Benchmarking Foundation Models with Multimodal Public Electronic Health Records | Paper Citation Record

Observation 84305f8a-679a-49bd-bd4d-77cdb1d29f9c · outbound

This paper cites Harnessing ehr data for health research,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Harnessing ehr data for health research,

Reference 1

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Observation b9c85cf0-5945-4bfe-92bf-c409462b6461 · outbound

This paper cites Personalized medicine and the power of electronic health records,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Personalized medicine and the power of electronic health records,

Reference 2

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Observation e482e653-2358-4db6-886c-fcabce35a160 · outbound

This paper cites Mining electronic health records: towards better research applications and clinical care,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Mining electronic health records: towards better research applications and clinical care,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.726118Z

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Observation 9b0cde92-3cb8-4a32-bab4-7a4186e76e4b · outbound

This paper cites A guide to sharing open healthcare data under the general data protection regulation,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records A guide to sharing open healthcare data under the general data protection regulation,

Reference 4

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This paper cites Mimic- iv, a freely accessible electronic health record dataset,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Mimic- iv, a freely accessible electronic health record dataset,

Reference 5

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This paper cites Comparing open-access database and traditional intensive care studies using machine learning: bibliometric analysis study,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Comparing open-access database and traditional intensive care studies using machine learning: bibliometric analysis study,

Reference 6

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Observation ea81bfc4-7d7e-4831-a295-5079d6a14a3a · outbound

This paper cites Multimodal biomedical ai,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Multimodal biomedical ai,

Reference 7

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Observation a2d86104-1b32-4f9f-9477-1f6d51cdd7bd · outbound

This paper cites Foundation models for generalist medical artificial intelligence,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Foundation models for generalist medical artificial intelligence,

Reference 8

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This paper cites Towards generalist biomedical ai,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Towards generalist biomedical ai,

Reference 9

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Observation 49246809-d0f4-436e-89e5-f4178f6a50e9 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records On the Opportunities and Risks of Foundation Models

Reference 10

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Observation 6657a2a4-8675-4239-ac5b-0a5ab9dc4f17 · outbound

This paper cites Multimodal data matters: Language model pre-training over structured and unstructured electronic health records,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Multimodal data matters: Language model pre-training over structured and unstructured electronic health records,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.554102Z

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Observation c34310a8-1fa2-4982-81b5-2784166655be · outbound

This paper cites Multimodal foundation models: From specialists to general-purpose assistants,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Multimodal foundation models: From specialists to general-purpose assistants,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.527120Z

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This paper cites Foundation model for advancing healthcare: challenges, opportunities and future directions,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Foundation model for advancing healthcare: challenges, opportunities and future directions,

Reference 13

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Observation ee88943d-0c54-46e9-961a-398d3710b20e · outbound

This paper cites A framework for evaluating the efficacy of foundation embedding models in healthcare,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records A framework for evaluating the efficacy of foundation embedding models in healthcare,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.471857Z

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Observation aa9d373f-891d-4ce8-9928-0c38390cdcd3 · outbound

This paper cites The application of multimodal large language models in medicine,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records The application of multimodal large language models in medicine,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.448842Z

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Observation 7e509667-7fbf-4e93-b302-1af73e8b1fa6 · outbound

This paper cites Large language models encode clinical knowledge,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Large language models encode clinical knowledge,

Reference 16

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Observation 12ab48eb-db86-4011-9423-2f072f84691f · outbound

This paper cites Capabilities of Gemini Models in Medicine.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Capabilities of Gemini Models in Medicine

Reference 17

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Observation b10c29ba-3752-4ce5-931d-9bc95f0a62c7 · outbound

This paper cites Large language models in health care: Development, 10 IEEE TRANSACTIONS AND JOURNALS TEMPLATE applications, and challenges,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Large language models in health care: Development, 10 IEEE TRANSACTIONS AND JOURNALS TEMPLATE applications, and challenges,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.400926Z

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Observation b5a6331d-336a-4e73-9fb7-893ea7952e93 · outbound

This paper cites Multimodal large language models in health care: applications, challenges, and future outlook,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Multimodal large language models in health care: applications, challenges, and future outlook,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.379922Z

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Observation 9fd41c81-cf51-486b-8b01-8b3e7ab3bab7 · outbound

This paper cites Disparities in clinical studies of ai enabled applications from a global perspective,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Disparities in clinical studies of ai enabled applications from a global perspective,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.350477Z

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Observation 7273d99f-6caf-46a3-a770-b33477a2d396 · outbound

This paper cites The shaky foundations of large language models and foundation models for electronic health records,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records The shaky foundations of large language models and foundation models for electronic health records,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.313991Z

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Observation e85fa830-7094-4ad1-b71c-29b45119a74e · outbound

This paper cites Gmai-mmbench: A comprehensive multimodal evaluation benchmark towards general medical ai,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Gmai-mmbench: A comprehensive multimodal evaluation benchmark towards general medical ai,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.292540Z

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Observation 865c7325-2e79-429c-8aaa-da6a70c23ee2 · outbound

This paper cites Multimodal clinical benchmark for emergency care (mc- bec): A comprehensive benchmark for evaluating foundation models in emergency medicine,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Multimodal clinical benchmark for emergency care (mc- bec): A comprehensive benchmark for evaluating foundation models in emergency medicine,

