Enhancing chatbot performance for imaging recommendations: Leveraging GPT-4 and context-awareness for trustworthy clinical guidance.

Purpose: To investigate if GPT-4 improves the accuracy, consistency, and trustworthiness of a context-aware chatbot to provide personalized imaging recommendations from American College of Radiology (ACR) appropriateness criteria documents using semantic similarity processing: In addition, we sought to enable auditability of the output by revealing the information source the decision relies on.

Material And Methods: We refined an existing chatbot that incorporated specialized knowledge of the ACR guidelines by upgrading GPT-3.5-Turbo to its successor GPT-4 by OpenAI, using the latest version of LlamaIndex, and improving the prompting strategy.

Deep learning reconstructed T2-weighted Dixon imaging of the spine: Impact on acquisition time and image quality.

Purpose: To assess the image quality and impact on acquisition time of a novel deep learning based T2 Dixon sequence (T2) of the spine.

Methods: This prospective, single center study included n = 44 consecutive patients with a clinical indication for lumbar MRI at our university radiology department between September 2022 and March 2023. MRI examinations were performed on 1.

A retrieval-augmented chatbot based on GPT-4 provides appropriate differential diagnosis in gastrointestinal radiology: a proof of concept study.

Background: We investigated the potential of an imaging-aware GPT-4-based chatbot in providing diagnoses based on imaging descriptions of abdominal pathologies.

Methods: Utilizing zero-shot learning via the LlamaIndex framework, GPT-4 was enhanced using the 96 documents from the Radiographics Top 10 Reading List on gastrointestinal imaging, creating a gastrointestinal imaging-aware chatbot (GIA-CB). To assess its diagnostic capability, 50 cases on a variety of abdominal pathologies were created, comprising radiological findings in fluoroscopy, MRI, and CT.

Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis.

Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedics due to its rising prevalence. Lower extremity misalignment, pivotal in knee injury etiology and management, necessitates comprehensive mechanical alignment evaluation via frequently-requested weight-bearing long leg radiographs (LLR). Despite LLR's routine use, current analysis techniques are error-prone and time-consuming.

Effective management of recurrent Doege-Potter syndrome with somatostatin-analogues: A case report.

Background: Doege-Potter syndrome is defined as paraneoplastic hypoinsulinemic hypoglycemia associated with a benign or malignant solitary fibrous tumor frequently located in pleural, but also extrapleural sites. Hypoglycemia can be attributed to paraneoplastic secretion of "Big-IGF-II," a precursor of Insulin-like growth factor-II. This prohormone aberrantly binds to and activates insulin receptors, with consecutive initiation of common insulin actions such as inhibition of gluconeogenesis, activation of glycolysis and stimulation of cellular glucose uptake culminating in recurrent tumor-induced hypoglycemic episodes.

Improving the use of LLMs in radiology through prompt engineering: from precision prompts to zero-shot learning.

Purpose: Large language models (LLMs) such as ChatGPT have shown significant potential in radiology. Their effectiveness often depends on prompt engineering, which optimizes the interaction with the chatbot for accurate results. Here, we highlight the critical role of prompt engineering in tailoring the LLMs' responses to specific medical tasks.

A deep learning approach for projection and body-side classification in musculoskeletal radiographs.

Background: The growing prevalence of musculoskeletal diseases increases radiologic workload, highlighting the need for optimized workflow management and automated metadata classification systems. We developed a large-scale, well-characterized dataset of musculoskeletal radiographs and trained deep learning neural networks to classify radiographic projection and body side.

Methods: In this IRB-approved retrospective single-center study, a dataset of musculoskeletal radiographs from 2011 to 2019 was retrieved and manually labeled for one of 45 possible radiographic projections and the depicted body side.

AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice.

Objectives: To aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selected representative custom-trained or commercial classification, detection and segmentation models for fracture detection on musculoskeletal radiographs of the distal radius by aligning their outputs.

Design And Setting: This single-centre retrospective study was conducted on a random subset of emergency department radiographs from 2008 to 2018 of the distal radius in Germany.

Materials And Methods: An image set was created to be compatible with training and testing classification and segmentation models by annotating examinations for fractures and overlaying fracture masks, if applicable.

A content-aware chatbot based on GPT 4 provides trustworthy recommendations for Cone-Beam CT guidelines in dental imaging.

Objectives: To develop a content-aware chatbot based on GPT-3.5-Turbo and GPT-4 with specialized knowledge on the German S2 Cone-Beam CT (CBCT) dental imaging guideline and to compare the performance against humans.

Methods: The LlamaIndex software library was used to integrate the guideline context into the chatbots.

Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study.

In magnetic resonance imaging (MRI), the perception of substandard image quality may prompt repetition of the respective image acquisition protocol. Subsequently selecting the preferred high-quality image data from a series of acquisitions can be challenging. An automated workflow may facilitate and improve this selection.

Photon-counting computed tomography - clinical application in oncological, cardiovascular, and pediatric radiology.

Background: Photon-counting detector computed tomography (PCD-CT) is a promising new technology with the potential to fundamentally change workflows in the daily routine and provide new quantitative imaging information to improve clinical decision-making and patient management.

