Description
You will meet these tools whether or not you chose them
Automated ECG interpretation arrives with the machine. Echo software quantifies before you have looked. Increasingly the question is not whether to adopt artificial intelligence but whether to countersign what it has already produced — and that is a clinical decision requiring an informed view of when it is likely to be wrong.
These 181 pages supply that view, without promotion and without dismissal.
Where performance is real
Detection of reduced ejection fraction from a standard ECG, and of atrial fibrillation from a sinus-rhythm tracing — both now beyond human capability. Automated echo quantification, which reduces the inter-observer variability that has always undermined serial comparison. Imaging segmentation. Risk prediction from routine data.
Where it degrades, and why it matters here
Performance falls on populations unlike the training data, and most of these systems were trained on North American and European cohorts. For clinicians practising elsewhere — most of this directory — that is not an academic caveat but the central practical issue, and the book treats it as such. Automated systems also fail silently rather than obviously, and models trained on historical decisions reproduce historical bias, including the under-investigation of women.
Accountability
What you remain responsible for having acted on an output, how to record that reasoning, and how to disagree with a model defensibly.
Suits
Any practising clinician. No technical background assumed.
PDF, lifetime access, from CardiologyBooks.com.
What is actually in clinical use
AI in cardiology is separated here into what is deployed, what is approved but not embedded, and what remains a research claim. ECG interpretation and rhythm detection, echocardiographic measurement and strain, coronary CT plaque quantification, and risk prediction from routinely collected data are each assessed on their validation population and their behaviour outside it.
Responsibility, failure modes and consent
The clinically important material is the failure modes: performance decay when the imaging protocol changes, bias inherited from a training set that did not resemble the local population, automation bias in the reporting clinician, and the unresolved question of who is accountable for a missed finding. Regulation, data governance and what a patient should be told are covered, so a department can adopt AI in cardiology with its obligations understood rather than assumed.
