powered biomarkers, data integration across multimodal sources, predictive analytics, explainable AI in clinical practice, and applications in digital health and remote monitoring. Both methodological advances and clinically oriented studies are welcome. By bringing together research at the intersection of AI and cardiovascular medicine, this Special Issue aims to accelerate progress toward earlier diagnosis and improved patient outcomes.

Dr. Nilima Rajpal Kundnani
Dr. Mihaela Adela Iancu
Guest Editors

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Keywords

cardiovascular diseases
patient care
preventive cardiology
artificial intelligence
machine learning

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Published Papers (7 papers)

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Research

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11 pages, 14203 KB

Open AccessArticle

Vision-Capable LLMs in Microsurgery: A Blinded Comparison of Two AI Models with Expert Microsurgeons in the Appraisal of 200 Experimental Anastomoses

by Victor Esanu, Horatiu Alexandru Colosi, Stefan Agoston, Elisa Marziali, Radu Alexandru Ilies, Lorena Maria Hantig, Claudia Mihaela Paun, Alexandra Ioana Stoia, Alexia Onaciu, Iulia Cezara Pop, Cristina Maria Boznea, Ana-Maria Vartolomei, Farran Moustafa, Clemens Dirven, George Calin Dindelegan and Victor Volovici

Med. Sci. 2026, 14(2), 235; https://doi.org/10.3390/medsci14020235 (registering DOI) - 2 May 2026

Abstract

Background/Objectives: Objective end-product assessment of microsurgical anastomoses is intensive and partly subjective. Vision-capable large language models (LLMs) may enable standardized image-based scoring, but their agreement with expert assessment remains uncertain. Methods: We studied 200 end-to-end femoral artery anastomoses, performed on chicken legs by novice, intermediate, and experienced microsurgeons. Images were scored independently by two blinded expert panels; disagreements were adjudicated by a third senior reviewer to establish expert consensus. Two LLMs, ChatGPT 5.2 Thinking Extended and Gemini 3.1 Pro, were evaluated using the exact same prompt and rubric. Each image was analyzed three times per model. Final scores were aggregated by median for numeric items and majority vote for categorical items. The primary endpoint was exact-match agreement with expert consensus. Agreement within ±1 was also assessed for numeric items. Agreement was measured using simple percentage agreement, Light’s kappa, and Krippendorff’s alpha; Bland–Altman analysis was used for numeric count items. Results: LLM 1 achieved a higher overall exact-match agreement than LLM 2 (0.659 vs. 0.539). Both models performed better on categorical than numeric items (0.713 vs. 0.610 and 0.651 vs. 0.445, respectively). LLM 1 showed the greatest advantages for gaps, knots, oblique stitches, and wide bites. Krippendorff’s alpha was positive for most endpoints with LLM 1, whereas LLM 2 showed negative values throughout. Allowing a ±1 tolerance for numeric items greatly improved agreement, suggesting only minor counting discrepancies, from 0.610 to 0.900 for LLM 1 and from 0.445 to 0.826 for LLM 2. Conclusions: Under a constrained scoring workflow, LLMs partially approximated intraluminal microsurgical end-product scoring. LLM 1 outperformed LLM 2, but agreement remained insufficient to replace the expert assessment entirely. These models can be assistive tools within a human-in-the-loop framework. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)

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20 pages, 2647 KB

Open AccessArticle

Explainable Artificial Intelligence Unravels the Possible Distinct Roles of VKORC1 and CYP2C9 in Predicting Warfarin Anticoagulation Control

