Artificial intelligence: revolutionizing cardiology with large language models

Artificial intelligence in cardiovascular practice

ABSTRACT

Artificial intelligence (AI) is everywhere, but how is this expansive technology being used in cardiovascular care? This article explores common AI models, how they are transforming healthcare delivery, and important roles for clinicians, including advanced practice providers, in the development, adoption, evaluation, and ethical use of AI in cardiovascular care.

Artificial intelligence in cardiovascular practice

ABSTRACT

Artificial intelligence (AI) is everywhere, but how is this expansive technology being used in cardiovascular care? This article explores common AI models, how they are transforming healthcare delivery, and important roles for clinicians, including advanced practice providers, in the development, adoption, evaluation, and ethical use of AI in cardiovascular care.

Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association

Artificial intelligence is poised to transform cardio-oncology by enabling personalized care for patients with cancer, who are at a heightened risk of cardiovascular disease due to both the disease and its treatments. The rising prevalence of cancer and the availability of multiple new therapeutic options has resulted in improved survival among patients with cancer and has expanded the scope of cardio-oncology to not only short-term but also long-term cardiovascular risks resulting from both cancer and its treatments. However, there is considerable heterogeneity in cardiovascular risk, driven by the nature of the malignancy as well as each individual's unique characteristics. The use of novel therapies, such as targeted therapies and immune checkpoint inhibitors, across multiple cancer groups has also broadened the populations among which cardiotoxicity has become an important consideration of therapy. Therefore, the ability to understand and personalize cardiovascular risk management in patients with cancer is a key target for artificial intelligence, which can deduce and respond to complex patterns within the data. These advances necessitate an overview of established biomarkers of risk, spanning advanced imaging, diagnostic testing, and multi-omics, the evidence supporting their use, and the proven and proposed role of artificial intelligence in refining this risk to attain greater precision in risk prediction and management in cardio-oncologic care.

Estimating inflammatory risk in atherosclerotic cardiovascular disease: plaque over plasma?

Inflammation is an important driver of disease in the context of atherosclerosis, and several landmark trials have shown that targeting inflammatory pathways can reduce cardiovascular event rates. However, the high cost and potentially serious adverse effects of anti-inflammatory therapies necessitate more precise patient selection. Traditional biomarkers of inflammation, such as high-sensitivity C-reactive protein, show an association with cardiovascular risk on a population level but do not have specificity for local plaque inflammation. Nowadays, advancements in non-invasive imaging of the vasculature enable direct assessment of vascular inflammation. Positron emission tomography (PET) tracers such as 18F-fluorodeoxyglucose enable detection of metabolic activity of inflammatory cells but are limited by low specificity and myocardial spillover effects. 18F-sodium fluoride is a tracer that identifies active micro-calcification in plaques, indicating vulnerable plaques. Gallium-68 DOTATATE targets pro-inflammatory macrophages by binding to somatostatin receptors, which enhances specificity for plaque inflammation. Coronary computed tomography angiography (CCTA) provides high-resolution images of coronary arteries, identifying high-risk plaque features. Measuring pericoronary adipose tissue attenuation on CCTA represents a novel marker of vascular inflammation. This review examines both established and emerging methods for assessing atherosclerosis-related inflammation, emphasizing the role of advanced imaging in refining risk stratification and guiding personalized therapies. Integrating these imaging modalities with measurements of systemic and molecular biomarkers could shift atherosclerotic cardiovascular disease management towards a more personalized approach.

Embracing Generative Artificial Intelligence in Clinical Research and Beyond: Opportunities, Challenges, and Solutions

To explore threats and opportunities and to chart a path for safely navigating the rapid changes that generative artificial intelligence (AI) will bring to clinical research, the Duke Clinical Research Institute convened a multidisciplinary think tank in January 2024. Leading experts from academia, industry, nonprofits, and government agencies highlighted the potential opportunities of generative AI in automation of documentation, strengthening of participant and community engagement, and improvement of trial accuracy and efficiency. Challenges include technical hurdles, ethical dilemmas, and regulatory uncertainties. Success is expected to require establishing rigorous data management and security protocols, fostering integrity and trust among stakeholders, and sharing information about the safety and effectiveness of AI applications. Meeting insights point towards a future where, through collaboration and transparency, generative AI will help to shorten the translational pipeline and increase the inclusivity and equitability of clinical research.

