Artificial Intelligence in Cardiovascular Care—Part 2: Applications

Dissecting racial bias in an algorithm used to manage the health of populations

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Unequal treatment: confronting racial and ethnic disparities in health care

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Health Care Disparities

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Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

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Large language models encode clinical knowledge

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Clinical use of current polygenic risk scores may exacerbate health disparities

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Fair prediction with disparate impact: a study of bias in recidivism prediction instruments

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Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation

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Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms

Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmms2004740 · Cited by 1,731 · see citing works

The false hope of current approaches to explainable artificial intelligence in health care

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724 · see citing works

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

Zech, J. R., Badgeley, M. A., Liu, M., Costa, A. B., Titano, J. J., & Oermann, E. K. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002683 · Cited by 1,719 · see citing works

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Algorithmic Decision Making and the Cost of Fairness

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Inherent trade-offs in the fair determination of risk scores

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Inherent Trade-Offs in the Fair Determination of Risk Scores

Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016) · arXiv · doi:10.48550/arxiv.1609.05807 · Cited by 941 · see citing works

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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera, S., Liu, X., Chan, A.-W., Denniston, A. K., Calvert, M. J., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Darzi, A., Holmes, C., Yau, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1037-7 · Cited by 748 · see citing works

Ethical Machine Learning in Healthcare

Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092820-114757 · Cited by 645 · see citing works

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., & Vardoulakis, L. M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376718 · Cited by 614 · see citing works

PaLM-E: An Embodied Multimodal Language Model

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591 · see citing works

A Validated Risk Score for In-Hospital Mortality in Patients With Heart Failure From the American Heart Association Get With the Guidelines Program

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Disparities in dermatology AI performance on a diverse, curated clinical image set

Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., et al. (2022) · Science Advances · doi:10.1126/sciadv.abq6147 · Cited by 544 · see citing works

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works

How Is ChatGPT’s Behavior Changing Over Time?

Chen, L., Zaharia, M., & Zou, J. (2024) · Harvard Data Science Review · doi:10.1162/99608f92.5317da47 · Cited by 507 · see citing works

Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation

Guo, Y., Wang, H., Zhang, H., Liu, T., Liang, Z., Xia, Y., Yan, L., Xing, Y., Shi, H., Li, S., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.08.019 · Cited by 489 · see citing works

Proceedings of the 27th International Conference on Neural Information Processing Systems

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Drawbacks of artificial intelligence and their potential solutions in the healthcare sector.

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The Society of Thoracic Surgeons 2018 Adult Cardiac Surgery Risk Models: Part 1—Background, Design Considerations, and Model Development

Shahian, D. M., Jacobs, J. P., Badhwar, V., Kurlansky, P. A., Furnary, A. P., Cleveland, J. C., Lobdell, K. W., Vassileva, C., Wyler von Ballmoos, M. C., Thourani, V. H., et al. (2018) · The Annals of Thoracic Surgery · doi:10.1016/j.athoracsur.2018.03.002 · Cited by 459 · see citing works

Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial

Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01335-4 · Cited by 424 · see citing works

Ambient artificial intelligence scribes to alleviate the burden of clinical documentation.

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Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study

Lubitz, S. A., Faranesh, A. Z., Selvaggi, C. A., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., McConnell, M. V., Pantelopoulos, A., & Foulkes, A. S. (2022) · Circulation · doi:10.1161/circulationaha.122.060291 · Cited by 380 · see citing works

Presenting machine learning model information to clinical end users with model facts labels

Sendak, M. P., Gao, M., Brajer, N., & Balu, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0253-3 · Cited by 279 · see citing works

Clinical Implications of Revised Pooled Cohort Equations for Estimating Atherosclerotic Cardiovascular Disease Risk

Yadlowsky, S., Hayward, R. A., Sussman, J. B., McClelland, R. L., Min, Y., & Basu, S. (2018) · Annals of Internal Medicine · doi:10.7326/m17-3011 · Cited by 271 · see citing works

An algorithmic approach to reducing unexplained pain disparities in underserved populations

Pierson, E., Cutler, D. M., Leskovec, J., Mullainathan, S., & Obermeyer, Z. (2021) · Nature Medicine · doi:10.1038/s41591-020-01192-7 · Cited by 268 · see citing works

Blinded, randomized trial of sonographer versus AI cardiac function assessment

He, B., Kwan, A. C., Cho, J. H., Yuan, N., Pollick, C., Shiota, T., Ebinger, J., Bello, N. A., Wei, J., Josan, K., et al. (2023) · Nature · doi:10.1038/s41586-023-05947-3 · Cited by 256 · see citing works

Vision–language foundation model for echocardiogram interpretation

Christensen, M., Vukadinovic, M., Yuan, N., & Ouyang, D. (2024) · Nature Medicine · doi:10.1038/s41591-024-02959-y · Cited by 231 · see citing works

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist

Sengupta, P. P., Shrestha, S., Berthon, B., Messas, E., Donal, E., Tison, G. H., Min, J. K., D’hooge, J., Voigt, J., Dudley, J. T., et al. (2020) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2020.07.015 · Cited by 229 · see citing works

Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M., Castro, D. C., Boecking, B., Sharma, H., Bouzid, K., Thieme, A., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01442 · Cited by 227 · see citing works

Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines

Ibrahim, H. F., Liu, X., Rivera, S. C., Moher, D., Chan, A., Sydes, M. R., Calvert, M., & Denniston, A. K. (2021) · Trials · doi:10.1186/s13063-020-04951-6 · Cited by 195 · see citing works

An empirical characterization of fair machine learning for clinical risk prediction

Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103621 · Cited by 164 · see citing works

Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies

Xie, F., Yuan, H., Ning, Y., Ong, M. E. H., Feng, M., Hsu, W., Chakraborty, B., & Liu, N. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103980 · Cited by 159 · see citing works

Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease

van Smeden, M., Heinze, G., CALSTER, B. V., Asselbergs, F. W., Vardas, P. E., Bruining, N., de Jaegere, P. P. T., Moore, J. H., Denaxas, S., Boulesteix, A., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac238 · Cited by 147 · see citing works

Paying for artificial intelligence in medicine

Parikh, R. B., & Helmchen, L. A. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00609-6 · Cited by 132 · see citing works

Defining AMIA’s artificial intelligence principles

Solomonides, A. E., Koski, E., Atabaki, S. M., Weinberg, S., McGreevey, J. D., Kannry, J. L., Petersen, C., & Lehmann, C. U. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac006 · Cited by 121 · see citing works

A framework for the oversight and local deployment of safe and high-quality prediction models

Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O’Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac078 · Cited by 119 · see citing works

Bridging the chasm between AI and clinical implementation

Aristidou, A., Jena, R., & Topol, E. J. (2022) · The Lancet · doi:10.1016/s0140-6736(22)00235-5 · Cited by 115 · see citing works

A Nationwide Network of Health AI Assurance Laboratories

Shah, N. H., Halamka, J. D., Saria, S., Pencina, M., Tazbaz, T., Tripathi, M., Callahan, A., Hildahl, H., & Anderson, B. (2024) · JAMA · doi:10.1001/jama.2023.26930 · Cited by 114 · see citing works

Clinical Prediction Models for Cardiovascular Disease

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Characterizing the clinical adoption of medical AI devices through U.S. insurance claims

Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C., Glass, L., Sun, J., & Zou, J. (2024) · NEJM AI · doi:10.1056/aioa2300030 · Cited by 103 · see citing works

Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?

Watson, J., Hutyra, C. A., Clancy, S. M., Chandiramani, A., Bedoya, A., Ilangovan, K., Nderitu, N., & Poon, E. G. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooz046 · Cited by 95 · see citing works

Using ChatGPT to Facilitate Truly Informed Medical Consent

Mirza, F. N., Tang, O. Y., Connolly, I. D., Abdulrazeq, H. A., Lim, R. K., Roye, G. D., Priebe, C. J., Chandler, C., Libby, T. J., Groff, M. W., et al. (2024) · NEJM AI · doi:10.1056/aics2300145 · Cited by 90 · see citing works

The association between use of ambient voice technology documentation during primary care patient encounters, documentation burden, and provider burnout

Owens, L. M., Wilda, J. J., Hahn, P. Y., Koehler, T., & Fletcher, J. J. (2024) · Family Practice · doi:10.1093/fampra/cmad092 · Cited by 86 · see citing works

Who Will Pay for AI?

Chen, M., Golding, L. P., & Nicola, G. N. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021210030 · Cited by 82 · see citing works

Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A. A., & Uhler, C. (2023) · Nature Communications · doi:10.1038/s41467-023-38125-0 · Cited by 82 · see citing works

Radiology-GPT: A large language model for radiology

Liu, Z., Li, Y., Shu, P., Zhong, A., Jiang, H., Pan, Y., Yang, L., Ju, C., Wu, Z., Ma, C., et al. (2025) · Meta-Radiology · doi:10.1016/j.metrad.2025.100153 · Cited by 78 · see citing works

Guiding principles for the responsible development of artificial intelligence tools for healthcare

Badal, K., Lee, C. M., & Esserman, L. J. (2023) · Communications Medicine · doi:10.1038/s43856-023-00279-9 · Cited by 77 · see citing works

Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy

Soto, J. T., Weston Hughes, J., Sanchez, P. A., Perez, M., Ouyang, D., & Ashley, E. A. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac033 · Cited by 76 · see citing works

Do We Still Need Clinical Language Models?

Lehman, E., Hernandez, E., Mahajan, D., Wulff, J., Smith, M. J., Ziegler, Z., Nadler, D., Szolovits, P., Johnson, A., & Alsentzer, E. (2023) · arXiv · doi:10.48550/arxiv.2302.08091 · Cited by 62 · see citing works

Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors

Khor, S., Haupt, E. C., Hahn, E. E., Lyons, L. J., Shankaran, V., & Bansal, A. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.18495 · Cited by 59 · see citing works

Radiology-GPT: A Large Language Model for Radiology

Liu, Z., Zhong, A., Li, Y., Yang, L., Ju, C., Wu, Z., Ma, C., Shu, P., Chen, C., Kim, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2306.08666 · Cited by 59 · see citing works

A survey of extant organizational and computational setups for deploying predictive models in health systems

