Loading
Life sciences · Journal article
European Heart Journal – Cardio-oncology · October 7, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Abstract Rapid advances in oncology care have resulted in a growing population of cancer survivors, many of whom suffer from short- and long-term cancer therapy related cardiovascular toxicities (CTR-CVT). Cardio-oncology aims to diagnose and manage these cardiovascular conditions, but part of the challenge pertains to finding such individuals among the millions of cancer survivors. Medical informatics may help identify patients eligible to receive cardio-oncology care, assess the risk of cardiovascular complications, and predict toxicity by employing artificial intelligence (AI) algorithms. Uncoordinated development of the medical informatics solutions for cardio-oncology has produced disparate algorithms aiming to predict CTR-CVT by analysing features of patient records in the context of clinical guidelines developed by organizations such as the American Heart Association, the American College of Cardiology, and the European Society of Cardiology. These algorithm development efforts emphasize narrowly-defined clinical goals, rather than integrating an entire array of the applicable data elements to provide medical professionals with comprehensive analyses resembling care delivery in community settings and translatable into clinical workflows. The resulting mismatch between solutions developed by informaticists and the needs of practicing clinicians has led to difficulty with external validation and practical application of these informatics solutions. Additionally, the latest AI and medical device developments, such as digital twins and wearables, enable new solutions to support clinical decision making and patient education in cancer survivorship care. We aim to describe, summarize, and evaluate these developments in a cohesive vision and call to action for medical informaticists to develop relevant translatable solutions for cardio-oncology.