A review on generative AI models for synthetic medical text, time series, and longitudinal data
Abstract This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modalit...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-024-01409-w |
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| _version_ | 1849326890827907072 |
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| author | Mohammad Loni Fatemeh Poursalim Mehdi Asadi Arash Gharehbaghi |
| author_facet | Mohammad Loni Fatemeh Poursalim Mehdi Asadi Arash Gharehbaghi |
| author_sort | Mohammad Loni |
| collection | DOAJ |
| description | Abstract This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modality, and research methodology of the reviewed studies, uncover the importance and the scope of the topic for the digital medicine context. In total, 52 publications met the eligibility criteria for generating medical time series (22), longitudinal data (17), and medical text (13). Privacy preservation was found to be the main research objective of the studied papers, along with class imbalance, data scarcity, and data imputation as the other objectives. The adversarial network-based, probabilistic, and large language models exhibited superiority for generating synthetic longitudinal data, time series, and medical texts, respectively. Finding a reliable performance measure to quantify SHR re-identification risk is the major research gap of the topic. |
| format | Article |
| id | doaj-art-e51c3552fd5745a88a5748a90a4cd03a |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-e51c3552fd5745a88a5748a90a4cd03a2025-08-20T03:48:02ZengNature Portfolionpj Digital Medicine2398-63522025-05-018111010.1038/s41746-024-01409-wA review on generative AI models for synthetic medical text, time series, and longitudinal dataMohammad Loni0Fatemeh Poursalim1Mehdi Asadi2Arash Gharehbaghi3School of Innovation, Design and Engineering, Mälardalen UniversityServicehälsan Familjeläkare i Västerås ABICT Department, Turku University of Applied SciencesDepartment of Biomedical Engineering, Linköping UniversityAbstract This paper presents the results of a novel scoping review on the practical models for generating three different types of synthetic health records (SHRs): medical text, time series, and longitudinal data. The innovative aspects of the review, which incorporate study objectives, data modality, and research methodology of the reviewed studies, uncover the importance and the scope of the topic for the digital medicine context. In total, 52 publications met the eligibility criteria for generating medical time series (22), longitudinal data (17), and medical text (13). Privacy preservation was found to be the main research objective of the studied papers, along with class imbalance, data scarcity, and data imputation as the other objectives. The adversarial network-based, probabilistic, and large language models exhibited superiority for generating synthetic longitudinal data, time series, and medical texts, respectively. Finding a reliable performance measure to quantify SHR re-identification risk is the major research gap of the topic.https://doi.org/10.1038/s41746-024-01409-w |
| spellingShingle | Mohammad Loni Fatemeh Poursalim Mehdi Asadi Arash Gharehbaghi A review on generative AI models for synthetic medical text, time series, and longitudinal data npj Digital Medicine |
| title | A review on generative AI models for synthetic medical text, time series, and longitudinal data |
| title_full | A review on generative AI models for synthetic medical text, time series, and longitudinal data |
| title_fullStr | A review on generative AI models for synthetic medical text, time series, and longitudinal data |
| title_full_unstemmed | A review on generative AI models for synthetic medical text, time series, and longitudinal data |
| title_short | A review on generative AI models for synthetic medical text, time series, and longitudinal data |
| title_sort | review on generative ai models for synthetic medical text time series and longitudinal data |
| url | https://doi.org/10.1038/s41746-024-01409-w |
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