Kashyap, S., Morse, K., Patel, B., & Shah, N. H. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab154 · Cited by 47 · see citing works

Artificial intelligence–enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges

Schepart, A., Burton, A., Durkin, L., Fuller, A., Charap, E., Bhambri, R., & Ahmad, F. S. (2023) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2023.04.003 · Cited by 40 · see citing works

XrayGPT: Chest Radiographs Summarization using Medical Vision-Language ModelsPreprint

Thawakar, O., Shaker, A., Mullappilly, S. S., Cholakkal, H., Anwer, R. M., Khan, S., Laaksonen, J. T., & Khan, F. S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2306.07971 · Cited by 40 · see citing works

Coarse race data conceals disparities in clinical risk score performancePreprint

Movva, R., Shanmugam, D., Hou, K., Pathak, P., Guttag, J., Garg, N., & Pierson, E. (2023) · Machine Learning in Health Care · doi:10.48550/arxiv.2304.09270 · Cited by 37 · see citing works

Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimation

Foryciarz, A., Pfohl, S. R., Patel, B., & Shah, N. H. (2022) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100460 · Cited by 36 · see citing works

Using synthetic data generation to train a cardiac motion tag tracking neural network

Loecher, M., Perotti, L. E., & Ennis, D. B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102223 · Cited by 35 · see citing works

Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare

Tipton, K. N., Leas, B. F., Flores, E. J., Jepson, C., Aysola, J., Cohen, J. B., Harhay, M. O., Schmidt, H., Weissman, G. E., Treadwell, J. R., et al. (2023) · doi:10.23970/ahrqepccer268 · Cited by 35 · see citing works

Artificial Intelligence for Cardiac Imaging-Genetics Research

de Marvao, A., Dawes, T. J. W., & O’Regan, D. P. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2019.00195 · Cited by 30 · see citing works

Untitled work

(2023) · doi:10.1001/jama.2023.25051 · Cited by 21 · see citing works

Intelligence-Based Cardiology and Cardiac Surgery

(2023) · Elsevier eBooks · doi:10.1016/c2020-0-03605-4 · Cited by 12 · see citing works

CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AIPreprint

Soin, A., Merkow, J., Long, J., Cohen, J., Saligrama, S., Kaiser, S., Borg, S., Tarapov, I., & Lungren, M. P. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.02833 · Cited by 12 · see citing works

Prescription digital therapeutics: Applying Medicaid experience to value assessment and formulary management

Salsabili, M., Tesell, M., Alcusky, M. J., Greenwood, B. C., Huang, D., Lenz, K., & Dave, J. (2023) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2023.29.6.685 · Cited by 8 · see citing works

Multimodal Foundation Models For Echocardiogram InterpretationPreprint

Christensen, M., Vukadinovic, M., Yuan, N., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.15670 · Cited by 6 · see citing works

Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care

Mullainathan, S., & Obermeyer, Z. (2021) · The Quarterly Journal of Economics · doi:10.1093/qje/qjab046 · Cited by 6 · see citing works

Quantifying Inequality in Underreported Medical Conditions.Preprint

Shanmugam, D., & Pierson, E. (2021) · arXiv (Cornell University) · Cited by 4 · see citing works

How Health Systems Decide to Use Artificial Intelligence for Clinical Decision Support

Gonzalez-Smith, J., Shen, H., Singletary, E. A., & Silcox, C. (2022) · NEJM Catalyst · doi:10.1056/cat.21.0416 · Cited by 4 · see citing works

Artificial Intelligence in Cardiovascular Clinical Trials

Cunningham, J. W., Abraham, W. T., Bhatt, A. S., Dunn, J., Felker, G. M., Jain, S. S., Lindsell, C. J., Mace, M., Martyn, T., Shah, R. U., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.08.069 · Cited by 52 · see citing works

Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence

Marvasti, T. B., Gao, Y., Murray, K. R., Hershman, S., McIntosh, C., & Moayedi, Y. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.009 · Cited by 35 · see citing works

Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease

Mayourian, J., Gearhart, A. S., Cava, W. L., Vaid, A., Nadkarni, G. N., Triedman, J. K., Powell, A. J., Wald, R. M., Valente, A. M., Geva, T., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.062 · Cited by 34 · see citing works

Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease

Mayourian, J., El‐Bokl, A., Lukyanenko, P., Cava, W. L., Geva, T., Valente, A. M., Triedman, J. K., & Ghelani, S. J. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae651 · Cited by 34 · see citing works

AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring

Nechita, L., Tutunaru, D., Nechita, A., Voipan, A. E., Voipan, D., Tupu, A. E., & Mușat, C. L. (2025) · Diagnostics · doi:10.3390/diagnostics15060787 · Cited by 29 · see citing works

Prospective Human Validation of Artificial Intelligence Interventions in Cardiology

Moosavi, A., Huang, S., Vahabi, M., Motamedivafa, B., Tian, N., Mahmood, R., Liu, P. X., & Sun, C. L. F. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.101202 · Cited by 24 · see citing works

Advancing Cardiovascular, Kidney, and Metabolic Medicine: A Narrative Review of Insights and Innovations for the Future

