Pre-training in Medical Data: A Survey

Qiu, Yixuan; Lin, Feng; Chen, Weitong; Xu, Miao

doi:10.1007/s11633-022-1382-8

Cited by 30Domain Adaptation and Few-Shot LearningMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Pre-training in Medical Data: A Survey

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Qiu, Yixuan, Lin, Feng, Chen, Weitong, Xu, Miao·2023·Machine Intelligence Research

DOI 10.1007/s11633-022-1382-8PMC PMC9942039

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Abstract

Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods′ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.

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Pretrained models followed by fine-tuning on target clinical data enable efficient adaptation to specialized clinical tasks even with limited data from the specific patient cohort.
“This approach enables efficient adaptation to specialized clinical tasks, even with limited data from the specific patient cohort [77].”
From Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review (2026) · cites this paper as [77]
The paper’s opening passage (section and page not provided) says pre-training improves performance in data-limited scenarios and is used across medical settings. It does not establish the more specific claim that fine-tuning enables efficient adaptation with limited data from a particular patient cohort. (LLM, conf=0.79)
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Authors: Qiu, Y., Lin, F., Chen, W., & Xu, M.
Affiliations: The University of Queensland (AU)
The University of Adelaide (AU)
Corresponding author: Chen, Weitong
ORCID: Qiu, Yixuan
Chen, Weitong
Xu, Miao
Venue: Machine Intelligence Research
Publisher: Springer Science and Business Media LLC
Published: 2023-02-21
Volume: 20
Issue: 2
Pages: 147-179
ISSN: 1476-8186 (ISSN-L)
2731-538X
2731-5398
DOI: 10.1007/s11633-022-1382-8
PMC: PMC9942039
OpenAlex: W4321437302
Cited by: 30
References: 235
Citations by year: 2026: 6 · 2025: 10 · 2024: 8 · 2023: 6
FWCI: 2.83
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Machine Intelligence Research (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
Funders: University of Adelaide
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: pre-training, health data, deep neural network, electronic health records, multimodal medical data, benchmarking, transfer learning

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