ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2022
Survey on natural language processing in medical image analysis.
Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Significant Decline in HCV-Related Mortality From Liver Cirrhosis and Chronic Liver Disease (2015-2022): A Sentinel Center Study.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Task-Specific Transformer-Based Language Models in Health Care: Scoping Review.JMIR medical informatics · 2024Article
- Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review.Pain and therapy · 2024Review
- Differentiating ChatGPT-Generated and Human-Written Medical Texts: Quantitative Study.JMIR medical education · 2023Article
- A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2023Review
- Surviving ChatGPT in healthcare.Frontiers in radiology · 2023Review
- Evaluating large language models on a highly-specialized topic, radiation oncology physics.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Recent advancement in natural language processing (NLP) and medical imaging empowers the wide applicability of deep learning models. These developments have increased not only data understanding, but also knowledge of state-of-the-art architectures and their real-world potentials. Medical imaging researchers have recognized the limitations of only targeting images, as well as the importance of integrating multimodal inputs into medical image analysis. The lack of comprehensive surveys of the current literature, however, impedes the progress of this domain. Existing research perspectives, as well as the architectures, tasks, datasets, and performance measures examined in the present literature, are reviewed in this work, and we also provide a brief description of possible future directions in the field, aiming to provide researchers and healthcare professionals with a detailed summary of existing academic research and to provide rational insights to facilitate future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.