In hospital settings, where patients are already frail, the sudden onset of a heart attack can have even more serious consequences. In this context, the use of algorithms and artificial intelligence (AI) has gained prominence as an innovative tool for the early prediction of cardiovascular events, especially in hospitalised patients.

Traditionally, heart attack risk assessment is based on clinical parameters such as age, medical history, electrocardiogram (ECG), laboratory tests, and vital signs. While these tools are effective, they cannot always accurately identify patients at the highest risk of developing an acute event in the coming hours or days.

This is where AI comes in: with its ability to analyse large volumes of data quickly and efficiently, it can identify subtle patterns and relationships not readily apparent to human knowledge.

In recent years, several studies have demonstrated the potential of AI to predict myocardial infarction based on data from hospitalised patients. Machine learning-based systems, for example, can "learn" from thousands of clinical cases, training algorithms to recognise early signs of deteriorating cardiac status. These signs can include subtle changes in troponin levels, variations in heart rhythm, or even breathing patterns recorded by hospital monitors.

One of AI's greatest advantages is its ability to seamlessly integrate with electronic healthcare systems (electronic medical record systems), enabling constant monitoring of hospitalised patients.

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An algorithm can analyse each patient's clinical information in real-time and issue automatic alerts to medical staff if it detects an increased risk of a heart attack. This allows for faster interventions and potentially saves lives. Furthermore, more advanced techniques, such as artificial neural networks and deep learning, have been used to interpret imaging tests such as echocardiograms and angiograms with greater accuracy, offering more precise diagnoses and guiding clinical decisions more safely.

However, despite these promising advances, significant challenges remain. One is the need to ensure that algorithms are trained with data from diverse populations to avoid bias and ensure equity in care. Another critical point is the ethical and safe integration of these technologies into daily hospital care, respecting patient privacy and the autonomy of healthcare professionals.

In short, artificial intelligence represents a silent revolution in hospital medicine, especially in cardiology. Its ability to predict myocardial infarction before it occurs can transform the way we care for hospitalised patients, offering smarter, more personalised, and proactive surveillance. With the continued advancement of technology and the responsible integration of these tools into healthcare systems, we are ever closer to a future where prevention will be as precise as treatment.

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