Fernando, K., Connolly, D. L., Darcy, E., Evans, M., Hinchliffe, W., Holmes, P., & Strain, W. D. (2025) · Diabetes Therapy · doi:10.1007/s13300-025-01738-3 · Cited by 24 · see citing works

Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory From the American Heart Association

Jain, S. S., Goto, S., Hall, J. L., Khan, S. S., MacRae, C. A., Ofori, C., Pegus, C., Pencina, M., Peterson, E. D., Schwamm, L. H., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001400 · Cited by 23 · see citing works

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

Parsa, S., Somani, S. S., Dudum, R., Jain, S. S., & Rodríguez, F. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01210-w · Cited by 22 · see citing works

Randomized Controlled Trials Evaluating Artificial Intelligence in Cardiovascular Care

Barzilai, D. H., Sudri, K., Goshen, G., Klang, E., Zimlichman, E., Barbash, I. M., & Cohen, M. S. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102152 · Cited by 20 · see citing works

Artificial intelligence-driven clinical decision support systems to assist healthcare professionals and people with diabetes in Europe at the point of care: a Delphi-based consensus roadmap

Bajramagic, M., Battelino, T., Cos, X., Coté, M., Cui, N., Forbes, A., Galderisi, A., Heinemann, L., Hussain, S., Imbert, J., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06601-5 · Cited by 17 · see citing works

Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review

van der Waerden, R. G. A., Volleberg, R. H. J. A., Luttikholt, T. J., Cancian, P., van der Zande, J. L., Stone, G. W., Holm, N. R., Kedhi, E., Escaned, J., Pellegrini, D., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf005 · Cited by 16 · see citing works

Artificial Intelligence in the Management of Heart Failure

Cheema, B. S., Hourmozdi, J., Kline, A. S., Ahmad, F. S., & Khera, R. (2025) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2025.02.020 · Cited by 16 · see citing works

Transparent and Robust Artificial Intelligence-Driven Electrocardiogram Model for Left Ventricular Systolic Dysfunction

Lee, M. S., Jang, J.-H., Kang, S., Han, G. I., Yoo, A.-H., Jo, Y., Son, J. M., Kwon, J., Lee, S., Lee, J. S., et al. (2025) · Diagnostics · doi:10.3390/diagnostics15151837 · Cited by 15 · see citing works

Artificial Intelligence for Cardiovascular Care in Action

Biswas, D., Thangaraj, P., Pedroso, A. F., Ogunniyi, M. O., Maddox, T. M., Ahmad, F. S., & Khera, R. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102307 · Cited by 15 · see citing works

An institutional framework to support ethical fair and equitable artificial intelligence augmented care

Dykstra, S., MacDonald, M., Beaudry, R. I., Labib, D., King, M., Feng, Y., Flewitt, J. A., Bakal, J. A., Lee, B., Dean, S., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01490-9 · Cited by 12 · see citing works

Cardiovascular disease prevention in China: challenges and opportunities in the artificial intelligence-enabled digital health era

Zhao, D. H., Zhang, Y., Wang, J., He, L., He, Y., & Ma, C. (2025) · Nature Reviews Cardiology · doi:10.1038/s41569-025-01222-2 · Cited by 11 · see citing works

BioBricks.ai: a versioned data registry for life sciences data assets

Gao, Y., Mughal, Z., Jaramillo-Villegas, J. A., Corradi, M., Borrel, A., Lieberman, B., Sharif, S., Shaffer, J., Fecho, K., Chatrath, A., et al. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1599412 · Cited by 10 · see citing works

Artificial intelligence in assisting pathogenic microorganism diagnosis and treatment: a review of infectious skin diseases

Han, R., Fan, X., Ren, S., & Niu, X. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1467113 · Cited by 9 · see citing works

Immunotherapy-Associated Cardiotoxicity: Current Insights and Future Directions for Precision Cardio-Oncology

Stefanou, E., Tsitsinakis, G., Karageorgou, D., & Kole, C. (2025) · Cancers · doi:10.3390/cancers17172838 · Cited by 9 · see citing works

Digital health innovation and artificial intelligence in cardiovascular care: a case-based review

Grant, J. K., Javaid, A., Carrick, R. T., Koester, M., Kassamali, A. A., Kim, C. H., Isakadze, N., Wu, K. C., Blaha, M. J., Whelton, S. P., et al. (2024) · npj Cardiovascular Health · doi:10.1038/s44325-024-00020-y · Cited by 9 · see citing works

Machine learning based insights into cardiomyopathy and heart failure research: a bibliometric analysis from 2005 to 2024

Akram, M. J., Nawaz, A., Yuxing, Y., Zhang, J., Huang, H., Liu, L., Qian, X., & Tian, J. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1602077 · Cited by 6 · see citing works

The Anatomy of Coronary Risk: How Arterial Geometry Shapes Coronary Artery Disease Through Blood Flow Haemodynamics – Latest Methods, Insights and Clinical Implications

Shen, C., Zhang, M., Keramati, H., Zhang, S., Gharleghi, R., Wentzel, J. J., Khan, M. O., Morbiducci, U., Qayyum, A., Niederer, S., et al. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10530-w · Cited by 6 · see citing works

Belgian position paper on implementing artificial intelligence in cardiology: a roadmap from theory to clinical practice

