ArticleThe British journal of cardiology2024
Heartificial intelligence: in what ways will artificial intelligence lead to changes in cardiology over the next 10 years.
Article in The British journal of cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Ethical Challenges and Current Opportunities of Artificial Intelligence in Cardiology.Avicenna journal of medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) will revolutionise cardiology practices over the next decade, from optimising diagnostics to individualising treatment strategies. Moreover, it can play an important role in combating gender inequalities in cardiovascular disease outcomes. There is growing evidence that AI algorithms can match humans at echocardiography analysis, while also being able to extract subtle differences that the human eye cannot detect. Similar promise is evident in the analysis of electrocardiograms, creating a new layer of interpretation. From big data, AI can produce algorithms that individualise cardiac risk factors and prevent perpetuating gender biases in diagnosis. Nonetheless, AI implementation requires caution. To avoid worsening health inequalities, it must be trained across diverse populations, and when errors arise, a robust regulatory framework must be in place to ensure safety and accountability. AI is perfectly positioned to capitalise on the growth of big data, but to proceed we require a generation of physicians who understand its fundamentals.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.