Artificial intelligence in interventional cardiology: present landscape and future directions

Q: What is the clinical evidence from this study?

Study design: Review. Population: Cardiovascular diseases requiring interventional cardiology procedures (e.g., coronary artery disease, structural heart disease). Intervention: Artificial intelligence (machine learning, deep learning, computer vision, natural language processing, reinforcement learning) vs. Traditional interventional cardiology practice.

Q: What does this research mean for the field?

Artificial intelligence demonstrates high utility and accuracy across interventional cardiology for preprocedural planning, intraprocedural navigation, and postprocedural prognosis, though clinical adoption remains constrained by data silos, lack of interpretability, and regulatory barriers. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

Jiayuan Li; Yufei Li; Qifeng Zhu; Xianbao Liu

doi:10.20517/2574-1209.2026.12

October 22, 2026Vessel PlusOpen Access

Artificial intelligence in interventional cardiology: present landscape and future directions

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Key result

Artificial intelligence demonstrates high accuracy across procedural planning, intraprocedural guidance, and postprocedural prognosis.

Why the study?

Interventional cardiology faces persistent challenges including stringent precision requirements, heavy reliance on operator experience, and variability in diagnostic and therapeutic efficiency. Artificial intelligence offers a pathway to address these challenges, yet faces barriers related to data quality, algorithm interpretability, clinical translation, and regulatory constraints.

Key Points

To review the current clinical applications, core technologies, technical limitations, and future directions of artificial intelligence in interventional cardiology.
Narrative review of artificial intelligence technologies, including machine learning, deep learning, and computer vision, applied to catheter-based cardiac interventions.
Synthesized evidence across preprocedural risk assessment and planning, intraprocedural real-time navigation and early complication detection, and postprocedural follow-up.
Artificial intelligence models support risk stratification, lesion characterization, real-time procedural guidance, and postprocedural prognosis in coronary and structural heart disease.
Key clinical translation challenges include fragmented data silos, selection bias, limited high-quality labeled datasets, lack of algorithmic interpretability, and regulatory barriers.
Future developments focus on integrating artificial intelligence directly with interventional devices, multimodal data modeling, and personalized interventional strategies.

Structured PICO

P

Population

Patients undergoing interventional cardiology procedures for conditions such as coronary and structural heart disease (e.g., percutaneous coronary intervention [PCI], transcatheter aortic valve replacement [TAVR], transcatheter mitral/tricuspid interventions)

I

Intervention

Artificial intelligence (AI) technologies, including machine learning, deep learning, and computer vision, applied across preprocedural risk assessment and planning, intraprocedural real-time navigation and complication warning, and postprocedural follow-up and prognosis prediction

This narrative review synthesizes the current applications, technical and regulatory challenges, and future directions of artificial intelligence across preprocedural, intraprocedural, and postprocedural workflows in interventional cardiology.

Limitations

Fragmented clinical data silos and limited integration of multimodal data
Selection bias in training datasets and limited generalizability to primary medical institutions and diverse populations
Scarcity of high-quality labeled data and interobserver variability in manual annotation
Inconsistent AI performance in complex coronary lesions (such as chronic total occlusions and severe calcification) and risk of misclassification
Limited interpretability of 'black-box' algorithms
Lack of harmonized industry standards, variable clinician acceptance, and high deployment and maintenance costs
Evolving regulatory frameworks, patient data privacy and security risks, and unclear allocation of medical liability
Data-related issues including fragmented data silos, selection bias in training datasets from tertiary hospitals, and scarce high-quality labeled data with interobserver variability
Technical limitations such as inconsistent performance in complex coronary lesions (e.g., chronic total occlusion, severely calcified lesions), risks of misclassification, and limited interpretability of 'black-box' algorithms
Clinical application barriers including lack of unified industry standards, variable clinician acceptance, and high deployment and maintenance costs
Regulatory and ethical risks involving unharmonized regulatory frameworks, patient data security and privacy concerns, and unclear attribution of medical liability

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Answers are grounded in this paper and cite their sources.

Implication

AI may enhance interventional workflows; leaves open prospective validation before clinical adoption.

Abstract

With population aging and the rising incidence of cardiac disease, interventional cardiology, as a cornerstone of treatment for conditions such as coronary and structural heart disease, faces persistent challenges, including stringent precision requirements, heavy reliance on operator experience, and variability in diagnostic and therapeutic efficiency. With robust capabilities in data mining, image recognition, real-time analysis, and predictive modeling, artificial intelligence (AI) offers a novel pathway to address the aforementioned challenges and support the intelligent transformation of interventional cardiology. This narrative review briefly outlines the background to the application of AI in interventional cardiology, focuses on core supporting technologies including deep learning, machine learning, and computer vision, and summarizes current research and clinical applications of AI across preprocedural risk assessment and planning, intraprocedural real-time navigation and early warning of complications, and postprocedural follow-up and prognosis prediction. It also analyzes the principal challenges currently facing AI in this field, such as data quality and security issues, insufficient algorithm interpretability, barriers to clinical translation, and constraints from ethical and regulatory frameworks. Finally, in light of technological advances and clinical needs, this review outlines future directions, including the integration of AI with interventional devices, multimodal data integration and modeling, and the development of personalized interventional strategies, thereby providing a reference for promoting the standardized and large-scale application of AI technology in interventional cardiology.

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Cite This Study

Li et al. (2026) conducted a review in Cardiovascular diseases requiring interventional cardiology procedures (e.g., coronary artery disease, structural heart disease). Artificial intelligence (machine learning, deep learning, computer vision, natural language processing, reinforcement learning) vs. Traditional interventional cardiology practice was evaluated. Artificial intelligence demonstrates high accuracy across interventional cardiology for preprocedural CT-FFR and plaque characterization, intraprocedural guidance, and postprocedural prognosis.

synapsesocial.com/papers/6aee6bb371a7594806220a7a https://doi.org/10.20517/2574-1209.2026.12

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Artificial intelligence in interventional cardiology: present landscape and future directions

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Artificial intelligence in interventional cardiology: present landscape and future directions

Key Points

Structured PICO

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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