Dujardin, K., Heyde, L. V., CARLIER, S. G., Bertrand, P. B., Koopman, P., Nedoszytko, M., Casado-Arroyo, R., & Jaeger, P. D. (2025) · Acta cardiologica. Supplementum · doi:10.1080/00015385.2025.2576441 · Cited by 6 · see citing works

Digital Therapeutics in Cardiovascular Healthcare: A Narrative Review

Shao, X., Hu, Y., Jia, H., & Song, J. (2025) · Current Cardiology Reports · doi:10.1007/s11886-025-02270-3 · Cited by 5 · see citing works

Artificial intelligence in cardiovascular medicine: An exoskeleton for perception, reasoning and action

Desroche, L., Trimaille, A., Garban, T., & Bouleti, C. (2025) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2025.07.012 · Cited by 5 · see citing works

AI-enhanced electrocardiography as a digital biomarker platform in cardiovascular medicine: clinical applications, validation gaps and future implementation pathways

Venturiello, D., Vignaroli, W., & Nasso, G. (2026) · Open Heart · doi:10.1136/openhrt-2026-004211 · Cited by 5 · see citing works

Synthetic generation of cardiac tissue motion from surface electrocardiograms

Radhakrishnan, A., Yanamala, N. V. K., Jamthikar, A. D., Wang, Y., East, S.-A., Hamirani, Y. S., Maganti, K., & Sengupta, P. P. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00629-x · Cited by 5 · see citing works

Artificial Intelligence-Enabled Electrocardiography in Practice: A State-of-the-Art Review

Lee, H. S., Croon, P. M., Lee, M. S., Poterucha, T. J., Lin, C. H., Son, J. M., Jeon, K. H., Tarabanis, C., Lee, S., Kim, K.-H., et al. (2026) · Korean Circulation Journal · doi:10.4070/kcj.2025.0486 · Cited by 4 · see citing works

Multimodal Generative AI in Diagnostics: Bridging Medical Imaging and Clinical ReasoningPreprint

Maleki, M., & Ghahari, S. (2025) · Preprints.org · doi:10.20944/preprints202508.1425.v1 · Cited by 4 · see citing works

Comparative performance of twelve machine learning models in predicting COVID-19 mortality risk in children: a population-based retrospective cohort study in Brazil

dos Santos, A. L., Oliveira, M. C. L., Colosimo, E. A., MAK, R. H. K., Pinhati, C. C., Galante, S. C., Martelli‐Júnior, H., Silva, A. C. S. E., & OLIVEIRA, E. A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2916 · Cited by 3 · see citing works

Current and future use of artificial intelligence in valvular heart disease imaging

Sengupta, P. P., Poterucha, T. J., Pezel, T., Tsang, T. S. M., & Cosyns, B. (2025) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeaf348 · Cited by 3 · see citing works

The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging

Sejo, C. S., Randazzo, M., Lang, R. M., & Slivnick, J. A. (2026) · Current Cardiology Reports · doi:10.1007/s11886-025-02344-2 · Cited by 3 · see citing works

Machine Learning in Postcardiotomy Shock: Implications for Temporary Mechanical Circulatory Support

Saggu, J. S., Herrera, H. R., Meng, Z., Wieruszewski, P. M., Ursoleo, J. D., Al-Ani, R. I., Nabzdyk, C. S., Comfere, T. B., Bignami, E. G., Wollborn, J., et al. (2026) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2026.05.005 · Cited by 3 · see citing works

Advancing Cardiovascular Disease Prediction Machine Learning Models With Psychological Factors

Krive, J., & Chertok, D. L. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.101185 · Cited by 3 · see citing works

Artificial intelligence: a promising tool for the clinical cardiologist

Escobar, C., Fácila, L., Vidal-Pérez, R., Lapeña, A. P., Vivas, D., Martín, A. G., Manzano‐Fernández, S., Caballero, E. G., Barrios, V., & Freixa‐Pamias, R. (2025) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2025.2520830 · Cited by 2 · see citing works

Ethical Challenges and Current Opportunities of Artificial Intelligence in Cardiology

Chamsi‐Pasha, M. A., & Chamsi‐Pasha, H. (2025) · Avicenna Journal of Medicine · doi:10.1055/s-0045-1809879 · Cited by 2 · see citing works

Monitoring and Follow-Up of Patients on Vitamin K Antagonist Oral Anticoagulant Therapy Using Artificial Intelligence: The AIto-Control Project

Romero-Arana, A., Romero-Sibajas, N., Arroyo-Bello, E., Ruiz, A. R., & Gómez-Salgado, J. C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14207191 · Cited by 2 · see citing works

Clinical Decision-Making of Artificial Intelligence vs Medical Professionals in Patients With Syncope

van Zanten, S., Boel, T. T., de Jong, J. S. Y., Bais, B., Fedorowski, A., Sutton, R., Selder, J. L., Giele, F., Geertsma, C., Scheffer, M. G., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102426 · Cited by 2 · see citing works

The role of artificial intelligence in early detection and risk prediction of ischemic heart disease

Azami, P., Kojuri, J., & Razeghian‐Jahromi, I. (2026) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000004812 · Cited by 2 · see citing works

