Paper Citation Record · LEDGER

Generation of Synthetic Clinical Text: A Systematic Review

As of 8 August 2026, this Paper Citation Record lists 100 of 256 outbound references and 3 inbound Pith citation observations for arXiv:2507.18451.

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

pith.paper-citation-record.v1
2507.18451 v1

Coverage vector

measured 100 of 256 reference resolution

Typed states for the displayed outbound observations.

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One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:41:10.816907Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T11:48:15.135068Z

Reference resolution

100 of 256 outbound references displayed

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External citation measurements

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Outbound references

Observation b97fce3d-2070-414e-be71-ce673ff2b8fd · outbound

This paper cites Should free-text data in electronic medical records be shared for research? a citizens’ jury study in the UK.

Generation of Synthetic Clinical Text: A Systematic Review Should free-text data in electronic medical records be shared for research? a citizens’ jury study in the UK

Reference 1

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Observation 5fc84f31-89b2-4c04-92e8-34e92a72e8b7 · outbound

This paper cites Synonym-based text generation in restructuring imbalanced dataset for deep learning models.

Generation of Synthetic Clinical Text: A Systematic Review Synonym-based text generation in restructuring imbalanced dataset for deep learning models

Reference 2

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Observation 0bccb547-8173-44f5-8efb-96a2c81b05d2 · outbound

This paper cites Generating natural language adversarial examples on a large scale with generative models, 2020.

Generation of Synthetic Clinical Text: A Systematic Review Generating natural language adversarial examples on a large scale with generative models, 2020

Reference 3

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Observation c1888175-b96d-42d2-971e-6b8a3887f1f9 · outbound

This paper cites Synthetic data generation: State of the art in health care domain.

Generation of Synthetic Clinical Text: A Systematic Review Synthetic data generation: State of the art in health care domain

Reference 4

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This paper cites Synthetic data generation for tabular health records: A systematic review.

Generation of Synthetic Clinical Text: A Systematic Review Synthetic data generation for tabular health records: A systematic review

Reference 5

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This paper cites Deep generative models for synthetic data: A survey.

Generation of Synthetic Clinical Text: A Systematic Review Deep generative models for synthetic data: A survey

Reference 6

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Observation d24daec3-d3cc-4d42-b801-eba00ea46a42 · outbound

This paper cites A systematic literature review: deep learn- ing techniques for synthetic medical image generation and their applications in radiotherapy.

Generation of Synthetic Clinical Text: A Systematic Review A systematic literature review: deep learn- ing techniques for synthetic medical image generation and their applications in radiotherapy

Reference 7

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Observation 2875790c-4b8c-4bc1-9d9e-e67e35ca31f1 · outbound

This paper cites Ghosheh, Jin Li, and Tingting Zhu.

Generation of Synthetic Clinical Text: A Systematic Review Ghosheh, Jin Li, and Tingting Zhu

Reference 8

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Observation 2fc09739-a793-4d90-aa59-2e09e98f360f · outbound

This paper cites Evalua- tion of synthetic electronic health records: A systematic review and experimental assessment.

Generation of Synthetic Clinical Text: A Systematic Review Evalua- tion of synthetic electronic health records: A systematic review and experimental assessment

Reference 9

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This paper cites Pezoulas, Dimitrios I.

Generation of Synthetic Clinical Text: A Systematic Review Pezoulas, Dimitrios I

Reference 10

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Observation 418bdd15-ab01-4952-8fb9-a758c974f76e · outbound

This paper cites Primer on generative artificial intelligence and large language models in medical imaging.

Generation of Synthetic Clinical Text: A Systematic Review Primer on generative artificial intelligence and large language models in medical imaging

Reference 11

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This paper cites Generative ai for synthetic 14 data across multiple medical modalities: A systematic review of recent developments and challenges.

Generation of Synthetic Clinical Text: A Systematic Review Generative ai for synthetic 14 data across multiple medical modalities: A systematic review of recent developments and challenges

Reference 12

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Observation 6a53aac6-8ac3-44af-80d1-23d4c8a1d7dd · outbound

This paper cites Five steps to conducting a systematic review, 2003.

