ReviewDiagnostics (Basel, Switzerland)2025
AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring.
Review in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cardiotoxicity in pediatric oncology: a systematic review and meta-analysis.Pediatric research · 2025Pooled it
- From Cardioprotection to Trial Design: Rethinking Cardiac Safety in Oncology.Current heart failure reports · 2026Review
- Empagliflozin Protects Against Doxorubicin Cardiotoxicity: Integrative Assessment of Cardiac Kinetics and Electrophysiology Using Machine Learning in a Rat Model.Medical sciences (Basel, Switzerland) · 2026Article
- Cardiotoxicity Induced by Targeted Cancer Therapies: Understanding the Risks and Developing Solutions.Cardiovascular drugs and therapy · 2026Review
- Innovative strategies for early detection of cardiotoxicity: artificial intelligence and multi-modality collaborative models.Journal of thrombosis and thrombolysis · 2026Review
- QT Prolongation and Arrhythmias in Cancer Therapy: A Narrative Review of Mechanistic and Clinical Studies.Cardiovascular toxicology · 2026Review
- From data to decision: a clinical pipeline for wearable AI in early cardiotoxicity detection.Cardio-oncology (London, England) · 2026Review
- Charting the Current Landscape and Future Prospects of Cancer Therapy-Related Cardiovascular Toxicity in Cancer Survivors: From Bench to Bedside.Drug design, development and therapy · 2026Review
- Integrated Anthropometric, Physiological and Biological Assessment of Elite Youth Football Players Using Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Healthcare Management in Cardio-Oncology, Clinical Strategies and Future Perspectives: A Narrative Review.Healthcare (Basel, Switzerland) · 2025Review
- Static Baropodometric Assessment for Musculoskeletal Rehabilitation: Plantar Pressure and Postural Load Distribution in Young Adults.Life (Basel, Switzerland) · 2025Article
- BioInnovate AI: A Machine Learning Platform for Rapid PCR Assay Design in Emerging Infectious Disease Diagnostics.Diagnostics (Basel, Switzerland) · 2025Article
- Risk Factors and Prevention of Cancer and CVDs: A Chicken and Egg Situation.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The increasing prevalence of cardiovascular complications in cancer patients due to cardiotoxic treatments has necessitated advanced monitoring and predictive solutions. Cardio-oncology is an evolving interdisciplinary field that addresses these challenges by integrating artificial intelligence (AI) and smart cardiac devices. This comprehensive review explores the integration of artificial intelligence (AI) and smart cardiac devices in cardio-oncology, highlighting their role in improving cardiovascular risk assessment and the early detection and real-time monitoring of cardiotoxicity. AI-driven techniques, including machine learning (ML) and deep learning (DL), enhance risk stratification, optimize treatment decisions, and support personalized care for oncology patients at cardiovascular risk. Wearable ECG patches, biosensors, and AI-integrated implantable devices enable continuous cardiac surveillance and predictive analytics. While these advancements offer significant potential, challenges such as data standardization, regulatory approvals, and equitable access must be addressed. Further research, clinical validation, and multidisciplinary collaboration are essential to fully integrate AI-driven solutions into cardio-oncology practices and improve patient outcomes.
Indexed as
Identifiers
What Socratic 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 Socratic graph.