A responsible artificial intelligence framework for translational readiness of machine learning biomarker models in clinical decision support

Habibzadehomran, V. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02035-z · Cited by 2 · see citing works

Contemporary Multi-modality Imaging of Prosthetic Aortic Valves

Abadie, B. Q., & Wang, T. K. M. (2025) · Reviews in Cardiovascular Medicine · doi:10.31083/rcm25339 · Cited by 2 · see citing works

Artificial Intelligence in Predicting Sudden Cardiac Death

Tran, H. H.-V., Thu, A., Twayana, A. R., Fuertes, A., Gonzalez, M., James, M., Basta, M., Mehta, K. A., Frishman, W. H., & Aronow, W. S. (2025) · Cardiology in Review · doi:10.1097/crd.0000000000001014 · Cited by 2 · see citing works

Answering the Call

Skalidis, I., Maurizi, N., Dangas, G., & Chatzizisis, Y. S. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.06.051 · Cited by 2 · see citing works

Guiding the AI Revolution in Cardiovascular Medicine

Krumholz, H. M. (2025) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2025.08.021 · Cited by 2 · see citing works

AI-driven Innovations in Cardiology: A Comprehensive Review of Effectiveness and Challenges

Ismael, A., Azzawi, M., Abdulaaima, A., Mohammed, M., Ullah, M., & Ihtishamul, H. (2025) · Clinical Medicine Research · doi:10.11648/j.cmr.20251404.15 · Cited by 1 · see citing works

Evolution of Heart Rate Variability (HRV) Analysis Methodologies (2000–2025): From Task Force Standards to Wearables, AI, and Clinical Translation

Zhou, Z., Wang, Y., Cheng, X., & Wang, J. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110575 · Cited by 1 · see citing works

Decoding the complexity of tricuspid regurgitation prognostic drivers using explainable artificial intelligence

Tsaban, G., Benfari, G., Essayagh, B. A., Lee, E., Hernandez, M. A. M., Thapa, P. B., Nkomo, V. T., Pislaru, S. V., Michelena, H. I., & Enriquez-Sarano, M. L. (2026) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeag168 · Cited by 1 · see citing works

AI-ECG Detection of Structural Heart Disease in the Community Setting

Poterucha, T. J., Hughes, J. W., Brener, M. I., Chuang, M. L., Hamid, N. B., Snyder, C., Thompson, J. B., Ungarten, J., Takahashi, K., Lindman, B. R., et al. (2026) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2026.06.013 · Cited by 1 · see citing works

The transformative potential of artificial intelligence in pediatric medicine: Current applications, methodological challenges, and future directions

Ruisong, W. A. N. G., Ding, X., Zhang, W., & Tieliu, S. (2026) · Pediatric Investigation · doi:10.1002/ped4.70061 · Cited by 1 · see citing works

Cardiology and Humanism in the Age of Artificial Intelligence: A Challenge to Become More Human

Baratta, S. (2026) · Revista Argentina de Cardiología · doi:10.7775/rac.es.v94.i4.21037 · Cited by 1 · see citing works

Novel technology, novel challenges: How do we compare the performance of artificial intelligence to human experts?

Elias, P. A., Bhave, S., & Poterucha, T. J. (2025) · European Heart Journal · doi:10.1093/eurheartj/ehae760 · Cited by 1 · see citing works

Finding a Signal in the Noise

Deswal, A. M., & Kumar, S. (2024) · JACC Heart Failure · doi:10.1016/j.jchf.2024.07.001 · Cited by 1 · see citing works

Artificial Intelligence-Assisted PCI

Alkhouli, M. A., & Chang, S. (2024) · JACC: Cardiovascular Interventions · doi:10.1016/j.jcin.2024.09.067

Embracing Artificial Intelligence in Cardiovascular Disease Fellowship

Derector, E., & Ricketti, D. A. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101709

Integrating Artificial Intelligence in the Pathology of Major Cardiovascular Diseases: From Atherosclerosis to Heart Failure

(2025) · International Journal of Integrative Cardiology · doi:10.47275/2690-862x-152

Advances in Artificial Intelligence-Assisted Diagnosis (AI) in Ultrasound

Jianglei, X., Xinyu, L., Xuan, Z. Y., Qian, Z., Xue, S., Yanling, Z., & Liu, C. (2025) · International Journal of Biomedical Science and Engineering · doi:10.11648/j.ijbse.20251303.13

Novel Artificial Intelligence Applications in Heart Transplantation

Richter, I., Raikhelkar, J., Clerkin, K. J., DeFilippis, E. M., Estrin, D. L., Lotan, D., ELAD, B., Sayer, G. T., & Uriel, N. (2025) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2025.11.012

Artificial Intelligence in heart and brain ultrasound: the least you need to know

Mozzini, C., Lucchini, G., Bertogna, M., Casadei, A., & Pagani, M. (2025) · Internal and Emergency Medicine · doi:10.1007/s11739-025-04228-3

Diagnostic Accuracy of Machine Learning Algorithms in Electrocardiogram-Based Heart Failure Detection: A Systematic Review and Meta-Analysis

Kılıç, M. E., Arayıcı, M. E., Akbilgiç, O., & Yılmaz, M. B. (2025) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2025.12.022