Fine-Tuning Large Language Models for Specialized Use Cases

Large language models (LLMs) are a type of artificial intelligence, which operate by predicting and assembling sequences of words that are statistically likely to follow from a given text input. With this basic ability, LLMs are able to answer complex questions and follow extremely complex instructions. Products created using LLMs such as ChatGPT by OpenAI and Claude by Anthropic have created a huge amount of traction and user engagements and revolutionized the way we interact with technology, bringing a new dimension to human-computer interaction. Fine-tuning is a process in which a pretrained model, such as an LLM, is further trained on a custom data set to adapt it for specialized tasks or domains. In this review, we outline some of the major methodologic approaches and techniques that can be used to fine-tune LLMs for specialized use cases and enumerate the general steps required for carrying out LLM fine-tuning. We then illustrate a few of these methodologic approaches by describing several specific use cases of fine-tuning LLMs across medical subspecialties. Finally, we close with a consideration of some of the benefits and limitations associated with fine-tuning LLMs for specialized use cases, with an emphasis on specific concerns in the field of medicine.

Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology

OBJECTIVE

Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.

METHODS

We focused on events related to cardiac catheterization as defined by the NCDR-IMPACT registry. These events were extracted from the Necker Children's Hospital data warehouse. Electronic health records were pre-screened using regular expressions. The resulting datasets contained numerous false positives sentences that were annotated by a cardiologist using an active learning process. A deep learning text classifier was then trained on this active learning-annotated dataset to accurately identify patients who have suffered a serious adverse event.

RESULTS

The dataset included 2,980 patients. Regular expression based extraction of adverse events related to cardiac catheterization achieved a perfect recall. Due to the rarity of adverse events, the dataset obtained from this initial pre-screening step was imbalanced, containing a significant number of false positives. The active learning annotation enabled the acquisition of a representative dataset suitable for training a deep learning model. The deep learning text-classifier identified patients who underwent adverse events after cardiac catheterization with a recall of 0.78 and a specificity of 0.94.

CONCLUSION

Our model effectively identified patients who experienced adverse events related to cardiac catheterization using real clinical data. Enabled by an active learning annotation process, it shows promise for large language model applications in clinical research, especially for rare diseases with limited annotated databases. Our model's strength lies in its development by physicians for physicians, ensuring its relevance and applicability in clinical practice.

The Danish Lymphoid Cancer Research (DALY-CARE) Data Resource: The Basis for Developing Data-Driven Hematology

Background

Lymphoid-lineage cancers (LC; International Classification of Diseases, 10th edition [ICD10] C81.x-C90.x, C91.1-C91.9, C95.1, C95.7, C95.9, D47.2, D47.9B, and E85.8A) share many epidemiological and clinical features, which favor meta-learning when developing medical artificial intelligence (mAI). However, access to large, shared datasets is largely missing and limits mAI research.

Aim

Creating a large-scale data repository for patients with LC to develop data-driven hematology.

Methods

We gathered electronic health data and created open-source processing pipelines to create a comprehensive data resource for Danish LC Research (DALY-CARE) approved for epidemiological, molecular, and data-driven research.

Results

We included all Danish adults registered with LC diagnoses since 2002 (n=65,774) and combined 10 nationwide registers, electronic health records (EHR), and laboratory data on a high-powered cloud-computer to develop a secure research environment. Among other, data include treatments (ie 21,750 cytoreductive treatment plans, 21.3M outpatient prescriptions, and 12.7M in-hospital administrations), biochemical analyses (77.3M), comorbidity (14.8M ICD10 codes), pathology codes (4.5M), treatment procedures (8.3M), surgical procedures (1.0M), radiological examinations (3.3M), vital signs (18.3M values), and survival data. We herein describe the data infrastructure and exemplify how DALY-CARE has been used for molecular studies, real-world evidence to evaluate the efficacy of care, and mAI deployed directly into EHR systems.

