ReviewCureus2023
The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases.
Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Enhancing Case Formulation Competence in Novice Counselors: A ChatGPT-Assisted Approach Using the 4P Model.Behavioral sciences (Basel, Switzerland) · 2026Article
- Linking shape and clinical outcomes: Statistical shape model of Bidirectional Glenn connection in congenital single ventricle patients.PLOS digital health · 2026Article
- Revolutionizing Multimorbidity Care: A Narrative Review on Artificial Intelligence Applications.Health science reports · 2026Article
- Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.Journal of clinical ultrasound : JCU · 2025Review
- Intimate partner violence and stress-related disorders: from epigenomics to resilience.Frontiers in global women's health · 2025Review
- The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency.Health science reports · 2025Article
- Innovation and challenges of artificial intelligence technology in personalized healthcare.Scientific reports · 2024Review
- Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.Journal of medical systems · 2024Article
- How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?Journal of clinical medicine · 2024Review
- Second harvest of Congenital Heart Surgery Database in Türkiye: Current outcomes.Turk gogus kalp damar cerrahisi dergisi · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.Risk management and healthcare policy · 2024Review
- Innovative strategies to predict and prevent the risk for malnutrition in child, adolescent, and young adult cancer survivors.Frontiers in nutrition · 2023Review
- Leveraging Machine Learning Models to Explore Disparities in Prostate Cancer Diagnosis, Treatment, and Survival.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 9 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.