Background: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI–enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology–AF) in a population-based sample. Methods: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. Results: A total of 1936 participants with median age 75.8 (interquartile range, 70.4–81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2–14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51–2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58–2.28]) independently predicted future AF without significant interaction ( P =0.54). C statistics were 0.69 (95% CI, 0.66–0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66–0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69–0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.

In its own (paraphrased) words, ChatGPT is ‘an artificial intelligence (AI) chatbot created by OpenAI using a neural network architecture called generative pre-trained transformer to understand natural language and generate humanlike responses to a wide range of questions and prompts; its purpose is to provide users with conversational AI that can assist them with various tasks, provide information, or just chat for fun’. Since its release in November 2022, ChatGPT has become one of the fastest growing consumer-facing applications in history and is disrupting the dissemination of knowledge through its ease of use, massive computational power, and ability to rationally synthesize large amounts of data available on the internet. In healthcare, ChatGPT may be used as a rapid resource for medical information about simple and complex concepts that may take the place of conventional searches of online bibliographical sources executed by the user. However, ChatGPT has not been trained specifically for healthcare or medical research. The accuracy of ChatGPT in delivering accurate medical information is unknown.

Current guidelines suggest that asymptomatic atrial fibrillation (AF) is independently associated with increased risks of stroke and mortality compared with symptomatic AF. Considering that recent investigations have provided conflicting results, the present study aimed to evaluate the association between symptom status and clinical outcomes in patients with AF. Medline, Cochrane Library, and Scopus were searched until 25 March 2024. Triple-independent study selection, data extraction and quality assessment were performed. Evidence was pooled using random-effects meta-analyses. Thirty-six studies (217 850 participants) were included. Based on the frequentist analysis, symptomatic individuals had no significant difference in the risk of all-cause mortality [hazard ratio (HR) .97, 95% confidence interval (CI) .80–1.17], cardiovascular mortality (HR 1.04, 95% CI .72–1.49), thromboembolism (HR 1.06, 95% CI .87–1.28), stroke (HR 1.06, 95% CI .84–1.34), hospitalization (HR 1.34, 95% CI .89–2.02), and myocardial infarction (HR .98, 95% CI .70–1.36), compared to the asymptomatic group. Symptomatic patients had a 33% increased risk of new-onset heart failure (HR 1.33, 95% CI 1.19–1.49) and a 30% lower risk of progression to permanent AF (HR .70, 95% CI .54–.89). The Bayesian analysis yielded comparable results, yet the association between symptom status and new-onset heart failure was not significant (HR 1.27, 95% credible interval .76–1.93; Bayes factor = 1.2). Symptomatic patients had higher odds of receiving antiarrhythmic drugs (odds ratio [OR] 1.64, 95% CI 1.33–2.03) and ablation therapy (OR 1.47, 95% CI 1.06–2.05) compared to asymptomatic cases. The risk of major clinical outcomes did not differ between individuals with and without AF-related symptoms. Asymptomatic patients had a greater hazard of progression to permanent AF. Major clinical outcomes in patients with symptomatic vs. asymptomatic atrial fibrillation. AF, atrial fibrillation; CI, confidence interval; CrI, credible interval; HR, hazard ratio; OR, odds ratio. * A two-tailed P-value <.05 was considered statistically significant

Recently, deep learning artificial intelligence (AI) models have been trained to detect cardiovascular conditions, including hypertrophic cardiomyopathy (HCM), from the 12-lead electrocardiogram (ECG). In this external validation study, we sought to assess the performance of an AI-ECG algorithm for detecting HCM in diverse international cohorts. A convolutional neural network-based AI-ECG algorithm was developed previously in a single-centre North American HCM cohort (Mayo Clinic). This algorithm was applied to the raw 12-lead ECG data of patients with HCM and non-HCM controls from three external cohorts (Bern, Switzerland; Oxford, UK; and Seoul, South Korea). The algorithm’s ability to distinguish HCM vs. non-HCM status from the ECG alone was examined. A total of 773 patients with HCM and 3867 non-HCM controls were included across three sites in the merged external validation cohort. The HCM study sample comprised 54.6% East Asian, 43.2% White, and 2.2% Black patients. Median AI-ECG probabilities of HCM were 85% for patients with HCM and 0.3% for controls (P < 0.001). Overall, the AI-ECG algorithm had an area under the receiver operating characteristic curve (AUC) of 0.922 [95% confidence interval (CI) 0.910–0.934], with diagnostic accuracy 86.9%, sensitivity 82.8%, and specificity 87.7% for HCM detection. In age- and sex-matched analysis (case–control ratio 1:2), the AUC was 0.921 (95% CI 0.909–0.934) with accuracy 88.5%, sensitivity 82.8%, and specificity 90.4%. The AI-ECG algorithm determined HCM status from the 12-lead ECG with high accuracy in diverse international cohorts, providing evidence for external validity. The value of this algorithm in improving HCM detection in clinical practice and screening settings requires prospective evaluation.