Hallucinations in medical devices | Paper Citation Record

Observation c6bd90e3-7244-4700-9910-041f1c89fc39 · outbound

This paper cites Measuring short-form factuality in large language models.

Hallucinations in medical devices Measuring short-form factuality in large language models

Reference 1

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This paper cites Health online 2013,.

Hallucinations in medical devices Health online 2013,

Reference 2

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This paper cites No. 54 Civ. 1461,.

Hallucinations in medical devices No. 54 Civ. 1461,

Reference 3

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This paper cites ONSC 2766,.

Hallucinations in medical devices ONSC 2766,

Reference 4

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This paper cites No. 2:24-cv-05205-FMO-MAA,.

Hallucinations in medical devices No. 2:24-cv-05205-FMO-MAA,

Reference 5

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This paper cites The impact of AI errors in a human- in-the-loop process,.

Hallucinations in medical devices The impact of AI errors in a human- in-the-loop process,

Reference 6

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This paper cites Quantifying the impact of AI recommendations with explanations on prescription decision making,.

Hallucinations in medical devices Quantifying the impact of AI recommendations with explanations on prescription decision making,

Reference 7

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This paper cites How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection,.

Hallucinations in medical devices How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection,

Reference 8

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This paper cites Humans inherit artificial intelligence biases,.

Hallucinations in medical devices Humans inherit artificial intelligence biases,

Reference 9

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

Hallucinations in medical devices Medical hallucination in foundation models and their impact on healthcare,

Reference 10

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Observation 6d5f0d9b-3264-4276-a24e-5bedaa2b1b5b · outbound

This paper cites Solving inverse problems using data- driven models,.

Hallucinations in medical devices Solving inverse problems using data- driven models,

Reference 11

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Observation 34db32d7-50e2-4d8c-852f-5d436b2ab84d · outbound

This paper cites Deep magnetic resonance image reconstruction: Inverse problems meet neural networks,.

Hallucinations in medical devices Deep magnetic resonance image reconstruction: Inverse problems meet neural networks,

Reference 12

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Observation cb2258ae-35aa-4cbc-9a6b-67bf24a44ca8 · outbound

This paper cites Convolutional neural networks for inverse problems in imaging: A review,.

Hallucinations in medical devices Convolutional neural networks for inverse problems in imaging: A review,

Reference 13

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Observation 0f7fd321-a832-4e3f-a8af-dd382c8c8edf · outbound

This paper cites Deep learning techniques for inverse problems in imaging,.

Hallucinations in medical devices Deep learning techniques for inverse problems in imaging,

Reference 14

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Observation 32dced7a-4cc2-47f2-b4f9-f82d849caa79 · outbound

This paper cites Deep learning for tomographic image reconstruction,.

Hallucinations in medical devices Deep learning for tomographic image reconstruction,

Reference 15

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Observation 9c10cf0f-072c-4d09-aaa3-8954755e82fe · outbound

This paper cites Deep learning for pet image reconstruction,.

Hallucinations in medical devices Deep learning for pet image reconstruction,

Reference 16

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Observation 226c72e9-b419-4314-99d6-635cbd40357e · outbound

This paper cites Image reconstruction is a new frontier of machine learning,.

Hallucinations in medical devices Image reconstruction is a new frontier of machine learning,

Reference 17

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Observation b4acb08e-258a-474d-ac51-e887387d0fee · outbound

This paper cites Null-space smoothing of tomographic images using tv norm minimization,.

Hallucinations in medical devices Null-space smoothing of tomographic images using tv norm minimization,

Reference 18

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This paper cites Null space and resolution in dynamic computerized tomography,.

Hallucinations in medical devices Null space and resolution in dynamic computerized tomography,

Reference 19

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Observation 18403ee5-c6c7-43ab-a0a9-e3f094d71ffa · outbound

This paper cites Deep Learning-Guided Image Reconstruction from Incomplete Data.

Hallucinations in medical devices Deep Learning-Guided Image Reconstruction from Incomplete Data

Reference 20

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This paper cites Deep null space learning for inverse problems: convergence analysis and rates,.

Hallucinations in medical devices Deep null space learning for inverse problems: convergence analysis and rates,

Reference 21

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This paper cites Improved inversion through use of the null space,.

Hallucinations in medical devices Improved inversion through use of the null space,

Reference 22

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Observation c4afb87d-4e81-49e1-bf8c-442e67332fc2 · outbound

This paper cites Nullspace shuttles,.

Hallucinations in medical devices Nullspace shuttles,

Reference 23

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This paper cites A perspective on deep imaging,.

