Paper Citation Record · LEDGER

An autonomous agent for auditing and improving the reliability of clinical AI models

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2507.05755.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.05755 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:23:52.190497Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

verified exact2
verified fuzzy14
unresolved23
parse uncertain0
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External citation measurements

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Outbound references

Observation c11cd078-bdb7-44a9-943a-316c1229d22d · outbound

This paper cites Nature Medicine 25, 30–36 (2019) https://doi.org/10.1038/ s41591-018-0307-0.

An autonomous agent for auditing and improving the reliability of clinical AI models Nature Medicine 25, 30–36 (2019) https://doi.org/10.1038/ s41591-018-0307-0

Reference 1

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Observation 8a49764a-908b-4f76-8062-573b0b7dd8fe · outbound

This paper cites Lancet Digital Health 1(4), 192–200 (2019) https://doi.org/10.1016/S2589-7500(19)30100-0.

An autonomous agent for auditing and improving the reliability of clinical AI models Lancet Digital Health 1(4), 192–200 (2019) https://doi.org/10.1016/S2589-7500(19)30100-0

Reference 2

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Observation dca9f417-e38b-4cb3-bee6-7010bfc7ef1c · outbound

This paper cites In: Advances in Neural Information Processing Systems, vol.

An autonomous agent for auditing and improving the reliability of clinical AI models In: Advances in Neural Information Processing Systems, vol

Reference 3

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Observation 5abd1b13-44ef-4263-bbe7-383f620d8153 · outbound

This paper cites https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206.

An autonomous agent for auditing and improving the reliability of clinical AI models https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206

Reference 4

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Observation 1585fcec-a146-4141-a229-53c3d94eb0ef · outbound

This paper cites Food and Drug Administration: Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan.

An autonomous agent for auditing and improving the reliability of clinical AI models Food and Drug Administration: Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:58.178146Z

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Observation 49fae7ac-e783-4bb9-9a31-544a5a68a058 · outbound

This paper cites Nature 542(7639), 115–118 (2017) https: //doi.org/10.1038/nature21056.

An autonomous agent for auditing and improving the reliability of clinical AI models Nature 542(7639), 115–118 (2017) https: //doi.org/10.1038/nature21056

Reference 6

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Observation 6cb2a52a-4ba0-4e0f-9e2b-7d85e711c043 · outbound

This paper cites Science Advances 8(32), 6147 (2022) https://doi.org/10.1126/ sciadv.abq6147.

An autonomous agent for auditing and improving the reliability of clinical AI models Science Advances 8(32), 6147 (2022) https://doi.org/10.1126/ sciadv.abq6147

Reference 7

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Observation af67e46d-07fd-4bc5-92bf-0aaed10dacc3 · outbound

This paper cites PLOS Medicine 15(11), 1002683 (2018) https://doi.org/10.1371/journal.pmed.1002683.

An autonomous agent for auditing and improving the reliability of clinical AI models PLOS Medicine 15(11), 1002683 (2018) https://doi.org/10.1371/journal.pmed.1002683

Reference 8

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Observation 3a8de8e8-63f4-43b1-bb55-c6ca17edcd78 · outbound

This paper cites Scientific Reports 13, 16791 (2023) https://doi.org/ 10.1038/s41598-023-43715-5.

An autonomous agent for auditing and improving the reliability of clinical AI models Scientific Reports 13, 16791 (2023) https://doi.org/ 10.1038/s41598-023-43715-5

Reference 9

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Observation 06eb3754-6a62-454c-9741-a91433625aee · outbound

This paper cites JAMA318(22), 2199–2210 (2017) https://doi.org/10.1001/jama.2017.14585.

An autonomous agent for auditing and improving the reliability of clinical AI models JAMA318(22), 2199–2210 (2017) https://doi.org/10.1001/jama.2017.14585

Reference 10

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Observation f1cae808-7879-4162-89e4-2bc87db91b1a · outbound

This paper cites Computerized Medical Imaging and Graphics 73, 60–71 (2019) https://doi.org/10.

