How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?
Alice PozzaLuca ZanellaBiagio CastaldiGiovanni di SalvoPublished in: Journal of clinical medicine (2024)
Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.
Keyphrases
- artificial intelligence
- congenital heart disease
- machine learning
- healthcare
- palliative care
- big data
- deep learning
- affordable care act
- quality improvement
- end stage renal disease
- newly diagnosed
- ejection fraction
- high resolution
- prognostic factors
- social media
- climate change
- skeletal muscle
- pain management
- peritoneal dialysis
- risk assessment
- smoking cessation
- case report
- pregnant women
- human health
- patient reported outcomes
- preterm birth
- decision making
- chronic pain