ChatGPT and assistive AI in structured radiology reporting: A systematic review.
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Summary
ChatGPT and assistive AI have significant potential to transform radiology reporting, enhancing accuracy and standardization while optimizing healthcare resources, according to a comprehensive search of MEDLINE and Embase from inception through May 2024.
- Type
- article
- Published
- 2024-07-01
- Cited by
- 48
- References
- 41
- OpenAlex
- https://openalex.org/W39004580
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:271105306
Keywords
Philosophy, Political science
References
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- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development
- Chatbot for the Return of Positive Genetic Screening Results for Hereditary Cancer Syndromes: Prompt Engineering Project
- Exploring Turkish equivalents of terms for musculoskeletal radiology: insights for a standardized terminology
- Automated generation of echocardiography reports using artificial intelligence: a novel approach to streamlining cardiovascular diagnostics
- Leveraging GPT-4 enables patient comprehension of radiology reports.
- Evolving and Novel Applications of Artificial Intelligence in Cancer Imaging
- Large language models for efficient whole-organ MRI score-based reports and categorization in knee osteoarthritis
- Artificial Intelligence Language Models to Translate Professional Radiology Mammography Reports Into Plain Language - Impact on Interpretability and Perception by Patients.
- Performance Analysis of Large Language Models in Radiology and Histopathology Reporting: From Diagnostic Support to Patient Communication
- Interpreting BI-RADS-Free Breast MRI Reports Using a Large Language Model: Automated BI-RADS Classification From Narrative Reports Using ChatGPT.
- LLaVA-Assisted Prompt Engineering for Liver X-Ray Report Analysis
- Improving diagnosis: advances in radiology
- Enhancing Large Vision Language Models for Liver CT Scans in Medical Reports
- Artificial Intelligence for CT and MRI Protocoling: A Meta-Analysis of Traditional Machine Learning, BERT, and Large Language Models.
- Use of Large Language Models on Radiology Reports: A Scoping Review.
- RoI-MedCap: Region of Interest-Based Medical Image Captioning with Multi-Stream Connector
- Evaluating Multimodal Large Language Model (LLM) (Generative Pre-trained Transformer 5 (GPT-5)) for Meniscal Tear Detection on Knee Magnetic Resonance Imaging (MRI): A Pilot Study
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