An autonomous agent for auditing and improving the reliability of clinical AI models Computerized Medical Imaging and Graphics 73, 60–71 (2019) https://doi.org/10

Reference 11

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Observation 79af8932-901a-49bc-8d90-eb485d3a3650 · outbound

This paper cites Nature Methods 21(2), 195–212 (2024) https://doi.org/10.1038/s41592-023-02151-z.

An autonomous agent for auditing and improving the reliability of clinical AI models Nature Methods 21(2), 195–212 (2024) https://doi.org/10.1038/s41592-023-02151-z

Reference 12

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Observation 80fd2123-a509-48e7-8bba-5f8d785f5bb0 · outbound

This paper cites Food and Drug Administration: Proposed Regulatory Framework for Modifications to AI/ML- Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback (2019).

An autonomous agent for auditing and improving the reliability of clinical AI models Food and Drug Administration: Proposed Regulatory Framework for Modifications to AI/ML- Based Software as a Medical Device (SaMD) – Discussion Paper and Request for Feedback (2019)

Reference 13

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Observation 9719de73-8bbf-469c-a49f-7e82e7349b0e · outbound

This paper cites MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions.

An autonomous agent for auditing and improving the reliability of clinical AI models MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions

Reference 14

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Observation fadb3c68-57b2-41c9-837a-1f7fe8bd759e · outbound

This paper cites Evaluating the Robustness of Self-Supervised Learning in Medical Imaging.

An autonomous agent for auditing and improving the reliability of clinical AI models Evaluating the Robustness of Self-Supervised Learning in Medical Imaging

Reference 15

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Observation 81c3da8f-4cd3-4b07-9580-0d5f6dc11fd4 · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

An autonomous agent for auditing and improving the reliability of clinical AI models AutoAugment: Learning Augmentation Policies from Data

Reference 16

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Observation 3e2cc85a-7ca9-493c-83a6-ac759479830b · outbound

This paper cites Artificial Intelligence Review 56(11), 12561–12605 (2023).

An autonomous agent for auditing and improving the reliability of clinical AI models Artificial Intelligence Review 56(11), 12561–12605 (2023)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:57.108540Z

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Observation 290b7be4-c133-445d-98c4-2290559a8903 · outbound

This paper cites In: Proceedings of the 2024 ACM Conference on Health, Inference, and Learning, pp.

An autonomous agent for auditing and improving the reliability of clinical AI models In: Proceedings of the 2024 ACM Conference on Health, Inference, and Learning, pp

Reference 18

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Observation be2b7e3c-4c52-4b9b-ba4a-380734b9d04b · outbound

This paper cites Journal of Medical Internet Research 26(4), 39672 (2024) https://doi.org/10.2196/ 39672.

An autonomous agent for auditing and improving the reliability of clinical AI models Journal of Medical Internet Research 26(4), 39672 (2024) https://doi.org/10.2196/ 39672

Reference 19

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Observation 74f59a11-ee27-4048-ac68-0020f4fe7d87 · outbound

This paper cites Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System.

An autonomous agent for auditing and improving the reliability of clinical AI models Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System

Reference 20

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Observation b55dac3f-72f4-407f-a68c-985bd2ee3364 · outbound

This paper cites Nature Biomedical Engineering 9(3), 294–306 (2025) https://doi.org/10.1038/s41551-023-01160-9.

An autonomous agent for auditing and improving the reliability of clinical AI models Nature Biomedical Engineering 9(3), 294–306 (2025) https://doi.org/10.1038/s41551-023-01160-9

Reference 21

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Observation 0dcc032f-d64c-4e89-8f21-40f9b749c315 · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

An autonomous agent for auditing and improving the reliability of clinical AI models Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 22

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Observation bde173c6-429e-4d8a-84a6-c2f0ea9e6413 · outbound

This paper cites Deep Residual Learning for Image Recognition.

An autonomous agent for auditing and improving the reliability of clinical AI models Deep Residual Learning for Image Recognition

Reference 23

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This paper cites In: International Conference on 16 Learning Representations (2021).

