Evaluation of ChatGPT for Pelvic Floor Surgery Counseling.

IMPORTANCE: Large language models are artificial intelligence applications that can comprehend and produce human-like text and language. ChatGPT is one such model. Recent advances have increased interest in the utility of large language models in medicine. Urogynecology counseling is complex and time-consuming. Therefore, we evaluated ChatGPT as a potential adjunct for patient counseling. OBJECTIVE: Our primary objective was to compare the accuracy and completeness of ChatGPT responses to information in standard patient counseling leaflets regarding common urogynecological procedures. STUDY DESIGN: Seven urogynecologists compared the accuracy and completeness of ChatGPT responses to standard patient leaflets using 5-point Likert scales with a score of 3 being "equally accurate" and "equally complete," and a score of 5 being "much more accurate" and much more complete, respectively. This was repeated 3 months later to evaluate the consistency of ChatGPT. Additional analysis of the understandability and actionability was completed by 2 authors using the Patient Education Materials Assessment Tool. Analysis was primarily descriptive. First and second ChatGPT queries were compared with the Wilcoxon signed rank test. RESULTS: The median (interquartile range) accuracy was 3 (2-3) and completeness 3 (2-4) for the first ChatGPT query and 3 (3-3) and 4 (3-4), respectively, for the second query. Accuracy and completeness were significantly higher in the second query (P < 0.01). Understandability and actionability of ChatGPT responses were lower than the standard leaflets. CONCLUSIONS: ChatGPT is similarly accurate and complete when compared with standard patient information leaflets for common urogynecological procedures. Large language models may be a helpful adjunct to direct patient-provider counseling. Further research to determine the efficacy and patient satisfaction of ChatGPT for patient counseling is needed.

Pathologic, immunologic, and clinical analysis of the microsatellite instability phenotype in endometrial carcinoma.

The objective of this study was to determine if quantifying the microsatellite instability (MSI) phenotype could serve as a biomarker for clinical and immunologic features of deficient mismatch repair (dMMR) endometrial cancer (EC). Patients with EC undergoing hysterectomy whose tumors demonstrated dMMR were included. Immunohistochemistry (IHC) of mismatch repair proteins and polymerase chain reaction analysis of NR27, BAT25, BAT26, NR24, and NR21 microsatellite loci were performed on each case. The MSI phenotype was quantified by subtracting the number of nucleotides of each microsatellite in tumor tissue from the corresponding microsatellite in paired normal tissue and summing the absolute differences. This was termed marker sum (MS) and is a novel quantification. Tumor-infiltrating lymphocytes (TILs) were identified by IHC for CD3, CD4, and CD8 and quantified with digital image analysis. Tumor infiltration of lymphocytes and clinical characteristics were stratified by MS. Four hundred fifty-nine consecutive patients with dMMR EC were analyzed. MS ranged from 1 to 32. Post hoc, 2 cohorts were defined using receiver operating characteristic curves (MS less than 13 and MS greater than 12). With the exception of tumor grade, all clinical and pathologic features, all tumor characteristics, and the numbers of TILs were similar between cohorts. The MSI phenotype is highly variable in dMMR EC, and no correlation between the immune profile and the severity of the MSI phenotype was observed.

Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers.

OBJECTIVES: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe segmentation and emphysema quantification (EQ) on chest-computed tomography as it compares to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) severity classification of chronic obstructive pulmonary disease (COPD) patients. METHODS: Patients (n=137) who underwent chest-computed tomography acquisition and spirometry within 6 months were retrospectively included in this Institutional Review Board-approved and Health Insurance Portability and Accountability Act-compliant study. Patient-specific spirometry data, which included forced expiratory volume in 1 second, forced vital capacity, and the forced expiratory volume in 1 second/forced vital capacity ratio (Tiffeneau-Index), were used to assign patients to their respective GOLD stage I to IV. Lung lobe segmentation was carried out using AI-RAD Companion software prototype (Siemens Healthineers), a deep convolution image-to-image network and emphysema was quantified in each lung lobe to detect the low attenuation volume. RESULTS: A strong correlation between the whole-lung-EQ and the GOLD stages was found (ρ=0.88, P<0.0001). The most significant correlation was noted in the left upper lobe (ρ=0.85, P<0.0001), and the weakest in the left lower lobe (ρ=0.72, P<0.0001) and right middle lobe (ρ=0.72, P<0.0001). CONCLUSIONS: AI-based per lobe segmentation and its EQ demonstrate a very strong correlation with the GOLD severity stages of COPD patients. Furthermore, the low attenuation volume of the left upper lobe not only showed the strongest correlation to GOLD severity but was also able to most clearly distinguish mild and moderate forms of COPD. This is particularly relevant due to the fact that early disease processes often elude conventional pulmonary function diagnostics. Earlier detection of COPD is a crucial element for positively altering the course of disease progression through various therapeutic options.

A methacrylate-based polymeric imidazole ligand yields quantum dots with low cytotoxicity and low nonspecific binding.

This paper assesses the biocompatibility for fluorescence imaging of colloidal nanocrystal quantum dots (QDs) coated with a recently-developed multiply-binding methacrylate-based polymeric imidazole ligand. The QD samples were purified prior to ligand exchange via a highly repeatable gel permeation chromatography (GPC) method. A multi-well plate based protocol was used to characterize nonspecific binding and toxicity of the QDs toward human endothelial cells. Nonspecific binding in 1% fetal bovine serum was negligible compared to anionically-stabilized QD controls, and no significant toxicity was detected on 24h exposure. The nonspecific binding results were confirmed by fluorescence microscopy. This study is the first evaluation of biocompatibility in QDs initially purified by GPC and represents a scalable approach to comparison among nanocrystal-based bioimaging scaffolds.