Manish Wadhwa, MD, is a cardiologist and electrophysiologist with San Diego Arrhythmia Associates and the chief executive officer of SummaCor.

Manish Wadhwa, MD

Artificial intelligence (AI) is a burgeoning industry, evolving in practically every direction as it continues to develop. AI-based algorithms are being implemented in countless fields, medicine not least among them. 

 in particular has seen a wide range of AI innovations in the past 5 years alone, from screening and analyzing patient characteristics to streamlining clinician workflows.

An Uncharted Path: How Clinicians are Implementing Artificial Intelligence Across Cardiology

In this feature article, the editorial team at 

 sat down with Manish Wadhwa, MD, a cardiologist and electrophysiologist with San Diego Arrhythmia Associates, Charalambos Antoniades, MD, PhD, director of the Acute Multidisciplinary Imaging & Interventional Centre and BHF chair of cardiovascular medicine at the University of Oxford, and Sadiya Khan, MD, MSc, the Magerstadt Professor of Cardiovascular Epidemiology at Northwestern University Feinberg, to discuss how AI is already implemented into cardiovascular care – as well as the debates over equity and access that naturally spring up around these devices.

Existing Integration and Future Directions

 from May 2026 highlighted the extent to which AI is already integrated into cardiovascular care, analyzing the impact these algorithms have had on real-world patient outcomes. Examining categories such as workflow efficiency, patient engagement, and clinical outcomes, the study found that the use of AI algorithms, more specifically machine learning and deep learning, substantially increased all 3 metrics.

Several clinical trials have also been published, and many more are ongoing, investigating the place AI may take in cardiovascular care. The ORFAN-MAESTRIA trial, for example, shone a spotlight on a particular algorithm’s efficacy in identifying indicators and measuring risk of cardioembolic 

. Using standard computed tomography (CT) scans, this program is capable of fully automated phenotyping of the peri-left atrial (LA) space, detecting LA myopathy before symptoms appear.

AI has also found a home in implantable devices, such as the ILR ECG Analyzer from Implicity, which received approval from the 

 in August 2026. This algorithm serves as a secondary filter for alerts generated by implantable cardiac monitors (ICMs), helping clinicians and patients alike by filtering out false positive alerts. Not only can this reduce the number of alerts flooding a given clinician’s inbox, but it also serves to help optimize the way clinicians spend their time, allowing them to aid more patients in need.

However, for all the benefits AI can provide to clinicians and patients, it remains a thorny issue among the public. Questions regarding data privacy and equitable access have sprung up across a variety of medical specialties, exacerbated by the fact that AI algorithms are in many ways subservient to the data on which they are trained. Any biases present in these data, including disparities in race or gender among patients, could remain as the algorithm is moved into day-to-day use.

Additionally, medical AI models often work with an immense amount of patient data, including imaging, genetic information, and patient history. This could make these data more vulnerable to breaches or unauthorized access. Many clinicians have discussed the need for stricter cybersecurity measures and guidelines before these programs are made capable of handling this sensitive information.

Despite these concerns, however, AI has already proven itself beneficial in myriad ways for cardiology clinics and clinicians. Be it through simplifying workflows, providing an extra layer of confirmation for alerts or indicators of disease, or simply categorizing patient data more efficiently, these algorithms have begun to integrate themselves in many aspects of practice. With the speed at which the technology is evolving, it is difficult to call AI integration into cardiology, and medicine writ large, anything but inevitable.

Editors’ Note: Antoniades reports disclosures with AstraZeneca, Caristo Diagnostics, Lexicon, Novo Nordisk, Sanofi, Silence Therapeutics, and others. Khan reports no relevant disclosures. Wadhwa reports his position as a clinical advisor for Implicity.

Lin YE, Yang SM, Huang CJ, et al. Impact of artificial intelligence on cardiovascular workflow, engagement, and outcomes: a systematic review. 

https://doi.org/10.1038/s41746-026-02690-7

Livingston R. ORFAN-MAESTRIA: AI Predicts Cardioembolic Stroke, Atrial Fibrillation from CT Scans. 

. September 6, 2026. Accessed September 23, 2026. 

https://www.hcplive.com/view/orfan-maestria-ai-predicts-cardioembolic-stroke-atrial-fibrillation-from-ct-scans

Livingston R. FDA-Cleared, AI-Assisted ECG Analyzer Algorithm Reduces False Positives in ICMs. 

. August 21, 2026. Accessed September 23, 2026. 

https://www.hcplive.com/view/fda-cleared-ai-assisted-ecg-analyzer-algorithm-reduces-false-positives-in-icms

Livingston R. Equitable and Ethical AI Use in Cardiology: Addressing Biases and Privacy Concerns. 

