Michael Ingrisch

Abstract Objectives To assess the quality of simplified radiology reports generated with the large language model (LLM) ChatGPT and to discuss challenges and chances of ChatGPT-like LLMs for medical text simplification. Methods In this exploratory case study, a radiologist created three fictitious radiology reports which we simplified by prompting ChatGPT with “Explain this medical report to a child using simple language.” In a questionnaire, we tasked 15 radiologists to rate the quality of the simplified radiology reports with respect to their factual correctness, completeness, and potential harm for patients. We used Likert scale analysis and inductive free-text categorization to assess the quality of the simplified reports. Results Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed relevant medical information, and potentially harmful passages were reported. Conclusion While we see a need for further adaption to the medical field, the initial insights of this study indicate a tremendous potential in using LLMs like ChatGPT to improve patient-centered care in radiology and other medical domains. Clinical relevance statement Patients have started to use ChatGPT to simplify and explain their medical reports, which is expected to affect patient-doctor interaction. This phenomenon raises several opportunities and challenges for clinical routine. Key Points • Patients have started to use ChatGPT to simplify their medical reports, but their quality was unknown. • In a questionnaire, most participating radiologists overall asserted good quality to radiology reports simplified with ChatGPT. However, they also highlighted a notable presence of errors, potentially leading patients to draw harmful conclusions. • Large language models such as ChatGPT have vast potential to enhance patient-centered care in radiology and other medical domains. To realize this potential while minimizing harm, they need supervision by medical experts and adaption to the medical field. Graphical Abstract

Dynamic susceptibility contrast MRI (DSC-MRI) is the current standard for the measurement of Cerebral Blood Flow (CBF) and Cerebral Blood Volume (CBV), but it is not suitable for the measurement of Extraction Flow (EF) and may not allow for absolute quantification. The objective of this study was to develop and evaluate a methodology to measure CBF, CBV, and EF from T1-weighted dynamic contrast-enhanced MRI (DCE-MRI). A two-compartment modeling approach was developed, which applies both to tissues with an intact and with a broken Blood-Brain-Barrier (BBB). The approach was evaluated using measurements in normal grey matter (GM) and white matter (WM) and in tumors of 15 patients. Accuracy and precision were estimated with simulations of normal brain tissue. All tumor and normal tissue curves were accurately fitted by the model. CBF (mL/100 mL/min) was 82 +/- 21 in GM and 23 +/- 14 in WM, CBV (mL/100 mL) was 2.6 +/- 0.8 in GM and 1.3 +/- 0.4 in WM. EF (mL/100 mL/min) was close to zero in GM (-0.009 +/- 0.05) and WM (-0.03 +/- 0.08). Simulations show an overlap between CBF values of WM and GM, which is eliminated when Contrast-to-Noise (CNR) is improved. The model provides a consistent description of tracer kinetics in all brain tissues, and an accurate assessment of perfusion and permeability in reference tissues. The measurement sequence requires optimization to improve CNR and the precision in the perfusion parameters. With this approach, DCE-MRI presents a promising alternative to DSC-MRI for quantitative bolus-tracking in the brain.

Selective functionalization of the external surface of porous nanoparticles is of great interest for numerous potential applications in the field of nanotechnology. Regarding metal–organic frameworks (MOFs), few methods for such modifications have been reported in the literature. Herein, we focus on the covalent attachment of functional polymers on the external surface of MIL-100(Fe) nanoparticles in order to implement properties such as increased chemical and colloidal stability or dye-labeling for the investigation of the particles by fluorescence based techniques. We prove covalent nanoparticles-polymer bond formation by liquid NMR after dissolution of the functionalized MOF under mild conditions and estimate the amount of covalently attached polymer by UV–vis spectroscopy. The functionalization of the MOF nanoparticles with fluorescently labeled polymers enables the investigation of nanoparticle uptake into tumor cells by fluorescence microscopy. Furthermore, the influence of the polymer shell on the magnetic resonance imaging activity of MIL-100(Fe) is investigated in detail. The functionalization approach presented here is expected to enable the fabrication of hybrid nanomaterials, extending the enormous chemical space of MOFs into polymer materials.

Cerebral small vessel disease (cSVD) comprises pathological processes of the small vessels in the brain that may manifest clinically as stroke, cognitive impairment, dementia, or gait disturbance. It is generally accepted that endothelial dysfunction, including blood-brain barrier (BBB) failure, is pivotal in the pathophysiology. Recent years have seen increasing use of imaging, primarily dynamic contrast-enhanced magnetic resonance imaging, to assess BBB leakage, but there is considerable variability in the approaches and findings reported in the literature. Although dynamic contrast-enhanced magnetic resonance imaging is well established, challenges emerge in cSVD because of the subtle nature of BBB impairment. The purpose of this work, authored by members of the HARNESS Initiative, is to provide an in-depth review and position statement on magnetic resonance imaging measurement of subtle BBB leakage in clinical research studies, with aspects requiring further research identified. We further aim to provide information and consensus recommendations for new investigators wishing to study BBB failure in cSVD and dementia.