Research graph
References from ChatGPT-4V prompt: A tool to enhance junior radiologists' diagnostic capabilities in cystic renal masses to senior-level accuracy. Local targets link to admitted publications; unresolved targets remain external evidence.
Prevalence of renal cysts and association with risk factors in a general population: an MRI-based study
10.1007/s00261-018-1565-5 · 2018 · External reference
Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment
10.1148/radiol.2019182646 · 2019 · External reference
An update of the Bosniak renal cyst classification system
10.1016/j.urology.2005.04.003 · 2005 · External reference
The current radiological approach to renal cysts
10.1148/radiology.158.1.3510019 · 1986 · External reference
EFSUMB 2020 proposal for a contrast-enhanced ultrasound-adapted bosniak cyst categorization – position statement
10.1055/a-1300-1727 · 2021 · External reference
The added value of contrast-enhanced ultrasound in evaluation of indeterminate small solid renal masses and risk stratification of cystic renal lesions
10.1007/s00330-021-07964-0 · 2021 · External reference
Ultrasound-based “CEUS-Bosniak”classification for cystic renal lesions: an 8-year clinical experience
10.1007/s00345-022-04094-0 · 2023 · External reference
10.1002/jum.16328
10.1002/jum.16328 · External reference
Intra- and Interobserver Study Investigating the Adapted EFSUMB Bosniak Cyst Categorization Proposed for Contrast-Enhanced Ultrasound (CEUS) in 2020
2023 · External reference
Contrast-enhanced US Bosniak Classification: intra- and inter-rater agreement, confounding features, and diagnostic performance
10.1186/s13244-024-01858-7 · 2024 · External reference
Collaborative enhancement of consistency and accuracy in US diagnosis of thyroid nodules using large language models
10.1148/radiol.232255 · 2024 · External reference
Large Language Models for Automated Synoptic Reports and Resectability Categorization in Pancreatic Cancer
10.1148/radiol.233117 · 2024 · External reference
Performance of GPT-4 with vision on text- and image-based ACR diagnostic radiology in-training examination Questions
10.1148/radiol.240153 · 2024 · External reference
Capability of multimodal large language models to interpret pediatric radiological images
2024 · External reference
Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs
10.1038/s41746-024-01029-4 · 2024 · External reference
Evaluating the use of large language models to provide clinical recommendations in the Emergency Department
10.1038/s41467-024-52415-1 · 2024 · External reference
Diagnostic performance of multimodal large language models in radiological quiz cases: the effects of prompt engineering and input conditions
10.14366/usg.25012 · 2025 · External reference
10.1111/j.2517-6161.1995.tb02031.x
10.1111/j.2517-6161.1995.tb02031.x · External reference
Comparison of four diagnostic guidelines for hepatocellular carcinoma using gadoxetic acid–enhanced Liver MRI
10.1148/radiol.233114 · 2024 · External reference
10.1348/000711006x126600
10.1348/000711006x126600 · External reference
10.2307/2529310
10.2307/2529310 · External reference
Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases
10.1148/radiol.240273 · 2024 · External reference
Prompt engineering for healthcare: methodologies and applications
2024 · External reference
Large language model capabilities in perioperative risk prediction and prognostication
2024 · External reference
10.1007/978-1-4419-8126-4
10.1007/978-1-4419-8126-4 · 2011 · External reference
10.1023/a:1022193728205
10.1023/a:1022193728205 · External reference
Prompt Engineering in Clinical Practice: Tutorial for Clinicians
10.2196/72644 · 2025 · External reference
10.1038/s41591-018-0316-z
10.1038/s41591-018-0316-z · External reference
Bosniak Classification of Cystic Renal Masses Version 2019: proportion of malignancy by class and subclass—systematic review and meta-analysis
10.2214/ajr.24.32342 · 2025 · External reference
Artificial intelligence and the clinical world: a view from the front line
10.5694/mja2.50025 · 2019 · External reference
A survey of methods for explaining black box models
10.1145/3236009 · 2019 · External reference
