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Artificial intelligence in atrial fibrillation – Timely diagnosis, risk assessment and personalized management
Kushal Chatterjee, Aaryamaan Verma, Erick Godinez, Daniel Joseph Gonzalez, Rahul Devathu, Mahmood I. Alhusseini, Muhammad Fazal, Tina Baykaner. Indian Pacing and Electrophysiology Journal, 2026-01-27
Topic: Amyloid, tau and blood-test diagnosis
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity and mortality, including stroke, systemic embolism, heart failure, and dementia. Timely diagnosis, accurate risk stratification, and personalized management are necessary to improving outcomes. Recent advancements in artificial intelligence (AI) have expanded the potential for AF care, leveraging machine and deep learning approaches for enhanced detection, risk assessment, and therapeutic guidance. In this review, we summarize the clinical integration of AI into AF management across three domains. First, AI-enhanced electrocardiography (ECG) and wearable photoplethysmography devices allow early detection and long-term, non-invasive screening of AF, including identification of subclinical or paroxysmal AF from routine sinus rhythm recordings. Second, AI models have the potential to refine stroke risk stratification and personalize anticoagulation decision-making by integrating multidimensional clinical data, providing individualized risk assessments beyond traditional scoring systems like CHA 2 DS 2 -VASc. Finally, AI has been increasingly integrated into procedural planning and execution for AF ablation, helping to identify optimal ablation targets and predict post-procedural arrhythmia recurrence risk for a given rhythm control strategy, based on imaging and biosignal-derived features. In summary, the emerging integration of machine learning approaches into AF management highlights its transformative potential to offer earlier detection, more precise and personalized risk stratification, and tailored therapeutic strategies and patient follow up. Despite these advancements, the clinical implementation of AI in AF management remains primitive, requiring large-scale validation, supplemental clinical oversight, and regulatory guidance to ensure safe and effective integration into our daily practices.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Chatterjee, K., Verma, A., Godinez, E., Gonzalez, D. J., Devathu, R., Alhusseini, M. I., et al. (2026). Artificial intelligence in atrial fibrillation – Timely diagnosis, risk assessment and personalized management. Indian Pacing and Electrophysiology Journal. https://doi.org/10.1016/j.ipej.2026.01.011
Vancouver
Chatterjee K, Verma A, Godinez E, Gonzalez DJ, Devathu R, Alhusseini MI, et al. Artificial intelligence in atrial fibrillation – Timely diagnosis, risk assessment and personalized management. Indian Pacing and Electrophysiology Journal. 2026. doi:10.1016/j.ipej.2026.01.011. PMID: 41611206.
BibTeX
@article{chatterjee2026-41611206,
author = {Chatterjee, Kushal and Verma, Aaryamaan and Godinez, Erick and Gonzalez, Daniel Joseph and Devathu, Rahul and Alhusseini, Mahmood I. and Fazal, Muhammad and Baykaner, Tina},
title = {{Artificial intelligence in atrial fibrillation – Timely diagnosis, risk assessment and personalized management}},
journal = {Indian Pacing and Electrophysiology Journal},
year = {2026},
doi = {10.1016/j.ipej.2026.01.011},
pmid = {41611206},
url = {https://doi.org/10.1016/j.ipej.2026.01.011}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity and mortality, including stroke, systemic embolism, heart failure, and dementia.
Why it matters
This review describes how AI could help detect atrial fibrillation earlier and guide its treatment, and it notes that atrial fibrillation is linked to dementia among other conditions. It does not test whether treating atrial fibrillation with AI-guided care prevents or cures Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 38/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Indian Pacing and Electrophysiology Journal) |
| Journal's citation record | 0 / 4 | Indian Pacing and Electrophysiology Journal: journal h-index 25 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | 3 / 6 | Cited 4.8× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Tina Baykaner: h-index 25 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Stanford University: institution h-index 2,684 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 41611206) |
| Free to read | 2 / 2 | Free to read |
| Total | 38 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.ipej.2026.01.011
- PubMed: 41611206
- Original source →
- Free full text: Indian Pacing and Electrophysiology Journal →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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