Hallucinations in medical devices A perspective on deep imaging,

Reference 24

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Observation 56b5b068-5536-4de8-ae73-0d2246e9c884 · outbound

This paper cites Image reconstruction: from sparsity to data- adaptive methods and machine learning,.

Hallucinations in medical devices Image reconstruction: from sparsity to data- adaptive methods and machine learning,

Reference 25

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Observation 0ad246d7-fd69-4e91-a230-27793c4fb8d3 · outbound

This paper cites The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems.

Hallucinations in medical devices The troublesome kernel -- On hallucinations, no free lunches and the accuracy-stability trade-off in inverse problems

Reference 26

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Observation fa55d7f2-7594-4471-b5b5-5e2f4ed0591b · outbound

This paper cites On instabilities of deep learning in image reconstruction and the potential costs of AI,.

Hallucinations in medical devices On instabilities of deep learning in image reconstruction and the potential costs of AI,

Reference 27

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This paper cites Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,.

Hallucinations in medical devices Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction,

Reference 28

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Observation e17210c2-9c6f-4577-bc68-e9cf25858f4f · outbound

This paper cites The promise and peril of deep learning in microscopy,.

Hallucinations in medical devices The promise and peril of deep learning in microscopy,

Reference 29

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This paper cites Machine learning for medical imaging: methodological failures and recommendations for the future,.

Hallucinations in medical devices Machine learning for medical imaging: methodological failures and recommendations for the future,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.525512Z

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Observation 0cf192c9-defb-4ab2-bf74-3e74ad4e254e · outbound

This paper cites Advancing machine learning for mr image reconstruction with an open competition: Overview of the 2019 fastmri challenge,.

Hallucinations in medical devices Advancing machine learning for mr image reconstruction with an open competition: Overview of the 2019 fastmri challenge,

Reference 31

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Observation 2a813bc2-b837-4d8a-b772-054ea49d0e21 · outbound

This paper cites Results of the 2020 fastmri challenge for machine learning mr image reconstruction,.

Hallucinations in medical devices Results of the 2020 fastmri challenge for machine learning mr image reconstruction,

Reference 32

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Observation bcced919-a9f0-4011-8c4e-3bae97f40459 · outbound

This paper cites Deep learning reconstruction of accelerated mri: False-positive cartilage delamination inserted in mri arthrography under traction,.

Hallucinations in medical devices Deep learning reconstruction of accelerated mri: False-positive cartilage delamination inserted in mri arthrography under traction,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.536214Z

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Observation 933ee8ea-2308-4746-9c1c-71a59e9816c6 · outbound

This paper cites Robust physical-world attacks on deep learning visual classification,.

Hallucinations in medical devices Robust physical-world attacks on deep learning visual classification,

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Observation 116e329a-e979-4289-b199-642775fc2882 · outbound

This paper cites Audio adversarial examples: Targeted attacks on speech-to-text,.

Hallucinations in medical devices Audio adversarial examples: Targeted attacks on speech-to-text,

Reference 35

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Observation e0fa8352-53a9-4474-a67a-c0566b0b4d7c · outbound

This paper cites Adversarial attacks on medical machine learning,.

Hallucinations in medical devices Adversarial attacks on medical machine learning,

Reference 36

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This paper cites Why deep-learning ais are so easy to fool,.

Hallucinations in medical devices Why deep-learning ais are so easy to fool,

Reference 37

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Observation 5fc213cb-8900-4b6b-84a9-5185cc27e1ec · outbound

This paper cites The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks.

Hallucinations in medical devices The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks

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Observation e9accc45-ad4a-439a-a5d2-fe9206dccc2d · outbound

This paper cites Some investigations on robustness of deep learning in limited angle tomography,.

Hallucinations in medical devices Some investigations on robustness of deep learning in limited angle tomography,

Reference 39

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Observation 0eca8cfd-32b0-4b48-a400-708a0058c4f8 · outbound

This paper cites Measuring robustness in deep learning based compressive sensing,.

Hallucinations in medical devices Measuring robustness in deep learning based compressive sensing,

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Observation 4b67e9e2-9250-4baf-b603-6e1f51b9f519 · outbound

This paper cites Solving inverse problems with deep neural networks– robustness included?,.

Hallucinations in medical devices Solving inverse problems with deep neural networks– robustness included?,

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Observation d70ac64c-d09d-4569-a01c-d74d49332c08 · outbound

This paper cites Improving robustness of deep-learning-based image reconstruction,.

Hallucinations in medical devices Improving robustness of deep-learning-based image reconstruction,

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Observation b766c480-8b36-4c36-9806-2c32b1158755 · outbound

This paper cites Adversarial robustness of mr image reconstruction under realistic perturbations,.

