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Paper Citation Record · LEDGER

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing

As of 7 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.27428.

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

pith.paper-citation-record.v1
2607.27428 v1

Coverage vector

measured 28 of 28 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-31T01:13:49.674435Z

measured 28 of 28 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

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measured 0 of 1 external citation measurements

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Reference resolution

28 of 28 outbound references displayed

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Outbound references

Observation 05250727-cc63-4b6e-9a59-98a42d493fbc · outbound

This paper cites A brief history of ai: how to prevent another winter (a critical review).PET clinics, 16(4):449–469, 2021.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing A brief history of ai: how to prevent another winter (a critical review).PET clinics, 16(4):449–469, 2021

Reference 1

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Observation 1bf9119d-1c62-4c8b-90cd-dfde52423f53 · outbound

This paper cites Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and Interventional Radiology, 2025.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Foundation models for radiology: fundamentals, applications, opportunities, challenges, risks, and prospects.Diagnostic and Interventional Radiology, 2025

Reference 2

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Observation 3ce38515-1ac9-4218-b14b-1b865f06889d · outbound

This paper cites Fda-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Fda-authorized oncology artificial intelligence and machine learning devices and their clinical evidence: A cross-sectional analysis

Reference 3

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Observation 903b683f-0d13-4bb8-9999-1f32139273ee · outbound

This paper cites an unresolved cited work.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Unresolved cited work

Reference 4

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Observation fde889c9-cf48-4e25-82cb-c423358e251f · outbound

This paper cites The revolution of glu- cose monitoring methods and systems: A survey.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing The revolution of glu- cose monitoring methods and systems: A survey

Reference 5

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Observation c9918beb-5e1d-438b-84ed-20d596afefc1 · outbound

This paper cites an unresolved cited work.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Unresolved cited work

Reference 6

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Observation 3b24cc87-3ee4-4021-895e-a6ce756cda09 · outbound

This paper cites Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data

Reference 7

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Observation 981af992-3ad9-43b9-9997-ef502061284d · outbound

This paper cites Trustworthy artificial intelligence in medical imaging.PET clinics, 17 (1):1, 2022.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Trustworthy artificial intelligence in medical imaging.PET clinics, 17 (1):1, 2022

Reference 8

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Observation 0311fae3-4b8f-4a19-9e30-bf188c2a9b9c · outbound

This paper cites Objective task-based evaluation of artificial intelligence-based medical imaging methods: framework, strategies, and role of the physician.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Objective task-based evaluation of artificial intelligence-based medical imaging methods: framework, strategies, and role of the physician

Reference 9

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Observation 7853e1f7-b000-4b21-a246-96314aa92e16 · outbound

This paper cites Interactive per- sonalized ai for physician-in-the-loop 3d tumor segmentation on ct.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Interactive per- sonalized ai for physician-in-the-loop 3d tumor segmentation on ct

Reference 10

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Observation 0178e503-a1e2-4da2-9870-6e686dfd0cca · outbound

This paper cites Continual-zoo: Leveraging zoo models for continual classification of medical images.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Continual-zoo: Leveraging zoo models for continual classification of medical images

Reference 11

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Observation eb085670-52f4-403c-9e23-1caad7d915c6 · outbound

This paper cites GC 2: Generalizable continual classifica- tion of medical images.IEEE Transactions on Medical Imaging, 43(11):3767–3779, 2024.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing GC 2: Generalizable continual classifica- tion of medical images.IEEE Transactions on Medical Imaging, 43(11):3767–3779, 2024

Reference 12

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Observation 71dc0c73-7005-457b-991a-98c8191539cf · outbound

This paper cites Continual-GEN: Continual group ensembling for domain-agnostic skin lesion classification.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Continual-GEN: Continual group ensembling for domain-agnostic skin lesion classification

Reference 13

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Observation 94538430-6200-4089-adb3-4973c9ad87f9 · outbound

This paper cites Biaspruner: Mitigating bias transfer in continual learning for fair medical image analysis.Medical image analysis, page 103764, 2025.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Biaspruner: Mitigating bias transfer in continual learning for fair medical image analysis.Medical image analysis, page 103764, 2025

Reference 14

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Observation 69876a00-e64c-4d57-916e-3727b185e792 · outbound

This paper cites The false hope of current ap- proaches to explainable artificial intelligence in health care.The lancet digital health, 3(11):e745– e750, 2021.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing The false hope of current ap- proaches to explainable artificial intelligence in health care.The lancet digital health, 3(11):e745– e750, 2021

Reference 15

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Observation b2960c85-683a-4a89-b8f7-5fef4561c85a · outbound

This paper cites On the challenges and perspectives of foundation models for medical image analysis.Medical image analysis, 91:102996, 2024.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing On the challenges and perspectives of foundation models for medical image analysis.Medical image analysis, 91:102996, 2024

Reference 16

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Observation 94d6f6a6-db07-470e-aad8-e03f954fb60c · outbound

This paper cites Boosternet: Improving domain gener- alization of deep neural nets using culpability-ranked features.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Boosternet: Improving domain gener- alization of deep neural nets using culpability-ranked features

Reference 17

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Observation e4a30f14-2322-4253-99d4-b6894f92078c · outbound

This paper cites Towards gener- alist foundation model for radiology by leveraging web-scale 2d&3d medical data.Nature Com- munications, 16(1):7866, 2025.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Towards gener- alist foundation model for radiology by leveraging web-scale 2d&3d medical data.Nature Com- munications, 16(1):7866, 2025

Reference 18

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Observation adc722e9-486b-4f04-864d-3d792c7e62a1 · outbound

This paper cites Vision Foundation Models for Computed Tomography.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Vision Foundation Models for Computed Tomography

Reference 19

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Observation 213babc1-de12-41dd-be43-672522b2c8d1 · outbound

This paper cites Avit: Adapting vision transform- ers for small skin lesion segmentation datasets.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Avit: Adapting vision transform- ers for small skin lesion segmentation datasets

Reference 20

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Observation 9831df9a-36c7-4dff-9192-deb9f5627844 · outbound

This paper cites Code and data sharing practices in the radiology artificial intelligence literature: a meta-research study.Radiol- ogy: Artificial Intelligence, 4(5):e220081, 2022.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Code and data sharing practices in the radiology artificial intelligence literature: a meta-research study.Radiol- ogy: Artificial Intelligence, 4(5):e220081, 2022

Reference 21

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Observation 9e5fa54a-9cb5-43fe-917c-4f822a5691f4 · outbound

This paper cites Much ado about data ownership.Harv.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Much ado about data ownership.Harv

Reference 22

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Observation 643fe357-0d3f-4228-9c47-55d785540237 · outbound

This paper cites Herington et al.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Herington et al

Reference 23

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Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Rahmim et al

Reference 24

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Observation c77d7f1b-1322-497d-91df-b712140c510a · outbound

This paper cites Fayaz-Bakhsh, J.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Fayaz-Bakhsh, J

Reference 25

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Observation 4f5559e4-7c25-4805-9112-21588a7a371e · outbound

This paper cites Artificial Intelligence in Healthcare: Lost In Translation?.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Artificial Intelligence in Healthcare: Lost In Translation?

Reference 26

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Observation f0f2fd6b-479f-4652-a059-0f2e1f0ae148 · outbound

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Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Unresolved cited work

Reference 27

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Observation 5fe31fa6-1b0a-4a36-91b1-a647320338ea · outbound

This paper cites Medagentbench: a virtual ehr environment to benchmark medical llm agents.

Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing Medagentbench: a virtual ehr environment to benchmark medical llm agents

Reference 28

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