Reference 23

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Observation 737f08df-c08c-4fdd-ba30-8f37ae6cf2bd · outbound

This paper cites Integrated multimodal artificial intelligence framework for healthcare applications,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Integrated multimodal artificial intelligence framework for healthcare applications,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.231987Z

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Observation fb6c1542-c6f4-49bb-bf82-fa6f9cc85abd · outbound

This paper cites A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.201617Z

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Observation cdd4feb1-a0e8-42d7-b8eb-bfb2505c7960 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 26

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Observation 4d3697b6-780c-4db5-bd2f-0a729ce250a4 · outbound

This paper cites MOMENT: A Family of Open Time-series Foundation Models.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records MOMENT: A Family of Open Time-series Foundation Models

Reference 27

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Observation 666dfae0-3411-4b22-aac0-a905ec4cecea · outbound

This paper cites ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

Reference 28

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This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 29

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This paper cites Improved fine-tuning of in-domain transformer model for inferring covid-19 presence in multi- institutional radiology reports,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Improved fine-tuning of in-domain transformer model for inferring covid-19 presence in multi- institutional radiology reports,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.163494Z

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Observation c694d716-4d64-4cbf-ad5b-da863c61ef58 · outbound

This paper cites Benchmarking emergency department prediction models with machine learning and public electronic health records,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Benchmarking emergency department prediction models with machine learning and public electronic health records,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.136794Z

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Observation 84708dc7-70e2-4a6a-bec7-01007ff2c2de · outbound

This paper cites Llava-med: Training a large language-and-vision assistant for biomedicine in one day,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Llava-med: Training a large language-and-vision assistant for biomedicine in one day,

Reference 32

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This paper cites Visual instruction tuning,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Visual instruction tuning,

Reference 33

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This paper cites A unified approach to interpreting model predictions,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records A unified approach to interpreting model predictions,

Reference 34

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This paper cites “everyone wants to do the model work, not the data work.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records “everyone wants to do the model work, not the data work

Reference 35

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Observation 67f8739e-07c5-4f11-84c1-ad2bf285bd9d · outbound

This paper cites An ethics assessment tool for artificial intelligence implementation in healthcare: Care-ai,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records An ethics assessment tool for artificial intelligence implementation in healthcare: Care-ai,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:16.017967Z

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Observation a0a95f75-0dbf-49b3-ae03-b95ea3d88a7f · outbound

This paper cites As artificial intelligence goes multimodal, medical appli- cations multiply,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records As artificial intelligence goes multimodal, medical appli- cations multiply,

Reference 37

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This paper cites Ascle—a python natural language processing toolkit for medical text generation: development and evaluation study,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Ascle—a python natural language processing toolkit for medical text generation: development and evaluation study,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.964927Z

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This paper cites Are time series foundation models ready for vital sign forecasting in healthcare?.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Are time series foundation models ready for vital sign forecasting in healthcare?

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.938397Z

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Observation a87b39f2-66cd-4c3c-8dc5-2014fd98b14a · outbound

This paper cites Enhancing medical coding efficiency through domain-specific fine-tuned large language models,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Enhancing medical coding efficiency through domain-specific fine-tuned large language models,

Reference 40

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This paper cites Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Artificial intelligence and multimodal data fusion for smart healthcare: topic modeling and bibliometrics,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.885597Z

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Observation 179253b2-3a88-47c4-88c5-a92c278a4e79 · outbound

This paper cites Visual–language foundation models in medicine,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Visual–language foundation models in medicine,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.851446Z

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Observation 3b84cb56-8528-4a37-a723-d71ae3767f39 · outbound

This paper cites LongHealth: A Question Answering Benchmark with Long Clinical Documents.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records LongHealth: A Question Answering Benchmark with Long Clinical Documents

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This paper cites Omnimedvqa: A new large-scale comprehensive evaluation benchmark for medical lvlm,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Omnimedvqa: A new large-scale comprehensive evaluation benchmark for medical lvlm,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.827980Z

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Observation 93ab5eee-1d55-42d7-94d2-b526e918378a · outbound

This paper cites Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.804259Z

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This paper cites Cares: A comprehensive benchmark of trustworthiness in medical vision language models,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Cares: A comprehensive benchmark of trustworthiness in medical vision language models,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.772238Z

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This paper cites RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR

Reference 47

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Observation 5625ea7e-e5ec-4ca4-860b-132ac33014a8 · outbound

This paper cites Learn- ing missing modal electronic health records with unified multi-modal data embedding and modality-aware attention,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Learn- ing missing modal electronic health records with unified multi-modal data embedding and modality-aware attention,

Reference 48

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This paper cites M3care: Learning with missing modalities in multimodal healthcare data,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records M3care: Learning with missing modalities in multimodal healthcare data,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.717337Z

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Observation e97f3fee-7aa0-489a-a062-a9da1ded3adf · outbound

This paper cites Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review,.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:50:15.680999Z

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This paper cites Rethinking Interpretability in the Era of Large Language Models.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Rethinking Interpretability in the Era of Large Language Models

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This paper cites Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey.

Benchmarking Foundation Models with Multimodal Public Electronic Health Records Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey

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Resolution: unresolved; no resolver link, observed 2026-08-06T15:50:15.335365Z

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