Hallucinations in medical devices Adversarial robustness of mr image reconstruction under realistic perturbations,

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Observation 3a10b1ce-d921-4ded-95ca-9e734b9cde83 · outbound

This paper cites Localized adversarial artifacts for compressed sensing mri,.

Hallucinations in medical devices Localized adversarial artifacts for compressed sensing mri,

Reference 44

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Observation d8eeefce-a752-4ad6-9f76-f837bbb9f73c · outbound

This paper cites On hallucinations in tomographic image reconstruction,.

Hallucinations in medical devices On hallucinations in tomographic image reconstruction,

Reference 45

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Observation e63a22d0-bdc1-4a7d-b845-96dfe765bba6 · outbound

This paper cites The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem,.

Hallucinations in medical devices The difficulty of computing stable and accurate neural networks: On the barriers of deep learning and Smale’s 18th problem,

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Observation 6a490ad0-3c55-4060-8641-3598300249ec · outbound

This paper cites Impact of deep learning- based image super-resolution on binary signal detection,.

Hallucinations in medical devices Impact of deep learning- based image super-resolution on binary signal detection,

Reference 47

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Observation 544f1b95-9ab4-4ca6-b99f-d751a3561d40 · outbound

This paper cites Unified SNR analysis of medical imaging systems,.

Hallucinations in medical devices Unified SNR analysis of medical imaging systems,

Reference 48

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Observation b9705935-d72e-4fe9-b83b-1cfa3f9ed291 · outbound

This paper cites Icru report 54: Medical imaging-the assessment of image quality-isbn 0-913394- 53-x. april 1996, maryland, usa,.

Hallucinations in medical devices Icru report 54: Medical imaging-the assessment of image quality-isbn 0-913394- 53-x. april 1996, maryland, usa,

Reference 49

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Observation 3bc3b327-7370-43ea-bd8d-4f881ec551c3 · outbound

This paper cites Model observers for assessment of image quality,.

Hallucinations in medical devices Model observers for assessment of image quality,

Reference 50

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Observation 2aef8989-56cc-4abb-a0d7-aace5dac6889 · outbound

This paper cites Strategies for reducing radiation dose in ct,.

Hallucinations in medical devices Strategies for reducing radiation dose in ct,

Reference 52

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Observation 3ef4ecc3-281d-439e-869b-a17642fc96bc · outbound

This paper cites Algorithms for reconstruction with nondiffracting sources,.

Hallucinations in medical devices Algorithms for reconstruction with nondiffracting sources,

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Observation 11a2eeca-db75-4082-94cc-2127d6c97353 · outbound

This paper cites Acquisition and reconstruction of magnetic resonance imaging,.

Hallucinations in medical devices Acquisition and reconstruction of magnetic resonance imaging,

Reference 54

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Observation 627c51c5-0c32-4580-817b-05fa5faf5554 · outbound

This paper cites Low-dose ct with a residual encoder-decoder convolutional neural network,.

Hallucinations in medical devices Low-dose ct with a residual encoder-decoder convolutional neural network,

Reference 55

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Observation c454a816-7d45-4ec0-8aca-2965454733eb · outbound

This paper cites Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss,.

Hallucinations in medical devices Low-dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss,

Reference 56

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Observation 9ac45f69-8b67-4534-86a5-d6916a382ca8 · outbound

This paper cites Deep admm-net for compressive sensing mri,.

Hallucinations in medical devices Deep admm-net for compressive sensing mri,

Reference 57

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Observation 47c6f30a-e644-4f85-a8fa-d622cee162f7 · outbound

This paper cites Deep networks and mutual information maximization for cross-modal medical image synthesis,.

Hallucinations in medical devices Deep networks and mutual information maximization for cross-modal medical image synthesis,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.629714Z

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Observation d4f9b1ea-b338-4a37-9f0d-57e377bfb2ed · outbound

This paper cites Cross-modality image synthesis from unpaired data using cyclegan: Effects of gradient consistency loss and training data size,.

Hallucinations in medical devices Cross-modality image synthesis from unpaired data using cyclegan: Effects of gradient consistency loss and training data size,

Reference 59

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Observation f9da669f-17f9-4c38-8ae1-2bee3ff74b11 · outbound

This paper cites The data processing inequality and stochastic resonance,.

Hallucinations in medical devices The data processing inequality and stochastic resonance,

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Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.638242Z

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Observation 99e43a5d-3774-4a13-a448-1e57864eb423 · outbound

This paper cites On hallucinations in tomographic image reconstruction,.