Conclusion

The DALY-CARE data resource allows for the development of near real-time decision-support tools and extrapolation of clinical trial results to clinical practice, thereby improving care for patients with LC while facilitating streamlining of health data infrastructure across cohorts and medical specialties.

Cardiovascular Risk Assessment and Prevention in Cardio-Oncology: Beyond Traditional Risk Factors

This review goes beyond traditional approaches in cardio-oncology, highlighting often-neglected factors impacting patient care. Social determinants, environment, health care access, and gut microbiome significantly influence patient outcomes. Powerful tools like multi-omics and wearable technologies offer deeper insights into real-world experiences. The future lies in integrating these advancements with established practices to achieve precision cardio-oncology care. By crafting tailored therapies and continuously updating comprehensive management plans based on real-time data, we can unlock the full potential of personalized care for all patients.

Artificial Intelligence Advancements in Cardiomyopathies: Implications for Diagnosis and Management of Arrhythmogenic Cardiomyopathy

PURPOSE OF REVIEW

This review aims to explore the emerging potential of artificial intelligence (AI) in refining risk prediction, clinical diagnosis, and treatment stratification for cardiomyopathies, with a specific emphasis on arrhythmogenic cardiomyopathy (ACM).

RECENT FINDINGS

Recent developments highlight the capacity of AI to construct sophisticated models that accurately distinguish affected from non-affected cardiomyopathy patients. These AI-driven approaches not only offer precision in risk prediction and diagnostics but also enable early identification of individuals at high risk of developing cardiomyopathy, even before symptoms occur. These models have the potential to utilise diverse clinical input datasets such as electrocardiogram recordings, cardiac imaging, and other multi-modal genetic and omics datasets. Despite their current underrepresentation in literature, ACM diagnosis and risk prediction are expected to greatly benefit from AI computational capabilities, as has been the case for other cardiomyopathies. As AI-based models improve, larger and more complicated datasets can be combined. These more complex integrated datasets with larger sample sizes will contribute to further pathophysiological insights, better disease recognition, risk prediction, and improved patient outcomes.

Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores

BACKGROUND AND AIMS

Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical models and AF polygenic score (PGS).

METHODS

Electrocardiograms in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with pre-existing AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping data sets: 70% for training, 10% for validation, and 20% for testing. The performance of ECG-AI, clinical models, and PGS was assessed in the test data set. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital data set.

RESULTS

A total of 669 782 ECGs from 145 323 patients were included. Mean age was 61 ± 15 years, and 58% were male. The primary outcome was observed in 15% of patients, and the ECG-AI model showed an area under the receiver operating characteristic (AUC-ROC) curve of .78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval: 4.02-4.57). In a subgroup analysis of 2301 patients, ECG-AI outperformed CHARGE-AF (AUC-ROC = .62) and PGS (AUC-ROC = .59). Adding PGS and CHARGE-AF to ECG-AI improved goodness of fit (likelihood ratio test P < .001), with minimal changes to the AUC-ROC (.76-.77). In the external validation cohort (mean age 59 ± 18 years, 47% male, median follow-up 1.1 year), ECG-AI model performance remained consistent (AUC-ROC = .77).

CONCLUSIONS

ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS.

Artificial intelligence in cardiology: a peek at the future and the role of ChatGPT in cardiology practice

Artificial intelligence has increasingly become an integral part of our daily activities. ChatGPT, a natural language processing technology developed by OpenAI, is widely used in various industries, including healthcare. The application of ChatGPT in healthcare is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. ChatGPT could be exploited in the medical field to improve patient education and information, thus increasing compliance. ChatGPT could facilitate information exchange on major cardiovascular diseases, provide clinical decision support, and improve patient communication and education. It could assist the clinician in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, it is unclear whether it will be possible to use ChatGPT for the management of patients who require rapid decisions. Indeed, many drawbacks are associated with the daily use of these technologies in the medical field, such as insufficient expertise in specialized fields and a lack of comprehension of the context in which it works. The pros and cons of its use have been explored in this review, which was not written with the help of ChatGPT.