Comparative performance of large language models on cardiovascular certification simulation exam

Nachnani, E., Goel, K., & Sullivan, A. E. (2026) · American Heart Journal · doi:10.1016/j.ahj.2026.107353

A closed-loop nanoplatform for precision intervention in coronary atherosclerosis: Integrating multimodal omics with intelligent 3D printing

Gao, M., Luo, Y., & Guo, R. (2026) · Nano Research · doi:10.26599/nr.2026.94908440

Charting the Current Landscape and Future Prospects of Cancer Therapy-Related Cardiovascular Toxicity in Cancer Survivors: From Bench to Bedside

Tao, S., Yu, L., Zhang, L., Li, J., Wu, J., & Tan, Y. (2026) · Drug Design Development and Therapy · doi:10.2147/dddt.s572304

Artificial intelligence in early heart failure detection: from algorithms to bedside integration

Zavaleta‐Monestel, E., Arguedas‐Chacón, S., & Speranza, M. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1717911

Harnessing RAG in Heart Failure Risk

Prasad, P., Nikhil, S., R, M. T., Singh, A., & Roopashree, S. (2025) · International Conference on Emerging Technologies in Electronics and Green Energy (ICETEG) · doi:10.1109/iceteg66194.2025.11472844

AI–driven models for early detection and prevention of cardiovascular diseases

Chugh, B., Abbot, V., Chakraborthy, G. S., Tripathi, A. K., Vishwakarma, D. K., Lamba, M., & Lamba, J. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44025-0.00017-6

CardioFusionNet: A Multimodal Deep Learning Framework for Enhanced Coronary Artery Disease Diagnosis

Tao, Z., Zhang, Y., & Pan, S. (2026) · International Conference on Artificial Intelligence, Digital Media Technology and Social Computing (ICAIDS) · doi:10.1145/3806262.3806271

APPLICATION OF ARTIFICIAL INTELLIGENCE IN CARDIOVASCULAR DISEASE DIAGNOSTICS: OPPORTUNITIES AND CHALLENGES

Ciastoń, M., Jurkowska, K., Madrzak, J., Endler, O., Dybicz, M., Filuk, M., Fidelus, J., Dutkiewicz, D., Czerniewska, J., & Handzel, M. (2026) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.2(50).2026.5622

AI-Driven Ultrasound: Automation, Image Interpretation and Decision Support

Sarkar, N., Stancliff, H., & Srinivasareddy, S. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1015391

ПОРІВНЯЛЬНИЙ АНАЛІЗ МЕТРИК ЗВ'ЯЗНОСТІ КОМПОНЕНТІВ МІКРОСЕРВІСНИХ СИСТЕМ

Лахай, В., & Seniv, M. (2026) · doi:10.64828/conf-122-2026-9

РОЛЬ АГРЕСИВНОСТІ У СХИЛЬНОСТІ ДО ПРАВОПОРУШЕНЬ СЕРЕД МОЛОДІ

Гримало, А. (2026) · doi:10.64828/conf-122-2026-2

ЗНАЧЕННЯ MODIC-ЗМІН У ВИБОРІ МЕТОДУ ХІРУРГІЧНОГО ЛІКУВАННЯ ГРИЖ ПОПЕРЕКОВОГО ВІДДІЛУ ХРЕБТА

Слинько, Є., & Chamata, R. (2026) · doi:10.64828/conf-122-2026-4

КЛІНІКО-РЕНТГЕНОЛОГІЧНЕ ОБҐРУНТУВАННЯ ЗАСТОСУВАННЯ КЕЙДЖІВ ПРИ ГРИЖАХ ПОПЕРЕКОВОГО ВІДДІЛУ ХРЕБТА

Слинько, Є., & Chamata, R. (2026) · doi:10.64828/conf-122-2026-3

РОЛЬ ТЕОРЕТИЧНИХ, У ТОМУ ЧИСЛІ КВАНТОВО-ХІМІЧНИХ, ДОСЛІДЖЕНЬ В РОЗРОБЦІ ЕФЕКТИВНИХ СОРБЦІЙНО-АНАЛІТИЧНИХ СИСТЕМ ДЛЯ ВИЗНАЧЕННЯ КВЕРЦЕТИНУ

Бондаренко, В. В., Khudyakova, S. N., & Кондратюк, Н. В. (2026) · doi:10.64828/conf-122-2026-6

МЕХАНІЗМ АВТОНОМНОЇ ПОСАДКИ ДРОНА НА ГІЛКУ ЯК ПРИКЛАД БІОНІЧНОГО ІНЖЕНЕРНОГО РІШЕННЯ

Івасина, О., Михайлюк, В., Солодуха, Є., & ФОМЕНКО, А. (2026) · doi:10.64828/conf-122-2026-1

АФІКСАЛЬНИЙ СПОСІБ ТВОРЕННЯ ОКАЗІОНАЛІЗМІВ У ТЕКСТАХ РЕКЛАМИ ДЛЯ ДІТЕЙ

Кравченко, Л., & Лавринчук, Ю. (2026) · doi:10.64828/conf-122-2026-10

НОВИЙ ХЕМОСЕНСОР ДЛЯ ЧУТЛИВОГО ТА СЕЛЕКТИВНОГО ВИЗНАЧЕННЯ ПАЛАДІЮ ТА СРІБЛА У ЇХ СУМІШАХ

Khudyakova, S. N., Smagin, I., & Кондратюк, Н. В. (2026) · doi:10.64828/conf-122-2026-8