Hallucinations in medical devices On hallucinations in tomographic image reconstruction,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.641842Z

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Observation b2440d9b-4ccf-4b4c-b9dc-ab0165b909b1 · outbound

This paper cites Null space imaging: nonlinear magnetic encoding fields designed complementary to receiver coil sensitivities for improved acceleration in parallel imaging,.

Hallucinations in medical devices Null space imaging: nonlinear magnetic encoding fields designed complementary to receiver coil sensitivities for improved acceleration in parallel imaging,

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This paper cites Image artifacts: Appearances, causes, and corrections,.

Hallucinations in medical devices Image artifacts: Appearances, causes, and corrections,

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Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.652354Z

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This paper cites Artifacts in magnetic resonance imaging,.

Hallucinations in medical devices Artifacts in magnetic resonance imaging,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.905598Z

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This paper cites an unresolved cited work.

Hallucinations in medical devices Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-15T17:20:18.892592Z

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Observation b16bfa23-1b22-40bf-9173-0fe323dc0afb · outbound

This paper cites fastmri+, clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data,.

Hallucinations in medical devices fastmri+, clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data,

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Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.663948Z

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Observation 76391b41-c0f4-4efa-bb88-26eef2721b7c · outbound

This paper cites Lungx challenge for computerized lung CONTENTS16 nodule classification,.

Hallucinations in medical devices Lungx challenge for computerized lung CONTENTS16 nodule classification,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.880600Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8753c6cb-082e-4a94-98fc-0b4686068ec2 · outbound

This paper cites Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,.

Hallucinations in medical devices Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.866769Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f31e1375-65d1-4c59-8484-68882923da8a · outbound

This paper cites No” zero-shot.

Hallucinations in medical devices No” zero-shot

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.854888Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation da7ab091-f039-43fc-aa16-0177407b933a · outbound

This paper cites Generative adversarial networks in medical image augmentation: a review,.

Hallucinations in medical devices Generative adversarial networks in medical image augmentation: a review,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.842050Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fd815c06-ba78-40aa-a04b-170c0beb2855 · outbound

This paper cites Data augmentation for medical imaging: A systematic literature review,.

Hallucinations in medical devices Data augmentation for medical imaging: A systematic literature review,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.829567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 440cfb7c-be46-4d6e-a8fb-3c6df247d01f · outbound

This paper cites Synthetic breast ultrasound images: A study to overcome medical data sharing barriers,.

Hallucinations in medical devices Synthetic breast ultrasound images: A study to overcome medical data sharing barriers,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.816294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 82ce9566-2bbc-465b-a321-cde1723e9ef7 · outbound

This paper cites Analyzing gan artifacts for simulating mammograms: application towards finding mammographically-occult cancer,.

Hallucinations in medical devices Analyzing gan artifacts for simulating mammograms: application towards finding mammographically-occult cancer,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.803527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b5ec6e38-1925-4b27-bbcd-d5b7a3f31efe · outbound

This paper cites Selective synthetic augmentation with histogan for improved histopathology image classification,.

Hallucinations in medical devices Selective synthetic augmentation with histogan for improved histopathology image classification,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.791318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f13254c4-001d-4a0b-9a3f-e96b93cb29f4 · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

Hallucinations in medical devices Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.698621Z

Source-reported events for the cited work

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Observation 738b43a7-7914-486a-a13e-b62b2a07bc23 · outbound

This paper cites A method for evaluating deep generative models of images for hallucinations in high-order spatial context,.

Hallucinations in medical devices A method for evaluating deep generative models of images for hallucinations in high-order spatial context,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.778991Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6e70143a-7d45-49e6-b2ec-98eb9eaabe4d · outbound

This paper cites Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context,.

Hallucinations in medical devices Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.765431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 89456544-0a08-460f-9410-a460fb529ebe · outbound

This paper cites Assessing the ability of generative adversarial networks to learn canonical medical image statistics,.

Hallucinations in medical devices Assessing the ability of generative adversarial networks to learn canonical medical image statistics,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.752194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d59c81f7-e1b7-4041-9108-4159755efa49 · outbound

This paper cites A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis,.

Hallucinations in medical devices A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.740026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 97be3c32-ff82-490c-affc-4a8517e4b5a8 · outbound

This paper cites Report on the aapm grand challenge on deep generative modeling for learning medical image statistics,.

Hallucinations in medical devices Report on the aapm grand challenge on deep generative modeling for learning medical image statistics,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.727163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 56b3b9e9-5712-4c00-817b-c9608bc9ba96 · outbound

This paper cites A knowledge-based method for detecting network-induced shape artifacts in synthetic images,.