Method: The contents of this review are based on an unrestricted literature search of PubMed and Google Scholar using the search terms "photon-counting CT", "photon-counting detector", "spectral CT", "computed tomography" as well as on the authors' own experience.

Results: The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCD-CT allows for the counting of every single photon at the detector level.

Performance of ChatGPT, human radiologists, and context-aware ChatGPT in identifying AO codes from radiology reports.

While radiologists can describe a fracture's morphology and complexity with ease, the translation into classification systems such as the Arbeitsgemeinschaft Osteosynthesefragen (AO) Fracture and Dislocation Classification Compendium is more challenging. We tested the performance of generic chatbots and chatbots aware of specific knowledge of the AO classification provided by a vector-index and compared it to human readers. In the 100 radiological reports we created based on random AO codes, chatbots provided AO codes significantly faster than humans (mean 3.

A Context-based Chatbot Surpasses Trained Radiologists and Generic ChatGPT in Following the ACR Appropriateness Guidelines.

Background Radiological imaging guidelines are crucial for accurate diagnosis and optimal patient care as they result in standardized decisions and thus reduce inappropriate imaging studies. Purpose In the present study, we investigated the potential to support clinical decision-making using an interactive chatbot designed to provide personalized imaging recommendations from American College of Radiology (ACR) appropriateness criteria documents using semantic similarity processing. Methods We utilized 209 ACR appropriateness criteria documents as specialized knowledge base and employed LlamaIndex, a framework that allows to connect large language models with external data, and the ChatGPT 3.

Automated detection of cephalometric landmarks using deep neural patchworks.

Objectives: This study evaluated the accuracy of deep neural patchworks (DNPs), a deep learning-based segmentation framework, for automated identification of 60 cephalometric landmarks (bone-, soft tissue- and tooth-landmarks) on CT scans. The aim was to determine whether DNP could be used for routine three-dimensional cephalometric analysis in diagnostics and treatment planning in orthognathic surgery and orthodontics.

Methods: Full skull CT scans of 30 adult patients (18 female, 12 male, mean age 35.

RF-induced heating of interventional devices at 23.66 MHz.

Objective: Low-field MRI systems are expected to cause less RF heating in conventional interventional devices due to lower Larmor frequency. We systematically evaluate RF-induced heating of commonly used intravascular devices at the Larmor frequency of a 0.55 T system (23.

Deep learning segmentation results in precise delineation of the putamen in multiple system atrophy.

Objectives: The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability to delineate the putamen in multiple system atrophy (MSA), Parkinson's disease (PD), and healthy controls.

Methods: We retrospectively included patients with MSA and PD as well as healthy controls.

Photon-Counting Computed Tomography - Basic Principles, Potenzial Benefits, and Initial Clinical Experience.

Background: Photon-counting computed tomography (PCCT) is a promising new technology with the potential to fundamentally change today's workflows in the daily routine and to provide new quantitative imaging information to improve clinical decision-making and patient management.

Method: The content of this review is based on an unrestricted literature search on PubMed and Google Scholar using the search terms "Photon-Counting CT", "Photon-Counting detector", "spectral CT", "Computed Tomography" as well as on the authors' experience.

Results: The fundamental difference with respect to the currently established energy-integrating CT detectors is that PCCT allows counting of every single photon at the detector level.

Where Position Matters-Deep-Learning-Driven Normalization and Coregistration of Computed Tomography in the Postoperative Analysis of Deep Brain Stimulation.

Introduction: Recent developments in the postoperative evaluation of deep brain stimulation surgery on the group level warrant the detection of achieved electrode positions based on postoperative imaging. Computed tomography (CT) is a frequently used imaging modality, but because of its idiosyncrasies (high spatial accuracy at low soft tissue resolution), it has not been sufficient for the parallel determination of electrode position and details of the surrounding brain anatomy (nuclei). The common solution is rigid fusion of CT images and magnetic resonance (MR) images, which have much better soft tissue contrast and allow accurate normalization into template spaces.

Analysis of the accuracy of computer-assisted DCIA flap mandibular reconstruction applying a novel approach based on geometric morphometrics.

Background: This study evaluated the accuracy of computer-assisted surgery (CAS)-driven DCIA (deep circumflex iliac artery) flap mandibular reconstruction by traditional morphometric methods and geometric morphometric methods (GMM).

Methods: Reconstruction accuracy was evaluated by measuring distances and angles between bilateral anatomical landmarks. Additionally, the average length of displacements vectors between landmarks was computed to evaluate factors assumed to influence reconstruction accuracy.

Impact of radiographic field-of-view volume on alignment accuracy during virtual implant planning: A noninterventional retrospective pilot study.

Objective: To evaluate the impact of reducing the radiographic field of view (FOV) on the trueness and precision of the alignment between cone beam computed tomography (CBCT) and intraoral scanning data for implant planning.

Materials And Methods: Fifteen participants presenting with one of three clinical scenarios: single tooth loss (ST, n = 5), multiple missing teeth (MT, n = 5) and presence of radiographic artifacts (AR, n = 5) were included. CBCT volumes covering the full arch (FA) were reduced to the quadrant (Q) or the adjacent tooth/teeth (A).

Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks.

Purpose: Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans.

Methods: The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks.

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

Objectives: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists.

Methods: In 880 patients (age 33.1 ± 19.