ReviewBiomedical engineering online2023
A survey on automatic generation of medical imaging reports based on deep learning.
Review in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancement in medical report generation: current practices, challenges, and future directions.Medical & biological engineering & computing · 2025Pooled it
- MedFusionT5: Cross-Modal Attention Boosts Semantic Quality and Reduces Hallucinations in Dental AI.International dental journal · 2026Article
- Article
- Construction of a public health emergency information system framework: A case study of Zhuhai city, China.PloS one · 2026Article
- Hybrid GAN-LSTM framework for diabetic foot ulcer image synthesis and automated diagnosis.Frontiers in medicine · 2026Article
- A deep learning based automatic report generator for retinal optical coherence tomography images.NPJ digital medicine · 2025Article
- Multi-view contrastive learning and symptom extraction insights for medical report generation.Scientific reports · 2025Article
- Intelligent health model for medical imaging to guide laymen using neural cellular automata.Scientific reports · 2025Article
- Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs.IEEE journal of translational engineering in health and medicine · 2025Article
- AI-Powered Synthesis of Structured Multimodal Breast Ultrasound Reports Integrating Radiologist Annotations and Deep Learning Analysis.Bioengineering (Basel, Switzerland) · 2024Article
- Preliminary experiments on interpretable ChatGPT-assisted diagnosis for breast ultrasound radiologists.Quantitative imaging in medicine and surgery · 2024Article
- Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for AI-generated Radiology Reports.Proceedings. IEEE International Conference on Healthcare Informatics · 2024Article
- Article
- Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports.ArXiv · 2024Article
- Research and progress on the mechanism of lower urinary tract neuromodulation: a literature review.PeerJ · 2024Review
- Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Vision-language models for medical report generation and visual question answering: a review.Frontiers in artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Recent advances in deep learning have shown great potential for the automatic generation of medical imaging reports. Deep learning techniques, inspired by image captioning, have made significant progress in the field of diagnostic report generation. This paper provides a comprehensive overview of recent research efforts in deep learning-based medical imaging report generation and proposes future directions in this field. First, we summarize and analyze the data set, architecture, application, and evaluation of deep learning-based medical imaging report generation. Specially, we survey the deep learning architectures used in diagnostic report generation, including hierarchical RNN-based frameworks, attention-based frameworks, and reinforcement learning-based frameworks. In addition, we identify potential challenges and suggest future research directions to support clinical applications and decision-making using medical imaging report generation systems.
Indexed as
Identifiers
What OpenQuestion holds
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.