Key result
Artificial intelligence and machine learning techniques demonstrate significant potential in cardio-oncology for the early prediction, diagnosis, and monitoring of cancer therapy-related cardiotoxicity.
Why the study?
Newer and more efficient data acquisition methods, such as artificial intelligence, are desirable to help assess cardiovascular risk and extract predictive patterns in cancer patients.
Can artificial intelligence improve cardiovascular risk prediction in cancer patients?
Can artificial intelligence improve cardiovascular risk prediction in cancer patients?
Artificial intelligence holds promise for improving cardiovascular risk assessment in cardio-oncology by identifying hidden patterns in large medical datasets.
Supports AI exploration in cardio-oncology but should not change practice yet; leaves open need for prospective validation.
Cardiooncology has been developed as a relatively new branch of medicine that focuses on the prevention and treatment of adverse cardiovascular events associated with cancer therapy. Newer, more efficient data acquisition methods, such as the use of artificial intelligence, are desirable to help assess CVR in cancer patients. By extracting hidden patterns and evidence from large volumes of medical data, artificial intelligence can create new predictors and parameters to predict risks in patients with cardio-oncological diseases.
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Buziashvili et al. (2023) conducted a review in Cardiovascular complications in cancer patients (Cardio-oncology). Artificial Intelligence and Machine Learning was evaluated. Artificial intelligence and machine learning techniques demonstrate significant potential in cardio-oncology for the early prediction, diagnosis, and monitoring of cancer therapy-related cardiotoxicity.
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