Paper Citation Record · LEDGER

Can Large Language Models Challenge CNNs in Medical Image Analysis?

As of 10 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2505.23503.

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

pith.paper-citation-record.v1
2505.23503 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:47:59.333243Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:47:55.415683Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:47:59.473383Z

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy30
unresolved9
parse uncertain1
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 09f94f0b-7708-4ce5-8fc5-d2ba4ff87006 · outbound

This paper cites Can Large Language Models Challenge CNNs in Medical Image Analysis?.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Can Large Language Models Challenge CNNs in Medical Image Analysis?

Reference 1

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Observation 426bd6dc-cf4b-4b31-ae59-4082b902e9a9 · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:48:06.821538Z

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Observation 6ba62a96-5f44-4d39-86fe-b794e11603fe · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:48:06.380403Z

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Observation b7babd35-24f6-4fec-9328-9cdb66cdfd8d · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 4

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Observation e121b5a2-e941-4b59-8271-631443dac242 · outbound

This paper cites From this dataset, we first extract only those samples correspond- ing to the desired label (e.g., “normal” in the context of COVID classification).

Can Large Language Models Challenge CNNs in Medical Image Analysis? From this dataset, we first extract only those samples correspond- ing to the desired label (e.g., “normal” in the context of COVID classification)

Reference 5

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Observation 50d47b90-8f81-493a-a466-8dc55c4dec6c · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 6

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Observation 9eaad0ea-c217-4275-b0f1-7930dbbf0af6 · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 7

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Observation f8e44618-c0e1-4e68-a2c4-e889ca1c647d · outbound

This paper cites Large language models in medicine,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Large language models in medicine,

Reference 8

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Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 9

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Observation c4988247-ef6a-4c5a-b7e4-63ebcca17961 · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 10

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Observation cd03c051-d9a3-4382-8cf9-c592e3a83138 · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 11

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Observation ffa89ccf-d945-4d41-bb57-86acee4ff1bd · outbound

This paper cites The first line should contain only either normal or abnormal.

Can Large Language Models Challenge CNNs in Medical Image Analysis? The first line should contain only either normal or abnormal

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:04.591711Z

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Observation 964d79d8-a0d9-478e-b354-ea3551f66e30 · outbound

This paper cites Evaluation Metrics The performance of CNNs and LLMs was evaluated using sev- eral key metrics.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Evaluation Metrics The performance of CNNs and LLMs was evaluated using sev- eral key metrics

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:04.331554Z

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Observation b8ddcbb6-7f91-45c0-92d2-55df6c9f8337 · outbound

This paper cites an unresolved cited work.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unresolved cited work

Reference 14

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Observation 367be540-15f5-468b-8230-2d61f7e993a2 · outbound

This paper cites A review of deep learning in medical imag- ing: Imaging traits, technology trends, case studies with progress highlights, and future promises,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? A review of deep learning in medical imag- ing: Imaging traits, technology trends, case studies with progress highlights, and future promises,

Reference 15

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Observation ab48e3f4-8555-42b2-8653-f2a9c0765162 · outbound

This paper cites This dataset is commonly used to benchmark AI models for classifying chest X-rays.

Can Large Language Models Challenge CNNs in Medical Image Analysis? This dataset is commonly used to benchmark AI models for classifying chest X-rays

Reference 16

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Source-reported events for the cited work

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Observation 69bb2e91-b2fe-4baa-879a-d3d4333658ac · outbound

This paper cites Two-stage selective ensemble of CNN via deep tree train- ing for medical image classification,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Two-stage selective ensemble of CNN via deep tree train- ing for medical image classification,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:03.566225Z

Source-reported events for the cited work

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Observation ee6acf73-7bba-46e8-a8cc-102238160d99 · outbound

This paper cites Blockchain- federated-learning and deep learning models for COVID- 19 detection using CT imaging,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Blockchain- federated-learning and deep learning models for COVID- 19 detection using CT imaging,

Reference 18

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Source-reported events for the cited work

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Observation 8f4a66c4-3a4f-4608-99a0-1de410477b0b · outbound

This paper cites Znet: Deep learning approach for 2D MRI brain tumor segmentation,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Znet: Deep learning approach for 2D MRI brain tumor segmentation,

Reference 19

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Observation 8f1ce7bf-3bad-45f3-a372-d93e08e0dd5e · outbound

This paper cites UNETR: Transformers for 3D medical image segmentation,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? UNETR: Transformers for 3D medical image segmentation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:02.981237Z

Source-reported events for the cited work

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Observation 056f473e-3581-4ba4-9b2d-543c1fdb4f41 · outbound

This paper cites CapsCovNet: A modified capsule network to diagnose COVID-19 from multimodal medical imaging,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? CapsCovNet: A modified capsule network to diagnose COVID-19 from multimodal medical imaging,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:02.831029Z

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Observation f6b6bb93-1a1c-4ddb-bd6b-3adb7abd5ab2 · outbound