An autonomous agent for auditing and improving the reliability of clinical AI models In: International Conference on 16 Learning Representations (2021)

Reference 24

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Observation 7e19cd2e-c6ef-4870-8490-92f656796114 · outbound

This paper cites WILDS: A Benchmark of in-the-Wild Distribution Shifts.

An autonomous agent for auditing and improving the reliability of clinical AI models WILDS: A Benchmark of in-the-Wild Distribution Shifts

Reference 25

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This paper cites Proceedings of the National Academy of Sciences 117(23), 12592–12594 (2020) https://doi.org/10.1073/pnas.1919012117 https://www.pnas.org/doi/pdf/10.1073/pnas.1919012117.

An autonomous agent for auditing and improving the reliability of clinical AI models Proceedings of the National Academy of Sciences 117(23), 12592–12594 (2020) https://doi.org/10.1073/pnas.1919012117 https://www.pnas.org/doi/pdf/10.1073/pnas.1919012117

Reference 26

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Observation 686bea34-1381-422e-ab70-224d0e8f4f78 · outbound

This paper cites Nature medicine 27(12), 2176–2182 (2021).

An autonomous agent for auditing and improving the reliability of clinical AI models Nature medicine 27(12), 2176–2182 (2021)

Reference 27

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Observation 9f9460a0-ae4a-4f96-be7e-74984b12c6bd · outbound

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An autonomous agent for auditing and improving the reliability of clinical AI models In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 28

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Observation 01c5bb94-688b-437b-babd-9dad1cdf164a · outbound

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An autonomous agent for auditing and improving the reliability of clinical AI models In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:55.964922Z

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Observation 55104815-8c78-4bb4-98e5-ec14e47f782e · outbound

This paper cites Scientific Data 5, 180161 (2018) https://doi.

An autonomous agent for auditing and improving the reliability of clinical AI models Scientific Data 5, 180161 (2018) https://doi

Reference 30

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An autonomous agent for auditing and improving the reliability of clinical AI models AI Agents That Matter

Reference 31

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This paper cites Navigating Distribution Shifts in Medical Image Analysis: A Survey.

An autonomous agent for auditing and improving the reliability of clinical AI models Navigating Distribution Shifts in Medical Image Analysis: A Survey

Reference 32

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Observation 7f6f554c-524c-41be-a227-d1c1e7b077bc · outbound

This paper cites Radiographics 43(5), 220105 (2023) 17.

An autonomous agent for auditing and improving the reliability of clinical AI models Radiographics 43(5), 220105 (2023) 17

Reference 33

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Observation 9efff8bb-ddf8-4a52-8817-b0de8481d5c5 · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

An autonomous agent for auditing and improving the reliability of clinical AI models In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 34

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This paper cites In: International Conference on Learning Representations (2015).

An autonomous agent for auditing and improving the reliability of clinical AI models In: International Conference on Learning Representations (2015)

Reference 35

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An autonomous agent for auditing and improving the reliability of clinical AI models Adam: A Method for Stochastic Optimization

Reference 36

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An autonomous agent for auditing and improving the reliability of clinical AI models Unresolved cited work

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Observation 49bbe4d4-29d6-4722-a7df-16d75b3f407e · outbound

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An autonomous agent for auditing and improving the reliability of clinical AI models Unresolved cited work

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An autonomous agent for auditing and improving the reliability of clinical AI models Unresolved cited work

Reference 40

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An autonomous agent for auditing and improving the reliability of clinical AI models You don’t need to list a lot of different numbers, just the ones that are important

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:23:54.341271Z

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Reference 42

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An autonomous agent for auditing and improving the reliability of clinical AI models Unresolved cited work

Reference 43

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Observation 6bdcdf13-fa07-4556-84a5-f017a37bca26 · outbound

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An autonomous agent for auditing and improving the reliability of clinical AI models After the initial advice, answer questions in a concise but helpful manner

Reference 44

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Pith citing papers

No inbound Pith citation observations are available.