Artificial intelligence is moving from promise to practice in medicine. Where it genuinely helps, where it does not yet, and what clinicians should expect next.
Category: AI in Medicines
Artificial intelligence has moved from conference keynotes into the working hospital, and cardiology has felt it earlier than most specialties. Automated ECG interpretation, echocardiographic strain analysis, coronary CT plaque quantification and risk models built on registry data are all in clinical use somewhere today. Some of it is genuinely useful. A good deal of it is a regression model with better marketing.This section covers what artificial intelligence is actually doing in medical practice, written for clinicians who will be asked to sign the report the algorithm produced. Articles look at where the evidence is strong, where a tool has been validated only on the population that trained it, and what a doctor remains accountable for when a machine proposes the diagnosis.For the clinical fundamentals these tools are trying to automate, start with ECG interpretation and echocardiography. The practical question for most clinicians is not whether artificial intelligence will change medicine but which of the current claims are already supported by evidence a regulator would accept. Articles in this category try to keep that distinction visible: what has been through prospective validation, what rests on retrospective performance in a single dataset, and what is still a demonstration. Coverage leans towards the areas where cardiology has moved first — ECG interpretation, echocardiographic measurement, imaging triage and risk prediction — and towards the governance questions that follow, including who carries responsibility for a decision a model contributed to.