Generation of Synthetic Clinical Text: A Systematic Review Five steps to conducting a systematic review, 2003

Reference 13

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This paper cites Systematic reviews and meta-analyses, February 2011.

Generation of Synthetic Clinical Text: A Systematic Review Systematic reviews and meta-analyses, February 2011

Reference 14

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This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Generation of Synthetic Clinical Text: A Systematic Review Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 15

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This paper cites Automatic generation of electronic medical record based on gpt2 model.

Generation of Synthetic Clinical Text: A Systematic Review Automatic generation of electronic medical record based on gpt2 model

Reference 16

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This paper cites Cmed-gpt: Prompt tuning for entity-aware chinese medical dialogue generation, 2023.

Generation of Synthetic Clinical Text: A Systematic Review Cmed-gpt: Prompt tuning for entity-aware chinese medical dialogue generation, 2023

Reference 17

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Observation 4663bb29-53e9-40f0-93f4-fcc9995764e5 · outbound

This paper cites Generation of synthetic elec- tronic medical record text.

Generation of Synthetic Clinical Text: A Systematic Review Generation of synthetic elec- tronic medical record text

Reference 18

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This paper cites Medconqa: Medical conversational question answering system based on knowledge graphs.

Generation of Synthetic Clinical Text: A Systematic Review Medconqa: Medical conversational question answering system based on knowledge graphs

Reference 19

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Observation f1fe1192-53e8-459a-9b5b-2e8d7d222ab1 · outbound

This paper cites A method for generating synthetic electronic medical record text.

Generation of Synthetic Clinical Text: A Systematic Review A method for generating synthetic electronic medical record text

Reference 20

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This paper cites Research on text generation of medical intelligent question and answer based on bi-lstm and neural network technology.

Generation of Synthetic Clinical Text: A Systematic Review Research on text generation of medical intelligent question and answer based on bi-lstm and neural network technology

Reference 21

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Generation of Synthetic Clinical Text: A Systematic Review Schapranow

Reference 22

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This paper cites GRASCCO — the first publicly shareable, multiply-alienated german clinical text corpus.

Generation of Synthetic Clinical Text: A Systematic Review GRASCCO — the first publicly shareable, multiply-alienated german clinical text corpus

Reference 23

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This paper cites Sharing copies of synthetic clinical corpora without physical distribution — a case study to get around IPRs and privacy constraints fea- turing the German JSYNCC corpus.

Generation of Synthetic Clinical Text: A Systematic Review Sharing copies of synthetic clinical corpora without physical distribution — a case study to get around IPRs and privacy constraints fea- turing the German JSYNCC corpus

Reference 24

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This paper cites Reinforcement 15 learning with imbalanced dataset for data-to-text medical report generation.

Generation of Synthetic Clinical Text: A Systematic Review Reinforcement 15 learning with imbalanced dataset for data-to-text medical report generation

Reference 25

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Generation of Synthetic Clinical Text: A Systematic Review A practical and universal framework for generating publicly available medical notes of authentic quality via the power of crowds

Reference 26

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Generation of Synthetic Clinical Text: A Systematic Review Cognitive assessment of japanese older adults with text data augmentation

Reference 27

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Observation 81a85a15-ce59-47c8-aa7e-1be34e9d440f · outbound

This paper cites Instruction-guided deidentification with synthetic test cases for norwegian clinical text.

Generation of Synthetic Clinical Text: A Systematic Review Instruction-guided deidentification with synthetic test cases for norwegian clinical text

Reference 28

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Generation of Synthetic Clinical Text: A Systematic Review Iterative development of family history annotation guidelines using a synthetic corpus of clinical text

Reference 29

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Generation of Synthetic Clinical Text: A Systematic Review Brekke, Taraka Rama, Ildik´ o Pil´ an, Øystein Nytrø, and Lilja Øvrelid

Reference 30

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Generation of Synthetic Clinical Text: A Systematic Review Can synthetic text help clinical named entity recognition? a study of electronic health records in French

Reference 31

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Generation of Synthetic Clinical Text: A Systematic Review Generating synthetic training data for supervised de-identification of electronic health records