ПАМ’ЯТІ БОРИСА НАУМОВА

Батрун, І. (2026) · doi:10.64828/conf-122-2026-7

ПІСЛЯОПЕРАЦІЙНА ДИНАМІКА БОЛЬОВОГО СИНДРОМУ ПІСЛЯ ДЕКОМПРЕСИВНИХ ТА СТАБІЛІЗУЮЧИХ ВТРУЧАНЬ

Слинько, Є., & Chamata, R. (2026) · doi:10.64828/conf-122-2026-5

Prognostic value of AI-enabled quantitative coronary CT angiography for major adverse cardiovascular events: A systematic review and meta-analysis

Malik, M., Batista, P., de Lucena, L. A., Montenegro, M. V., Silva, R. R. A. D. S., Bacca, C. D. O. F., Giorgi, J. M., & Strom, J. B. (2026) · International Journal of Cardiology Cardiovascular Risk and Prevention · doi:10.1016/j.ijcrp.2026.200660

Achieving Equity in Generative Artificial Intelligence in Cardiovascular Medicine

Kulkarni, A., & Visweswaran, S. (2026) · JACC Advances · doi:10.1016/j.jacadv.2026.102967

Multidimensional cardiac functional phenotyping beyond ejection fraction: a multimodality imaging framework for cardiovascular care

Li, Y., & Liu, Y. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1868144

Artificial intelligence, preventive cardiology and making time for what matters: a fellow’s voice

Lopes, R. A. (2026) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2026.101754

Special article—EchoPeer: a standardized framework for assessing echocardiography reports in the era of artificial intelligence: a recommendation from the Korean Society of Echocardiography AI and Future Strategy Committee

Bae, S., Moon, I., Park, J., Lim, J., Lee, S., Yoon, Y. E., & Cho, G. (2026) · Journal of Cardiovascular Imaging · doi:10.1186/s44348-026-00085-6

ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS, TREATMENT AND PROGNOSIS OF CARDIOVASCULAR DISEASES

Soboleva, M. A., Kargaev, O. G., Chekurishvili, M. A., Tedeeva, M. V., Gopantsov, I. V., Zakaryan, N. G., Babayan, A. A., Tsogoeva, D. A., Tsagaraev, A. B., Yuzbasheva, V. E., et al. (2026) · Complex Issues of Cardiovascular Diseases · doi:10.17802/2306-1278-2026-15-4-225-241

Large language models as integrative intelligence for multimodal cardiovascular decision support

Mitrovic, P. (2026) · Journal of Clinical and Translational Research · doi:10.36922/jctr026290071

Islamic Neuro-Pedagogical Approach: Integration of Neuroscience in Strengthening Students' Spiritual Values

Sidqi, M. H., Qushwa, F. G., & Sain, Z. H. (2026) · Indonesian Journal of Islamic Religious Education · doi:10.63243/injire.v4i1.06

Artificial Intelligence in Cardiovascular Medicine: Implications for Mortality, Longevity, and Insurance Risk Selection

Pósán, E. (2026) · Journal of Insurance Medicine · doi:10.17849/insm-53-3-1-7.1a

Artificial Intelligence in Intensive Care: From Clinical Applications to Ethical and Medico-Legal Challenges

Lazzaro, F. D., Barletta, G., Magnanimi, E., Cirillo, B., Frati, P., Marinelli, E., Pugliese, F. R., Paola, L. D., & Giordano, G. G. (2026) · Healthcare · doi:10.3390/healthcare14203382

Artificial intelligence for vascular minimally invasive surgery: techniques, clinical integration, and challenges

Jiang, C., Bortnovskii, B., Gul, I., Özkan, M., Zhang, L., Yu, D., Pandey, V., Wang, R., Wu, J., & Qin, P. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1966224

Pharmacist-Led Medication Titration for Cardiometabolic Disease: Evidence, Equity, and Scale

Yarlagadda, S., & Blood, A. J. (2026) · Current Cardiology Reports · doi:10.1007/s11886-026-02432-x

Angina with Non-obstructive Coronary Arteries: Diagnostic Evaluation and Endotype-guided Management

Reddy, T. K., Sejo, C. S., Garber, R., Kassab, J. T., Gangavelli, A., Donisan, T., Vasilakis, G. M., & Raphael, C. E. (2026) · US Cardiology Review · doi:10.15420/usc.2026.02

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Artificial intelligence in cardiovascular practice

Farquhar-Snow, M., Simone, A. E., Singh, S. V., & Bushardt, R. L. (2025) · JAAPA · doi:10.1097/01.jaa.0000000000000204 · Cited by 4 · see citing works

Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?

Singhal, A., Bowyer-Howell, T., Sabharwal, N. K., Lewis, A. C. W., Mitchell, A. R. J., Rider, O. J., & Henry, J. A. (2026) · Life · doi:10.3390/life16030507 · Cited by 1 · see citing works