Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging
Bibliographic record
Abstract
Artificial intelligence (AI) has made significant strides in cardiac imaging, offering advancements in image acquisition, risk prediction, and workflow automation. However, its readiness for widespread clinical adoption remains debated. This review explores both sides of the argument across key domains. It discusses the advantages and challenges of AI for cardiac imaging regarding pre-and post-processing, risk-stratification and prognostication, workflow augmentation, regulatory and ethical frameworks, and cost-effectiveness of AI tools. It will discuss the diagnostic accuracy shown by AI for automated measurements, improved image quality and workflow efficiency with AI-driven worklist prioritization. The potential of personalized care using AI-based prognostic models. It discusses regulatory frameworks for approving AI tools, while ethical frameworks to ensure safe and ethical use of AI are being implemented, simultaneously reimbursement is becoming available, signalling growing trust in their safety and efficacy. It also addresses the challenges AI has yet to overcome, such as the lack of generalizability across diverse populations, limited availability of outcome data and cost-efficacy studies. Despite progress, regulatory and ethical frameworks still struggle to keep pace with AI's rapid evolution, raising concerns about accountability, patient safety, bias, data privacy, and algorithmic transparency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".