Hallucinations in medical devices A knowledge-based method for detecting network-induced shape artifacts in synthetic images,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.714140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 228241ec-3475-4cce-8d1a-6578f4c2e3df · outbound

This paper cites Distribution matching losses can hallucinate features in medical image translation,.

Hallucinations in medical devices Distribution matching losses can hallucinate features in medical image translation,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.725102Z

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Unavailable: canonical work link unavailable.

Observation cb2ec5c8-d67f-4d6a-ab46-86d78a50d10f · outbound

This paper cites Cyclegan for virtual stain transfer: Is seeing really believing?,.

Hallucinations in medical devices Cyclegan for virtual stain transfer: Is seeing really believing?,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.700520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e4159df3-1fe9-4f24-ad90-ccdfb71e60ca · outbound

This paper cites Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,.

Hallucinations in medical devices Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.732943Z

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Observation 6125e8d5-fbd8-46be-8a4c-8a53a919f342 · outbound

This paper cites Benchmarking large language models for news summarization,.

Hallucinations in medical devices Benchmarking large language models for news summarization,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.678659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9d4242b6-bc8a-4396-a08d-0da597860f2d · outbound

This paper cites Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family.

Hallucinations in medical devices Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.740254Z

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Unavailable: canonical work link unavailable.

Observation 3ac3fcb6-a7da-4873-a1d4-3d48837ff954 · outbound

This paper cites Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis.

Hallucinations in medical devices Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.744358Z

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Observation 52c6259d-1c6e-4ef7-8127-c5204534a678 · outbound

This paper cites Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics.

Hallucinations in medical devices Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.748429Z

Source-reported events for the cited work

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Observation 20e81740-0004-4045-b1b0-e951ea348a32 · outbound

This paper cites Challenges in building intelligent open-domain dialog systems,.

Hallucinations in medical devices Challenges in building intelligent open-domain dialog systems,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.664299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8dbfcdb1-337e-4245-a6b2-e6f0701a6ae4 · outbound

This paper cites Language models are unsupervised multitask learners,.

Hallucinations in medical devices Language models are unsupervised multitask learners,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.757777Z

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Observation 216dcdf5-c154-4722-8ab4-b3c6524fccd5 · outbound

This paper cites Training language models to follow instructions with human feedback,.

Hallucinations in medical devices Training language models to follow instructions with human feedback,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.761547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 036c901d-5dcc-4747-afb1-d6aaff1171ab · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Hallucinations in medical devices Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.765416Z

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Observation 8b6464a7-e927-4c79-b9f4-e654703aeb20 · outbound

This paper cites How Much Knowledge Can You Pack Into the Parameters of a Language Model?.

Hallucinations in medical devices How Much Knowledge Can You Pack Into the Parameters of a Language Model?

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.769021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01af0fcb-b79f-4362-ac49-bacd2ebcc206 · outbound

This paper cites Black swans and the domains of statistics,.

Hallucinations in medical devices Black swans and the domains of statistics,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.626347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 26c5e10a-84e4-445b-ba16-3d69a6d32b0a · outbound

This paper cites Survey of hallucination in natural language generation,.

Hallucinations in medical devices Survey of hallucination in natural language generation,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.777002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3352f614-6f1d-4831-9275-06abf5515157 · outbound

This paper cites Diversifying Dialogue Generation with Non-Conversational Text.

Hallucinations in medical devices Diversifying Dialogue Generation with Non-Conversational Text

Reference 97

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:20:18.239211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc0427cd-57b0-4bd8-bafe-a2a2beeaad6f · outbound

This paper cites UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation.

Hallucinations in medical devices UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

Reference 98

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:20:18.220989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 382e8d4a-2240-44a4-8030-247961719e08 · outbound

This paper cites Retrieval Augmentation Reduces Hallucination in Conversation.

Hallucinations in medical devices Retrieval Augmentation Reduces Hallucination in Conversation

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.788613Z

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Observation ae321199-70bc-4bcd-8c55-f3b62b2511bc · outbound

This paper cites Towards Conversational Diagnostic AI.

Hallucinations in medical devices Towards Conversational Diagnostic AI

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-15T17:20:17.792556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36356004-c44f-4430-941f-741d996a430c · outbound

This paper cites Towards generalist biomedical ai,.

Hallucinations in medical devices Towards generalist biomedical ai,

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.602466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84af9b04-1bf9-4382-8993-fb3e964bdb7b · outbound

This paper cites Towards a holistic framework for multimodal llm in 3d brain ct radiology report generation,.

Hallucinations in medical devices Towards a holistic framework for multimodal llm in 3d brain ct radiology report generation,

Reference 103

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:20:18.576971Z

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