Artificial intelligence in cardiology: fundamentals and applications
Bibliographic record
Abstract
Artificial intelligence (AI) is an overarching term that encompasses a set of computational approaches that are trained through generalised learning to autonomously execute specific tasks. AI is a rapidly expanding field in medicine. In particular cardiology, with its high reliance on numerical patient data in decision making, has great potential to benefit from AI. Types of AI, including neural networks and computer vision, can dramatically change the day-to-day workflow of cardiologists, primarily through integration in diagnostic imaging modalities, periprocedural planning, electronic health record analysis and patient monitoring. Healthcare systems will undoubtedly become more automated and shift to more AI-driven methods to improve efficiency and reduce cost. Patients in the end will benefit from these changes with improved diagnostic accuracy, better tailored treatments resulting in a greater quality and quantity of life. In this article, we will describe some of the fundamental principles underlying AI that physicians should have an understanding of, along with current clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".