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Paper Citation Record · LEDGER

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities

As of 18 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2508.07031.

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

pith.paper-citation-record.v1
2508.07031 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:26:06.534145Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

31 of 31 outbound references displayed

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  • verified fuzzy17
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External citation measurements

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Outbound references

Observation aa1696ea-3f58-4a00-81e1-db30f248b666 · outbound

This paper cites GPT-4 Technical Report.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities GPT-4 Technical Report

Reference 1

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Observation 26adc0fc-6091-4403-9851-eabc5d1c763a · outbound

This paper cites Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI

Reference 2

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Observation 8d37967d-2007-4f57-952b-3feb7289c699 · outbound

This paper cites Can large language models challenge CNNs in medical image analysis? In IEEE International Conference on Image Processing (ICIP), 2025.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Can large language models challenge CNNs in medical image analysis? In IEEE International Conference on Image Processing (ICIP), 2025

Reference 3

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Observation 24bedb19-f4b0-4740-8abc-73f8563834ab · outbound

This paper cites Al-Yasriy.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Al-Yasriy

Reference 4

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Source-reported events for the cited work

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Observation 33d3bae3-5301-4103-9de9-8c2c3b18856b · outbound

This paper cites A framework to assess clini- cal safety and hallucination rates of LLMs for medical text summarisation.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities A framework to assess clini- cal safety and hallucination rates of LLMs for medical text summarisation

Reference 5

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Observation f0249c07-72a9-408f-b65d-7cc5f4a44a11 · outbound

This paper cites Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

Reference 6

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Observation 14e06fbe-2c51-4a29-b4b2-cdb14b536526 · outbound

This paper cites Breaking the shield: Vulnerabilities in content moderation for multi- modal language models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Breaking the shield: Vulnerabilities in content moderation for multi- modal language models

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation b5b4f2af-03a5-4059-9b43-016f347b1f5e · outbound

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

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models

Reference 8

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Source-reported events for the cited work

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Observation 33c717e2-0f33-48d3-a8d5-2b354f6376d2 · outbound

This paper cites Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Potential of ChatGPT and GPT-4 for data mining of free-text CT reports on lung cancer

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8ea22f44-06aa-409e-a10e-0bfb7a78ecc3 · outbound

This paper cites MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

Reference 10

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Observation c6889b5c-dc7b-41c0-96e1-a752587b6aa4 · outbound

This paper cites ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports

Reference 11

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Source-reported events for the cited work

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Observation 6cc3ea18-d382-49d6-9219-81bab60bb453 · outbound

This paper cites FactCheXcker: Miti- gating measurement hallucinations in chest X-ray report gen- eration models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities FactCheXcker: Miti- gating measurement hallucinations in chest X-ray report gen- eration models

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 1ecce22d-4213-444f-99af-31fb948f18c0 · outbound

This paper cites DALL-M: Context-aware clinical data augmentation with large language models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities DALL-M: Context-aware clinical data augmentation with large language models

Reference 13

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Source-reported events for the cited work

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Observation 7a6b1d82-2683-4970-a87d-7502176e73b1 · outbound

This paper cites Evaluation of SVM performance in the detection of lung cancer in marked ct scan dataset.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Evaluation of SVM performance in the detection of lung cancer in marked ct scan dataset

Reference 14

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Observation 9ccd7257-8280-4a92-a51f-d0bf30849d7b · outbound

This paper cites Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Tackling Hallucination from Conditional Models for Medical Image Reconstruction with DynamicDPS

Reference 15

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Source-reported events for the cited work

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Observation eb8e25c2-e61b-4243-860a-ee684ae7b5fb · outbound

This paper cites Medical hallucinations in foundation models and their impact on healthcare.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Medical hallucinations in foundation models and their impact on healthcare

Reference 16

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Observation c58c4778-7d25-4221-813b-dda01f2ee04d · outbound

This paper cites Mitigating structural hallucination in LLMs with local diffusion.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Mitigating structural hallucination in LLMs with local diffusion

Reference 17

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Source-reported events for the cited work

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Observation 578ac02a-6cd1-4a2f-8aad-23ff43fa6ad8 · outbound

This paper cites LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation

Reference 18

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Observation 0721e603-b6f0-480c-b074-205206e87cd1 · outbound

This paper cites Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation

Reference 19

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Observation e74a0b91-8df3-426e-86fe-6e295b3c4efa · outbound

This paper cites Prompt-guided generation of structured chest X-ray report using a pre-trained LLM.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Prompt-guided generation of structured chest X-ray report using a pre-trained LLM

Reference 20

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Observation c6ba83b1-7b2d-404c-8047-4a3ded74d5f4 · outbound

This paper cites Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval

Reference 21

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Observation 4f86f4cf-e2f5-4f45-9b5c-5ac6e38e2e6c · outbound

This paper cites Indiana university chest x- ray.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Indiana university chest x- ray

Reference 22

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Observation 7c9b19d9-58d6-4fb8-8500-4fa9fabe4bbf · outbound

This paper cites Med-HALT: Medical Domain Hallucination Test for Large Language Models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Med-HALT: Medical Domain Hallucination Test for Large Language Models

Reference 23

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Observation 49802b43-6d17-4874-97d1-e74574dc1996 · outbound

This paper cites Leveraging large language models to foster equity in health- care.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Leveraging large language models to foster equity in health- care

Reference 24

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Observation 10ddba5a-c4bc-42be-9e51-ae23c6ea7b82 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Gemini: A Family of Highly Capable Multimodal Models

Reference 25

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Observation 241df84c-9a33-4023-8439-fd56a3519824 · outbound

This paper cites Hallucination index: An image quality metric for gener- ative reconstruction models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Hallucination index: An image quality metric for gener- ative reconstruction models

Reference 26

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Observation fecdd32e-f578-4351-8739-a0726791c98a · outbound

This paper cites On large visual language models for medical imaging analysis: An empirical study.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities On large visual language models for medical imaging analysis: An empirical study

Reference 27

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Source-reported events for the cited work

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Observation 304975c7-4ab4-4342-9daa-e431f85bcdf2 · outbound

This paper cites Rodney Long, and George R.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Rodney Long, and George R

Reference 28

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Source-reported events for the cited work

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Observation 844042e8-b8bf-49fa-bb62-852164dcabe7 · outbound

This paper cites Med-hvl: Au- tomatic medical domain hallucination evaluation for large vision-language models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities Med-hvl: Au- tomatic medical domain hallucination evaluation for large vision-language models

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation ecf50640-6d51-4c35-b2f1-219f0360aa8e · outbound

This paper cites RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation baa9a7b1-57c2-4848-91ed-e9d0ce1365f3 · outbound

This paper cites MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models.

Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalities MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models

Reference 31

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Pith citing papers

No inbound Pith citation observations are available.