by Kannan Sridharan and Gowri Sivaramakrishnan

Med. Sci. 2026, 14(1), 156; https://doi.org/10.3390/medsci14010156 - 22 Mar 2026

Viewed by 450

Abstract

Background: Warfarin pharmacogenomics is critical due to its narrow therapeutic index and significant interpatient variability. While machine learning (ML) can predict anticoagulation control status (ACS), its “black-box” nature limits clinical translatability. Explainable Artificial Intelligence (XAI) addresses this by providing interpretable insights. This study applied ML and XAI to a warfarin pharmacogenomic dataset to predict poor ACS and explain model decisions. Methods: A post hoc analysis was conducted on a cross-sectional dataset of 232 patients receiving warfarin for ≥6 months. Data included age, gender, interacting drugs, SAMe-TT2R2 score, and genotypes for CYP2C9, VKORC1, and CYP4F2. Poor ACS was defined as time in therapeutic range (TTR) < 70%. The dataset was split into training (70%) and testing (30%) cohorts. Three models, Random Forest, XGBoost, and Logistic Regression, were developed and evaluated using AUC-ROC, sensitivity, and specificity. XAI techniques, including permutation importance and SHapley Additive exPlanations (SHAP), were employed for global and local interpretability. Results: Of 232 patients, 141 (60.8%) had poor ACS. XGBoost and Random Forest demonstrated comparable predictive accuracy (AUC-ROC: 0.67), outperforming Logistic Regression. Sensitivity was 0.83 and 0.79 for XGBoost and Random Forest, respectively. However, specificity was modest for both ensemble methods (Random Forest: 0.48; XGBoost: 0.41) and extremely low for Logistic Regression (0.04), indicating poor discrimination, particularly for identifying patients with adequate anticoagulation control. Globally, important predictors included the age, SAMe-TT2R2 score, CYP2C9 (*2/*2), female gender, and VKORC1 (C/T). XAI revealed predictions were primarily driven by VKORC1, CYP4F2, SAMe-TT2R2 scores, and drug interactions. Concordance between XAI predictions and actual ACS was 78% for adequate and 88.6% for poor ACS. SHAP analysis showed VKORC1 provided a stable risk signal (mean absolute SHAP: 1.44 ± 0.49 in concordant cases), while CYP2C9 was a high-variance, high-impact driver of discordance (mean SHAP: 3.44 ± 3.79 in discordant cases). Conclusions: ML models, particularly ensemble methods, show modest ability to predict poor warfarin control with limited ability to correctly identify patients with adequate control from our dataset. XAI transforms these models into interpretable tools, with SHAP analysis attributing predictions to specific genetic and clinical features. While predictive accuracy remains modest, this approach enhances transparency and provides a foundation for generating hypotheses that may ultimately support clinical decision-making in pharmacogenomic-guided warfarin therapy. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)

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14 pages, 1101 KB

Open AccessArticle

AI in the Hot Seat: Head-to-Head Comparison of Large Language Models and Cardiologists in Emergency Scenarios

by Vedat Cicek, Lili Zhao, Yalcin Tur, Ahmet Oz, Sahhan Kilic, Gorkem Durak, Faysal Saylik, Mert Ilker Hayiroglu, Tufan Cinar and Ulas Bagci

Med. Sci. 2026, 14(1), 33; https://doi.org/10.3390/medsci14010033 - 8 Jan 2026

Viewed by 641

Abstract

Background: The clinical applicability of large language models (LLMs) in high-stakes cardiac emergencies remains unexplored. This study evaluated how well advanced LLMs perform in managing complex catheterization laboratory (Cath lab) scenarios and compared their performance with that of interventional cardiologists. Methods and Results: A cross-sectional study was conducted from 20 June to 2 December 2024. Twelve challenging inferior myocardial infarction scenarios were presented to seven LLMs (ChatGPT, Gemini, LLAMA, Qwen, Bing, Claude, DeepSeek) and five early-career interventional cardiologists. Responses were standardized, anonymized, and evaluated by thirty experienced interventional cardiologists. Performance comparisons were analyzed using a linear mixed-effects model with correlation and reliability statistics. Physicians had an average reference score of 80.68 (95% CI 76.3–85.0). Among LLMs, ChatGPT ranked highest (87.4, 95% CI 82.5–92.3), followed by Claude (80.8, 95% CI 75.7–85.9) and DeepSeek (78.7, 95% CI 72.9–84.6). LLAMA (73.7), Qwen (66.2), and Bing (64.3) ranked lower, while Gemini scored the lowest (59.0). ChatGPT scored higher than the early-career physician comparator group (difference 6.69, 95% CI 0.00–13.37; p < 0.05), whereas Gemini, LLAMA, Qwen, and Bing performed significantly worse; Claude and DeepSeek showed no significant difference. Conclusions: This expanded assessment reveals significant variability in LLM performance. In this simulated setting, ChatGPT demonstrated performance comparable to that of early-career interventional cardiologists. These results suggest that LLMs could serve as supplementary decision-support tools in interventional cardiology under simulated conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)

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11 pages, 548 KB

Open AccessArticle

Risk of Chronic Kidney Disease and Implications in Patients with Atrial Fibrillation for the Development of Major Adverse Cardiovascular Events with Machine Learning

by Pedro Moltó-Balado, Josep-Lluís Clua-Espuny, Carlos Tarongi-Vidal, Paula Barrios-Carmona, Victor Alonso-Barberán, Maria-Teresa Balado-Albiol, Andrea Simeó-Monzó, Jorge Canela-Royo and Alba del Barrio-González