Exploring the role of Large Language Models in haematology: A focused review of applications, benefits and limitations

Large language models (LLMs) have significantly impacted various fields with their ability to understand and generate human-like text. This study explores the potential benefits and limitations of integrating LLMs, such as ChatGPT, into haematology practices. Utilizing systematic review methodologies, we analysed studies published after 1 December 2022, from databases like PubMed, Web of Science and Scopus, and assessing each for bias with the QUADAS-2 tool. We reviewed 10 studies that applied LLMs in various haematology contexts. These models demonstrated proficiency in specific tasks, such as achieving 76% diagnostic accuracy for haemoglobinopathies. However, the research highlighted inconsistencies in performance and reference accuracy, indicating variability in reliability across different uses. Additionally, the limited scope of these studies and constraints on datasets could potentially limit the generalizability of our findings. The findings suggest that, while LLMs provide notable advantages in enhancing diagnostic processes and educational resources within haematology, their integration into clinical practice requires careful consideration. Before implementing them in haematology, rigorous testing and specific adaptation are essential. This involves validating their accuracy and reliability across different scenarios. Given the field's complexity, it is also critical to continuously monitor these models and adapt them responsively.

Revolutionizing Cardiology With Words: Unveiling the Impact of Large Language Models in Medical Science Writing

Large language models (LLMs) are a unique form of machine learning that facilitates inputs of unstructured text/numerical information for meaningful interpretation and prediction. Recently, LLMs have become commercialized, allowing the average person to access these incredibly powerful tools. Early adopters focused on LLM use in performing logical tasks, including-but not limited to-generating titles, identifying key words, summarizing text, initial editing of scientific work, improving statistical protocols, and performing statistical analysis. More recently, LLMs have been expanded to clinical practice and academia to perform higher cognitive and creative tasks. LLMs provide personalized assistance in learning, facilitate the management of electronic medical records, and offer valuable insights into clinical decision making in cardiology. They enhance patient education by explaining intricate medical conditions in lay terms, have a vast library of knowledge to help clinicians expedite administrative tasks, provide useful feedback regarding content of scientific writing, and assist in the peer-review process. Despite their impressive capabilities, LLMs are not without limitations. They are susceptible to generating incorrect or plagiarized content, face challenges in handling tasks without detailed prompts, and lack originality. These limitations underscore the importance of human oversight in using LLMs in medical science and clinical practice. As LLMs continue to evolve, addressing these challenges will be crucial in maximizing their potential benefits while mitigating risks. This review explores the functions, opportunities, and constraints of LLMs, with a focus on their impact on cardiology, illustrating both the transformative power and the boundaries of current technology in medicine.

The potential for large language models to transform cardiovascular medicine

Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early diagnosis and prediction of occurrence of these diseases. Deep neural networks can improve the accuracy of medical image interpretation and their outputs can provide rich information that otherwise would not be detected by cardiologists. With recent advances in transformer models, multimodal AI, and large language models, the ability to integrate electronic health record data with images, genomics, biosensors, and other data has the potential to improve diagnosis and partition patients who are at high risk for primary preventive strategies. Although much emphasis has been placed on AI supporting clinicians, AI can also serve patients and provide immediate help with diagnosis, such as that of arrhythmia, and is being studied for automated self-imaging. Potential risks, such as loss of data privacy or potential diagnostic errors, should be addressed before use in clinical practice. This Series paper explores opportunities and limitations of AI models for cardiovascular medicine, and aims to identify specific barriers to and solutions in the application of AI models, facilitating their integration into health-care systems.

Practical Aspects of Using Large Language Models to Screen Abstracts for Cardiovascular Drug Development: Cross-Sectional Study

Unlabelled

Cardiovascular drug development requires synthesizing relevant literature about indications, mechanisms, biomarkers, and outcomes. This short study investigates the performance, cost, and prompt engineering trade-offs of 3 large language models accelerating the literature screening process for cardiovascular drug development applications.