Reference 32

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Generation of Synthetic Clinical Text: A Systematic Review Synthetic Arabic medical dialogues using advanced multi-agent LLM techniques

Reference 33

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Generation of Synthetic Clinical Text: A Systematic Review AI-driven approach for automatic synthetic patient status corpus generation

Reference 34

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Generation of Synthetic Clinical Text: A Systematic Review Johnson, Tom J

Reference 35

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Generation of Synthetic Clinical Text: A Systematic Review Unresolved cited work

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Generation of Synthetic Clinical Text: A Systematic Review Kohli, Marc B

Reference 37

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Generation of Synthetic Clinical Text: A Systematic Review Annotated dataset creation through general purpose language models for non-english medical nlp, 2022

Reference 38

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Generation of Synthetic Clinical Text: A Systematic Review Joyce, Niall Taylor, Alejo Nevado-Holgado, Andrea Cipriani, and An- drey Kormilitzin

Reference 39

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Generation of Synthetic Clinical Text: A Systematic Review Does synthetic data generation of llms help clinical text mining?, 2023

Reference 40

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Generation of Synthetic Clinical Text: A Systematic Review Two directions for clinical data generation with large language models: Data-to-label and label-to-data

Reference 41

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This paper cites An adversorial approach to enable re-use of machine learning models and collaborative research efforts using synthetic unstructured free-text medical data, August 2019.

Generation of Synthetic Clinical Text: A Systematic Review An adversorial approach to enable re-use of machine learning models and collaborative research efforts using synthetic unstructured free-text medical data, August 2019

Reference 42

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Generation of Synthetic Clinical Text: A Systematic Review Are synthetic clinical notes useful for real natural language processing tasks: A case study on clinical entity recognition

Reference 43

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Generation of Synthetic Clinical Text: A Systematic Review Cardinal, Angus Roberts, Robert Stewart, and Sumithra Velupillai

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Generation of Synthetic Clinical Text: A Systematic Review Generative adversarial networks for creating synthetic free-text medical data: A proposal for collaborative research and re-use of machine learning models, May 2021

Reference 45

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Generation of Synthetic Clinical Text: A Systematic Review Unresolved cited work

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Generation of Synthetic Clinical Text: A Systematic Review Transforming health- care documentation: harnessing the potential of ai to generate discharge summaries

Reference 47

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Generation of Synthetic Clinical Text: A Systematic Review Transformer models trained on mimic-iii to generate synthetic patient notes, 2020

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Generation of Synthetic Clinical Text: A Systematic Review Identifying and aligning medical claims made on social media with medical evidence, 2024

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Generation of Synthetic Clinical Text: A Systematic Review Medically aware gpt-3 as a data generator for medical dialogue summarization, 2021

Reference 50

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Generation of Synthetic Clinical Text: A Systematic Review Generaci´ on masiva de historias cl ´ ınicas sint´ eticas con chatgpt: un ejemplo en fractura de cadera

Reference 51

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Generation of Synthetic Clinical Text: A Systematic Review Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes

Reference 52

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Generation of Synthetic Clinical Text: A Systematic Review How to use language models for synthetic text generation in cerebrovascular disease-specific medical reports

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Generation of Synthetic Clinical Text: A Systematic Review Smit h, Nima PourNejatian, Anthony B

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Generation of Synthetic Clinical Text: A Systematic Review Towards automatic generation of shareable syn- thetic clinical notes using neural language models

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Generation of Synthetic Clinical Text: A Systematic Review Differentially private medical texts generation using generative neural networks

Reference 57

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Generation of Synthetic Clinical Text: A Systematic Review Knowledge-infused prompting: Assessing and advancing clinical text data generation with large language models

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Generation of Synthetic Clinical Text: A Systematic Review SynthNotes: A gen- erator framework for high-volume, high-fidelity synthetic mental health notes

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Generation of Synthetic Clinical Text: A Systematic Review Aci-bench: a novel ambient clinical intelligence dataset for benchmarking automatic visit note generation

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Generation of Synthetic Clinical Text: A Systematic Review An empirical study of clinical note generation from doctor-patient encounters

