Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T10:51:35.166775Z
Paper Citation Record · LEDGER
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images
As of 9 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.20087.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
27 of 27 outbound references displayed
- verified exact4
- verified fuzzy0
- unresolved22
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation b185087d-89c4-423f-b171-aebf6fc7566d · outbound
This paper cites 2025 Heart Disease and Stroke Statistics: A Report of US and Global Data from the American Heart Association,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images 2025 Heart Disease and Stroke Statistics: A Report of US and Global Data from the American Heart Association,
Reference 1
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation b523c2f5-a3f9-46b2-9ce3-8e5f2dcbbd50 · outbound
This paper cites European Society of Cardiology: the 2023 Atlas of Cardiovascular Disease Statistics,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images European Society of Cardiology: the 2023 Atlas of Cardiovascular Disease Statistics,
Reference 2
- Resolution
- verified exact
- doi, observed 2026-08-01T10:54:16.993891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9cb45060-831e-4929-8db3-20fb78c6d392 · outbound
This paper cites The Role of Cardiovascular Magnetic Resonance Imaging in Heart Failure,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images The Role of Cardiovascular Magnetic Resonance Imaging in Heart Failure,
Reference 3
- Resolution
- verified exact
- doi, observed 2026-08-01T10:54:16.738187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a52d24c1-1898-4da4-97a1-f52a51daf368 · outbound
This paper cites On the Opportunities and Risks of Foundation Models.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images On the Opportunities and Risks of Foundation Models
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation 13f9b0ea-71d7-484b-8b91-87fc48fd3c79 · outbound
This paper cites Foundation Models in Radiology: What, How, Why, and Why Not,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Foundation Models in Radiology: What, How, Why, and Why Not,
Reference 5
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c47344aa-6b38-431f-9026-72fc1b6add35 · outbound
This paper cites Large language models in medicine,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Large language models in medicine,
Reference 6
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6ababbd6-7403-4080-834d-07caa7b3e849 · outbound
This paper cites The future landscape of large language models in medicine,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images The future landscape of large language models in medicine,
Reference 7
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45df6cb7-d3da-48b8-9ea2-cdac635ea35e · outbound
This paper cites The application of large language models in medicine: A scoping review,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images The application of large language models in medicine: A scoping review,
Reference 8
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20035675-7982-488a-92d5-cf47068926ac · outbound
This paper cites Segment Anything,.
Reference 9
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 10bf0eeb-95f6-419e-a3a5-ee265fe719e2 · outbound
This paper cites Segment anything in medical images,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Segment anything in medical images,
Reference 10
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 41d665be-4620-436e-80a0-6004c9a95921 · outbound
This paper cites Emerging Properties in Self -Supervised Vision Transformers,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Emerging Properties in Self -Supervised Vision Transformers,
Reference 11
- Resolution
- unresolved
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Source-reported events for the cited work
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Observation a610ddb8-48da-4657-9583-1992503fee1a · outbound
This paper cites DINOv2: Learning Robust Visual Features without Supervision,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images DINOv2: Learning Robust Visual Features without Supervision,
Reference 12
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 807bdcca-25a2-480e-95da-34aadbb3b05c · outbound
This paper cites Towards a CMR Foundation Model for Multi-Task Cardiac Image Analysis,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Towards a CMR Foundation Model for Multi-Task Cardiac Image Analysis,
Reference 13
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bc9d378b-e85a-435a-9211-5a7b4287ae67 · outbound
This paper cites Bridging the Gap in Cardiac MRI AI Implementations: From Ambitious Goals to Real -World Progress using Foundation Models,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Bridging the Gap in Cardiac MRI AI Implementations: From Ambitious Goals to Real -World Progress using Foundation Models,
Reference 14
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8b09edd-5f66-4282-ba91-67cfa3d98037 · outbound
This paper cites Screening and diagnosis of cardiovascular disease using artificial intelligence- enabled cardiac magnetic resonance imaging,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Screening and diagnosis of cardiovascular disease using artificial intelligence- enabled cardiac magnetic resonance imaging,
Reference 15
- Resolution
- verified exact
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation cdf550b2-e993-4705-9358-92c8d8d1e96d · outbound
This paper cites A versatile foundation model for cine cardiac magnetic resonance image analysis tasks.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images A versatile foundation model for cine cardiac magnetic resonance image analysis tasks
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c1b911e-3ae7-4ee3-b97a-4939a2d59499 · outbound
This paper cites Comparative analysis of privacy-preserving open-source LLMs regarding extraction of diagnostic information from clinical CMR imaging reports.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Comparative analysis of privacy-preserving open-source LLMs regarding extraction of diagnostic information from clinical CMR imaging reports
Reference 17
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91784046-654f-4e9e-a21e-0c174ccd5d7d · outbound
This paper cites On the usability of synthetic data for improving the robustness of deep learning -based segmentation of cardiac magnetic resonance images,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images On the usability of synthetic data for improving the robustness of deep learning -based segmentation of cardiac magnetic resonance images,
Reference 18
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a7281747-31b5-49d5-ad03-2be42fabce6b · outbound
This paper cites nnU -Net: a self - configuring method for deep learning-based biomedical image segmentation,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images nnU -Net: a self - configuring method for deep learning-based biomedical image segmentation,
Reference 19
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- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 091841a5-4f6a-4a83-a58d-901130ebf414 · outbound
This paper cites Overcoming data scarcity in biomedical imaging with a foundational multi - task model,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Overcoming data scarcity in biomedical imaging with a foundational multi - task model,
Reference 20
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 84ceb46b-544e-4906-af86-5d70be7eac36 · outbound
This paper cites TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch -based sampling of medical images in deep learning,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch -based sampling of medical images in deep learning,
Reference 21
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd5bb28a-71bc-4708-97c0-d24f415c0d80 · outbound
This paper cites MONAI: An open-source framework for deep learning in healthcare.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images MONAI: An open-source framework for deep learning in healthcare
Reference 22
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a96f7d06-e6be-4b7a-95c0-3c48887b06a6 · outbound
This paper cites FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare,
Reference 23
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a63c54f3-b4f5-41c4-a3ec-d2dbbd2fd455 · outbound
This paper cites Grad -CAM: Visual Explanations from Deep Networks via Gradient -based Localization,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Grad -CAM: Visual Explanations from Deep Networks via Gradient -based Localization,
Reference 24
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6fe44c85-3f22-46d6-b562-1e28c1c6c7a4 · outbound
This paper cites Deep Learning Techniques for Automatic MRI Cardiac Multi -Structures Segmentation and Diagnosis: Is the Problem Solved?,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Deep Learning Techniques for Automatic MRI Cardiac Multi -Structures Segmentation and Diagnosis: Is the Problem Solved?,
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T10:51:35.158367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bca653b1-c336-4891-b2bd-57a1783196fa · outbound
This paper cites Multi-Centre, Multi-Vendor and Multi -Disease Cardiac Segmentation: The MMs Challenge,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Multi-Centre, Multi-Vendor and Multi -Disease Cardiac Segmentation: The MMs Challenge,
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T10:51:35.162533Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation abc63e40-4097-4d7e-ac91-9f9b896a4900 · outbound
This paper cites Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge,.
Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge,
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T10:51:35.166775Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.