Assessment of ChatGPT's Compliance with ESC-Acute Coronary Syndrome Management Guidelines at 30-Day Intervals

Background: Despite ongoing advancements in healthcare, acute coronary syndromes (ACS) remain a leading cause of morbidity and mortality. The 2023 European Society of Cardiology (ESC) guidelines have introduced significant improvements in ACS management. Concurrently, artificial intelligence (AI), particularly models like ChatGPT, is showing promise in supporting clinical decision-making and education. Methods: This study evaluates the performance of ChatGPT-v4 in adhering to ESC guidelines for ACS management over a 30-day interval. Based on ESC guidelines, a dataset of 100 questions was used to assess ChatGPT's accuracy and consistency. The questions were divided into binary (true/false) and multiple-choice formats. The AI's responses were initially evaluated and then re-evaluated after 30 days, using accuracy and consistency as primary metrics. Results: ChatGPT's accuracy in answering ACS-related binary and multiple-choice questions was evaluated at baseline and after 30 days. For binary questions, accuracy was 84% initially and 86% after 30 days, with no significant change (p = 0.564). Cohen's Kappa was 0.94, indicating excellent agreement. Multiple-choice question accuracy was 80% initially, improving to 84% after 30 days, also without significant change (p = 0.527). Cohen's Kappa was 0.93, reflecting similarly high consistency. These results suggest stable AI performance with minor fluctuations. Conclusions: Despite variations in performance on binary and multiple-choice questions, ChatGPT shows significant promise as a clinical support tool in ACS management. However, it is crucial to consider limitations such as fluctuations and hallucinations, which could lead to severe issues in clinical applications.

Cardiovascular care with digital twin technology in the era of generative artificial intelligence

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.

Artificial intelligence-enhanced patient evaluation: bridging art and science

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Artificial Intelligence and Health Inequities in Dietary Interventions on Atherosclerosis: A Narrative Review

Poor diet is the top modifiable mortality risk factor globally, accounting for 11 million deaths annually with half being from diet-linked atherosclerotic cardiovascular disease (ASCVD). Yet, most of the world cannot afford a healthy diet-as the hidden costs of the inadequate global food system total over USD 13 trillion annually-let alone the much more clinically, financially, and ecologically costly and resource-intensive medical interventions required to address the disease progression and acute complications of ASCVD. Yet, AI is increasingly understood as a force multiplying revolutionary technology which may catalyze multi-sector efforts in medicine and public health to better address these significant health challenges. This novel narrative review seeks to provide the first known overview of the state-of-the-art in clinical interventions and public health policies in healthy diets for ASCVD, accelerated by health equity-focused AI. It is written from the first-hand practitioner perspective to provide greater relevance and applicability for health professionals and data scientists. The review summarizes the emerging trends and leading use cases in population health risk stratification and precision public health, AI democratizing clinical diagnosis, digital twins in precision nutrition, and AI-enabled culinary medicine as medical education and treatment. This review may, therefore, help inform and advance the evidence-based foundation for more clinically effective, financially efficient, and societally equitable dietary and nutrition interventions for ASCVD.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

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

PURPOSE OF REVIEW

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.

RECENT FINDINGS

AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.

Machine learning in cardiac surgery: a narrative review

Background and Objective

Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the clinical utility of ML in this domain is unclear. The aim of this review is to provide an overview of ML in cardiac surgery, particularly with regards to its utility in predictive analytics and implications for use in clinical decision support.

Methods

We performed a narrative review of relevant articles indexed in PubMed since 2000 using the MeSH terms "Machine Learning", "Supervised Machine Learning", "Deep Learning", or "Artificial Intelligence" and "Cardiovascular Surgery" or "Thoracic Surgery".

Key Content and Findings

ML methods have been widely used to generate pre-operative risk profiles, consistently resulting in the accurate prediction of clinical outcomes in cardiac surgery. However, improvement in predictive performance over traditional risk metrics has proven modest and current applications in the clinical setting remain limited.

Conclusions

Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.

How AI drives innovation in cardiovascular medicine

Medicine is entering a new era in which artificial intelligence (AI) and deep learning have a measurable impact on patient care. This impact is especially evident in cardiovascular medicine. While the purpose of this short opinion paper is not to provide an in-depth review of the many applications of AI in cardiovascular medicine, we summarize some of the important advances that have taken place in this domain.

Beyond standard echocardiography: Strain imaging as the AI-powered key to comprehensive cardiac function evaluation

Application of left ventricular and left atrial strain to distinguish cardiac from non-cardiac dyspnea.