Med. Sci. 2025, 13(4), 289; https://doi.org/10.3390/medsci13040289 - 27 Nov 2025

Cited by 2 | Viewed by 800

Abstract

Background: Atrial fibrillation (AF) and chronic kidney disease (CKD) often overlap and may amplify cardiovascular risk. Whether renal dysfunction should be incorporated into composite cardiovascular endpoints in AF remains uncertain. We aimed to quantify AF-associated risk of MACE and evaluate the incremental prognostic value of kidney measures (eGFR and albuminuria) to inform composite outcomes and clinical management. Methods: We performed a retrospective, community-based cohort study of 40,297 adults aged 65–95 years. Individuals with incident AF (n = 2574) were followed for 5 years. MACE and components were ascertained from linked health records; only events after AF diagnosis were analyzed. Cox models estimated adjusted hazard ratios (HRs). Risk was further stratified by eGFR stages and urine albumin-to-creatinine ratio (UACR) categories. Exploratory machine learning (ML) was developed to predict MACE in patients with AF and CKD, with model interpretability assessed by feature importance analysis. Results: Incident AF was associated with higher risk of MACE (HR 3.52), CKD (HR 1.97) and all-cause mortality (HR 1.14). CKD was nearly twice more frequent in AF than in non-AF (30.9% vs. 14.5%; p < 0.001). Among patients with AF, a graded eGFR–risk relationship was observed: compared with higher eGFR, MACE risk increased across G3a–G5, peaking in G5 (HR 2.08). Albuminuria showed a parallel gradient: versus UACR <30 mg/g, UACR 30–299 mg/g and ≥300 mg/g were associated with an increased risk of MACE (HR 1.51 and 1.76, respectively). Conclusions: Newly diagnosed AF confers a substantial excess risk of MACE and its components. The consistent eGFR and albuminuria in AF support considering clinically meaningful renal endpoints within composite outcomes and prioritizing integrated cardiorenal management. These findings provide actionable evidence to refine risk stratification and endpoint selection in AF research and care. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)

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Review

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17 pages, 335 KB

Open AccessReview

The Role of the Cardiothoracic Surgeon in the Age of AI—Are the Robots Going to Take Our Jobs?

by Caius-Glad Streian, Vlad-Alexandru Meche, Horea Bogdan Feier, Dragos Cozma, Ciprian Nicușor Dima, Constantin Tudor Luca and Sergiu-Ciprian Matei

Med. Sci. 2026, 14(2), 164; https://doi.org/10.3390/medsci14020164 - 25 Mar 2026

Viewed by 816

Abstract

Introduction: Artificial intelligence (AI) and robot-assisted platforms are increasingly influencing cardiothoracic surgery. AI enhances risk prediction, imaging interpretation, and early complication detection, while robotics improves visualization, dexterity, and minimally invasive access. This systematic review evaluates the current evidence supporting these technologies and their implications for clinical practice. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Web of Science, and Google Scholar (January 2000–May 2025) following PRISMA 2020 guidelines. After screening and eligibility assessment, 67 studies met predefined inclusion criteria and were incorporated into the qualitative synthesis. Additional high-impact reviews and consensus documents were consulted for contextual interpretation. Results: Machine learning models demonstrated modest but consistent improvements in predictive performance compared with EuroSCORE II and STS scores, particularly in high-risk cohorts. Robot-assisted mitral and coronary procedures showed reduced postoperative pain, blood loss, ICU stay, and recovery time in experienced centers, though early learning phases were associated with longer operative, cross-clamp, and bypass times. AI-enabled intraoperative tools, such as video analysis, workflow recognition, and real-time anatomical segmentation, emerged as promising adjuncts for surgical precision. Structured robotic training programs, especially simulation-based and dual-console pathways, accelerated proficiency acquisition. Conclusions: AI and robotic systems act as augmentative technologies that enhance rather than replace the surgeon’s role. Their safe and effective adoption requires standardized training, transparent AI decision pathways, and clear ethical and medico-legal governance. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)

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23 pages, 2115 KB

Open AccessReview

Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support

by Minodora Teodoru, Alexandra-Kristine Tonch-Cerbu, Dragoș Cozma, Cristina Văcărescu, Raluca-Daria Mitea, Florina Batâr, Horea-Laurentiu Onea, Florin-Leontin Lazăr and Alina Camelia Cătană