. September 11, 2026. Accessed September 23, 2026. 

https://www.hcplive.com/view/equitable-and-ethical-ai-use-in-cardiology-addressing-biases-and-privacy-concerns

Videos

As AI continues to evolve at a breakneck pace, catch up on some of the ways it’s already been implemented in cardiovascular care.

 granted 510(k) clearance to the ILR ECG Analyzer, which serves as a second layer for residual alerts generated by implantable cardiac monitors (ICMs).

FDA-Cleared, AI-Assisted ECG Analyzer Algorithm Reduces False Positives in ICMs

ICMs are well-known for their tendency to produce false positive alerts, detecting 

 that are not present or clinically significant. Even with the implementation of AI algorithms to filter these alerts from genuine arrhythmias, the substantial burden posed by false positives still lingers. Past research has shown that many devices register benign rhythms or certain signal artifacts, such as electrical noise or premature ventricular contractions, which are frequently labeled as significant events.

To combat this, Implicity’s ILR ECG Analyzer was developed to act as a backup filter for alerts that bypass initial manufacturer filters. The algorithm can analyze data from all major device manufacturers. Recent research has shown that the algorithm reduced false positives by ≤76% while maintaining high sensitivity for detecting clinically meaningful events.

“Essentially, you keep looking at something – if it’s real, that’s great, but you get so much information that’s not real that it creates a major workflow challenge,” Manish Wadhwa, MD, a cardiologist and electrophysiologist with San Diego Arrhythmia Associates and chief executive officer of SummaCor, told 

 in an exclusive interview. “That’s why something like this, this AI-based analyzer, is so important: it helps to mitigate the workflow challenges.”

, investigators conducted a multicenter, multivendor database analysis of ICMs to compare the accuracy of arrhythmia detection between 4 commonly used ICMs – the LINQ II from Medtronic (MDT), the LUX-Dx from Boston Scientific (BSX), the Biomonitor III from Biotronik (BIO), and the Confirm Rx from Abbott (ABT). A total of 437,351 electrocardiogram-verified episodes were collected from 6756 patients across 25 centers – investigators selected a random sample of 1140 patients based on a prospectively determined power analysis.

The study cohort generated 25,826 total alerts, with a median of 5 alerts (interquartile range [IQR], 2-14) per patient. ICM implant indications included cryptogenic stroke (21%), suspected atrial fibrillation (11.3%), atrial fibrillation management (23.6%), syncope (23.2%), palpitations (14.9%), ventricular tachycardia (2.1%), and unknown (3.8%).

Atrial tachycardia and atrial fibrillation were the most frequent alerts (BIO, 70.8%; MDT, 68%; ABT, 55.7%; BSX, 35.7%). After adjudication, BSX displayed the highest positive predictive values, followed by ABT and MDT, while BIO had the lowest (BSX, 0.73; MDT, 0.56; ABT, 0.51; BIO, 0.23). False positives for atrial tachycardia and atrial fibrillation were attributed to ectopy (75.8%), combined ectopy with oversensing or noise (5.3%), isolated oversensing (5.2%), sinus arrhythmias (3.9%), indeterminate reason (3.7%), noise (3.6%), and bradycardia (2.5%).

BSX demonstrated the highest positive prediction values (0.96), while MDT had the lowest (0.36) – this was primarily attributed to undersensing. ABT had the lowest pause detection accuracy (positive prediction values: 0.01; 79.8% due to undersensing) and BSX had the highest (positive prediction values: 0.7). Additionally, tachycardia alerts had moderate positive prediction values for BIO (0.62) and BSX (0.61), with substantially lower values for MDT (0.29) and ABT (0.27).

“There is one other benefit to this as well, and that is standardization. If you took a single strip and you asked 20 different practitioners to interpret that strip, sadly, you’re probably going to get about 10 to 15 different answers,” Wadhwa said. “To that end, if there is a ground truth, what you actually want is for the standardized output to be the truth. You can keep asking people and you’ll keep coming back with different answers, but the AI is very sure, and if we train the models properly, it will always come back the same way.”

Editors’ Note: Wadhwa reports his position as a clinical advisor for Implicity.

Implicity. Implicity’s Next-Generation ILR ECG Analyzer Earns FDA Clearance. 

. July 28, 2026. Accessed August 21, 2026. 

https://www.globenewswire.com/news-release/2026/07/28/3334149/0/en/implicity-s-next-generation-ilr-ecg-analyzer-earns-fda-clearance.html

Implicity. EHRA 2026 Studies Reveal Why False Positives Persist in AI-Equipped Implantable Cardiac Monitors. April 14, 2026. Accessed August 21, 2026. 

https://implicity.com/ehra-2026-studies-reveal-why-false-positives-persist-in-ai-equipped-implantable-cardiac-monitors/

Kamsani SH, Middeldorp ME, Evans S, et al. Accuracy of implantable loop recorders. JACC: Clinical Electrophysiology. 2026;12(5):1113-1127. 

Manish Wadhwa, MD, discusses Implicity’s newest algorithm to improve the accuracy of implantable cardiac monitors in identifying arrhythmic episodes.

Manish Wadhwa, MD

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