ReviewCardio-oncology (London, England)2025
Bridging gaps in AI-driven cardio-oncology: global advances and Middle East perspectives.
Review in Cardio-oncology (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- From Cardioprotection to Trial Design: Rethinking Cardiac Safety in Oncology.Current heart failure reports · 2026Review
- Precision Cardio-Oncology and Nuclear Imaging: Current Applications, Molecular Innovations, and Future Trajectories.Cancers · 2026Review
- Artificial intelligence in cardio-oncology: decoding mechanisms, predicting toxicity, and personalizing cancer therapy.Frontiers in cardiovascular medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiotoxicity remains a critical barrier to effective cancer treatment, contributing significantly to morbidity and mortality in cancer survivors. Recent advancements (2020-2025) highlight artificial intelligence (AI) as a transformative tool in cardio-oncology, enhancing cardiotoxicity detection, personalized risk assessment, and patient management. Despite rapid technological progress, significant gaps remain in AI integration across different healthcare settings globally.
methodsThis narrative review systematically synthesizes literature published between 2020 and 2025, evaluating key applications of AI in cardio-oncology, including imaging modalities, predictive modeling, wearable technology, and clinical decision-support systems. Comparative analyses between high-income countries (HICs) and low-to-middle-income countries (LMICs), with an emphasis on the Middle East, were performed to illustrate global disparities and unique regional challenges.
resultsAI-enabled imaging technologies, particularly echocardiography and cardiac MRI, significantly improved the early detection and management of cardiotoxicity. Predictive algorithms integrating multimodal data demonstrated superior risk stratification accuracy over traditional methods. Wearable technologies combined with AI enabled real-time cardiac monitoring, demonstrating feasibility in diverse resource settings. Nonetheless, adoption barriers such as dataset biases, inadequate regulatory frameworks, prohibitive costs, ethical dilemmas, and limited digital infrastructure persist, disproportionately affecting LMICs.
conclusionTo harness the full potential of AI in cardio-oncology, strategic investments in collaborative international research, standardized regulatory frameworks, educational initiatives, and infrastructural support are urgently needed. Future research should prioritize equitable, inclusive AI solutions, validated through prospective trials and adapted specifically to underserved populations.
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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.