ArticleFrontiers in cardiovascular medicine2022
Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed, 36 citations in OpenAlex.
- Challenges and Recommendations in Regulating AI Medical Devices in the European Union: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Wearable Devices and Machine Learning in Cardiovascular Monitoring: Current Evidence and Future Directions for Precision Medicine.Journal of personalized medicine · 2026Review
- Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority.NPJ digital medicine · 2026Review
- Artificial Intelligence in Cardiovascular Imaging: From Automated Acquisition to Precision Diagnostics and Clinical Decision Support.Medical sciences (Basel, Switzerland) · 2026Review
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.Diagnostics (Basel, Switzerland) · 2026Review
- A Multidimensional Index Framework for Evaluating the Regulatory Applicability of ISO 10328 Using a Prosthetic Knee Case Study.Medical devices (Auckland, N.Z.) · 2026Review
- Artificial intelligence in medical imaging diagnosis: are we ready for its clinical implementation?Journal of medical imaging (Bellingham, Wash.) · 2025Article
- A scoping review of the governance of federated learning in healthcare.NPJ digital medicine · 2025Article
- Exploring the Role of Artificial Intelligence in Smart Healthcare: A Capability and Function-Oriented Review.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Cardiology: General Perspectives and Focus on Interventional Cardiology.Anatolian journal of cardiology · 2025Review
- Early-fusion hybrid CNN-transformer models for multiclass ovarian tumor ultrasound classification.Frontiers in artificial intelligence · 2025Article
- Review
- Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices.Journal of personalized medicine · 2024Article
- The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.Journal of personalized medicine · 2024Review
- Ethics and Legal Framework for Trustworthy Artificial Intelligence in Vascular Surgery.EJVES vascular forum · 2023Article
- Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations.Frontiers in cardiovascular medicine · 2023Article
- Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions.Discoveries (Craiova, Romania)Review
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 at 1 institution in 1 country.
Funding
Abstract
Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by "learning" medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.
Indexed as
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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.