This paper cites Unsupervised medical image translation with adversarial diffusion models,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Unsupervised medical image translation with adversarial diffusion models,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:02.710129Z

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Observation da807b35-c5f9-4f31-b3bc-c1cd26e6ae46 · outbound

This paper cites Eval- uation and mitigation of the limitations of large language models in clinical decision-making,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Eval- uation and mitigation of the limitations of large language models in clinical decision-making,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:02.324741Z

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Observation c351cda3-c118-4070-8de3-d99f95a4038a · outbound

This paper cites Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models

Reference 24

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Source-reported events for the cited work

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Observation 92a931d8-b3ff-4f03-96ad-9238b02b4ddd · outbound

This paper cites Performance of ChatGPT on USMLE: potential for AI-assisted medical education us- ing large language models,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Performance of ChatGPT on USMLE: potential for AI-assisted medical education us- ing large language models,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:02.163837Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f1de3b1c-73d0-4106-b3c6-d6343d7bc2e4 · outbound

This paper cites Explainability for large language models: A survey,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Explainability for large language models: A survey,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:01.992411Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 333f71ae-a0c7-4f66-90c2-ac2b48b59dc5 · outbound

This paper cites CXR-LLaV A: a multimodal large language model for interpreting chest X-ray images,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? CXR-LLaV A: a multimodal large language model for interpreting chest X-ray images,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:01.813875Z

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Observation 3a81eb59-dfb7-45b7-88f3-607055ba194a · outbound

This paper cites A LLM-based hybrid-transformer diagnosis system in healthcare,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? A LLM-based hybrid-transformer diagnosis system in healthcare,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:01.674075Z

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Observation 9aad3807-7693-453a-ae47-471f0b34c1a5 · outbound

This paper cites Chat- CAD+: Toward a universal and reliable interactive CAD using LLMs,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Chat- CAD+: Toward a universal and reliable interactive CAD using LLMs,

Reference 29

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Observation 5a20ab3b-cfba-46bf-8218-0e0b40fde613 · outbound

This paper cites Can AI help in screening viral and COVID-19 pneumo- nia?,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Can AI help in screening viral and COVID-19 pneumo- nia?,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:01.293603Z

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Observation bf89030b-377e-4869-8866-843340316962 · outbound

This paper cites Exploring the effect of image enhancement techniques on COVID-19 detec- tion using chest X-ray images,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Exploring the effect of image enhancement techniques on COVID-19 detec- tion using chest X-ray images,

Reference 31

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Observation bdd27c7f-adf1-48a2-b82f-a20f5c8905c5 · outbound

This paper cites Brain tumor MRI dataset,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Brain tumor MRI dataset,

Reference 32

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Observation 0a0529e0-6fd7-439b-b5e1-2448e08fb9b5 · outbound

This paper cites Chest CT-scan images dataset,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Chest CT-scan images dataset,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:00.852385Z

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Observation 8b89bb19-f4ae-4b57-bd03-d1d35d064183 · outbound

This paper cites MMBERT: Mul- timodal BERT pretraining for improved medical VQA,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? MMBERT: Mul- timodal BERT pretraining for improved medical VQA,

Reference 34

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Observation b5e8cd18-8a41-4d82-8e80-61a91e8af733 · outbound

This paper cites Multi-task paired masking with alignment modeling for medical vision-language pre-training,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Multi-task paired masking with alignment modeling for medical vision-language pre-training,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:00.587705Z

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Observation fdbe237b-e147-484b-bc42-8a868e06bbb2 · outbound

This paper cites On large visual language models for medical imaging analy- sis: An empirical study,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? On large visual language models for medical imaging analy- sis: An empirical study,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:00.453871Z

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Observation d427b9cf-7dbb-43ab-92c7-a85dba1cdfd1 · outbound

This paper cites Large language mod- els in healthcare and medical domain: A review,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Large language mod- els in healthcare and medical domain: A review,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:00.311555Z

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Observation 6662363c-cc9f-4a43-a95c-161821d80eb3 · outbound

This paper cites Ex- plainable vertical federated learning for healthcare: En- suring privacy and optimal accuracy,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Ex- plainable vertical federated learning for healthcare: En- suring privacy and optimal accuracy,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:00.195148Z

Source-reported events for the cited work

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Observation 8a4a233a-26fb-4476-ba5e-7905db86d8ba · outbound

This paper cites On calibration of modern neural networks,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? On calibration of modern neural networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:48:00.060314Z

Source-reported events for the cited work

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Observation 3f8a7133-072a-4c16-8385-a3c361745ee8 · outbound

This paper cites Estimating the carbon footprint of bloom, a 176b parameter language model,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Estimating the carbon footprint of bloom, a 176b parameter language model,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:59.919205Z

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 2ee97a13-a651-4da5-aff6-0f327bb65262 · outbound

This paper cites Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning,.

Can Large Language Models Challenge CNNs in Medical Image Analysis? Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:47:59.768060Z

Source-reported events for the cited work

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Pith citing papers

Observation 09f94f0b-7708-4ce5-8fc5-d2ba4ff87006 · inbound