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Generation of Synthetic Clinical Text: A Systematic Review Feng, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman, and Eduard Hovy

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Generation of Synthetic Clinical Text: A Systematic Review Synth-sbdh: A synthetic dataset of social and behavioral determinants of health for clinical text, 2024

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Generation of Synthetic Clinical Text: A Systematic Review Ehr-ds-qa: A synthetic qa dataset derived from medical dis- charge summaries for enhanced medical information retrieval systems, 2024

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Generation of Synthetic Clinical Text: A Systematic Review coherent data set

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Generation of Synthetic Clinical Text: A Systematic Review Generating continuous representations of medical texts, 2018

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Generation of Synthetic Clinical Text: A Systematic Review On the automatic generation of medical imaging reports

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Generation of Synthetic Clinical Text: A Systematic Review Generation of natural-language textual summaries from longitudinal clinical records

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Generation of Synthetic Clinical Text: A Systematic Review Enhancing clinical note gener- ation from doctor-patient conversations through semantic partition-oriented summarization

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Generation of Synthetic Clinical Text: A Systematic Review Enhancing clinical documentation with synthetic data: Leveraging generative models for improved accuracy

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Generation of Synthetic Clinical Text: A Systematic Review Medt2t: An adaptive pointer constrain generating method for a new medical text-to-table task

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Generation of Synthetic Clinical Text: A Systematic Review Li, Xiaodan Liang, Zhiting Hu, and Eric P

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Generation of Synthetic Clinical Text: A Systematic Review Implementation of gpt models for text generation in healthcare domain

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Generation of Synthetic Clinical Text: A Systematic Review Set to ordered text: Generating discharge instruc- tions from medical billing codes

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Generation of Synthetic Clinical Text: A Systematic Review Medical scientific table-to-text generation with human-in-the-loop under the data spar- sity constraint, 2022

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Generation of Synthetic Clinical Text: A Systematic Review Neural text generation in regulatory medical writing

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Generation of Synthetic Clinical Text: A Systematic Review Prott3: Protein-to-text generation for text-based protein understanding, 2024

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Generation of Synthetic Clinical Text: A Systematic Review Multi- modal understanding and generation for medical images and text via vision-language pre- training

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Generation of Synthetic Clinical Text: A Systematic Review Medm2g: Unifying medical multi-modal generation via cross-guided diffusion with visual invariant, 2024

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Generation of Synthetic Clinical Text: A Systematic Review Improving radiology report generation quality and diversity through reinforcement learning and text augmentation

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Generation of Synthetic Clinical Text: A Systematic Review Generat- ing explanations in medical question-answering by expectation maximization inference over evidence, 2023

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Generation of Synthetic Clinical Text: A Systematic Review A dictionary-based oversampling approach to clinical document classification on small and imbalanced dataset

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Generation of Synthetic Clinical Text: A Systematic Review A simple data augmentation method to im- prove the performance of named entity recognition models in medical domain

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Generation of Synthetic Clinical Text: A Systematic Review An exploratory study on pseudo-data generation in prescription and adverse drug reaction extraction, August 2019

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Generation of Synthetic Clinical Text: A Systematic Review An NLP-inspired data augmen- tation method for adverse event prediction using an imbalanced healthcare dataset

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Generation of Synthetic Clinical Text: A Systematic Review Exploring transformer text generation for medical dataset augmentation

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Generation of Synthetic Clinical Text: A Systematic Review Substituting clinical features using synthetic medical phrases: Medical text data augmenta- tion techniques

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Generation of Synthetic Clinical Text: A Systematic Review Textual data augmentation for patient out- comes prediction

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Generation of Synthetic Clinical Text: A Systematic Review UMLS-based data augmentation for natural language processing of clinical research literature

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Generation of Synthetic Clinical Text: A Systematic Review A new generative model for textual descriptions of medical images using transformers enhanced with convolutional neural networks

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Generating High Quality Synthetic Data for Dutch Medical Conversations Generation of Synthetic Clinical Text: A Systematic Review

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Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale Generation of Synthetic Clinical Text: A Systematic Review

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Observation 0002b0ae-e4e5-4bf3-b715-4ab5a980b54a · inbound

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