Med. Sci. 2026, 14(1), 132; https://doi.org/10.3390/medsci14010132 - 11 Mar 2026

Viewed by 832

Abstract

Cardiovascular imaging is a cornerstone of modern cardiology, yet its clinical impact is limited by operator dependence, inter-observer variability, time-consuming workflows, and unequal access to advanced expertise. Artificial intelligence (AI), particularly machine learning and deep learning, offers new opportunities to overcome these limitations. This review aims to summarize current and emerging AI applications in cardiovascular imaging and to evaluate their potential clinical value in precision diagnostics and decision support. This narrative review synthesizes clinically relevant literature on AI applications across major cardiovascular imaging modalities, including echocardiography, cardiovascular magnetic resonance, cardiac computed tomography, and nuclear cardiology. Evidence was analyzed with a focus on AI-enabled acquisition support, image segmentation, quantitative and functional assessment, workflow automation, and risk stratification, alongside key methodological and implementation considerations. Across imaging modalities, AI-driven approaches have demonstrated improved reproducibility, efficiency, and scalability of cardiovascular imaging workflows. Automated algorithms reduce operator dependence, facilitate standardized extraction of imaging biomarkers, and support advanced functional assessment and prognostic stratification. Recent developments in video-based, temporal, and multimodal models further expand AI capabilities from technical automation toward integrated disease phenotyping and personalized clinical decision support. However, translation into routine practice remains limited by heterogeneous datasets, insufficient external validation, algorithmic bias, limited interpretability, and challenges related to regulatory approval and workflow integration. Artificial intelligence has the potential to reshape cardiovascular imaging into a more efficient, reproducible, and patient-centered precision medicine tool. Real-world clinical impact will depend on outcome-driven evaluation, robust external validation, multimodal data integration, and human-in-the-loop implementation strategies that ensure safe, equitable, and clinically meaningful adoption. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)

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Other

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15 pages, 2759 KB

Open AccessSystematic Review

Diagnostic Performance of Angiography-Derived Quantitative Flow Ratio: A Systematic Review and Meta-Analysis

by Guo Huang, Pu Ge, He Zhu, Sheng Han and Luwen Shi

Med. Sci. 2026, 14(1), 51; https://doi.org/10.3390/medsci14010051 - 19 Jan 2026

Viewed by 597

Abstract

Background: Quantitative flow ratio (QFR) is a novel technology to assess the functional significance of coronary stenoses based on standard coronary angiography, which can be alternatives to invasive fractional flow reserve (FFR) assessment. However, the evidence is limited to single-center studies and small sample sizes. This study systematically determined the diagnostic performance of QFR to diagnose functionally significant stenosis with FFR as the reference standard. Methods: A systematic review and meta-analysis of studies assessing the diagnostic performance of angiography-derived QFR systems were performed. All relevant studies from six literature databases were searched and screened according to the inclusion and exclusion criteria. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR−), and diagnostic odds ratio (DOR), along with their 95% confidence intervals (CIs), were calculated using DerSimonian–Laird methodology. The summary receiver operating characteristic (SROC) curve and area under the curve were estimated. Meta-regression analysis was performed to identify a potential source of heterogeneity. Results: Fifty-seven studies comprising 13,215 patients and 16,125 vessels were included in the final analysis. At the vessel level, the pooled sensitivity and specificity of QFR for detecting a significant coronary stenosis were 0.826 (95% CI: 0.798–0.851) and 0.919 (95% CI: 0.902–0.933). Pooled LR+ and LR− were 10.198 (95% CI: 8.469–12.281) and 0.189 (95% CI: 0.163–0.219), with a pooled DOR of 53.968 (95% CI: 42.888–67.910). The SROC revealed an area under the curve (AUC) of 0.94 (95% CI: 0.91–0.96). The summary AUCs were 0.90 (95% CI: 0.87–0.92) for fixed-flow QFR (fQFR), 0.95 (95% CI: 0.92–0.96) for contrast-flow QFR (cQFR), 0.97 (95% CI: 0.95–0.98) for Murray law-based QFR (μQFR), and 0.91 (95% CI: 0.89–0.94) for non-specified QFR. The adjusted pooled DORs were as follows: 126.25 for μQFR, 45.49 for cQFR, 26.12 for adenosine-flow QFR (aQFR), 25.88 for fQFR, and 36.54 for non-specified QFR. Conclusions: The accuracy of angiography-derived QFR was strong to assess the functional significance of coronary stenoses with FFR as a reference. μQFR demonstrated the highest diagnostic performance among the five evaluated modes. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) in Cardiovascular Medicine)

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