Risk stratification of thyroid nodules: Assessing the suitability of ChatGPT for text-based analysis.

PURPOSE: Accurate risk stratification of thyroid nodules is essential for optimal patient management. This study aimed to assess the suitability of ChatGPT for risk stratification of thyroid nodules using a text-based evaluation. METHODS: A dataset was compiled comprising 50 anonymized clinical reports and associated risk assessments for thyroid nodules. The Chat Generative Pre-trained Transformer (ChatGPT) was used to classify sonographic patterns in accordance with the Thyroid Imaging Reporting and Data System (TI-RADS). The model's performance was assessed using various criteria, including sensitivity, specificity, and accuracy. A comparative analysis was conducted, evaluating the model against investigator-based risk stratification as well as histology. RESULTS: With an overall agreement rate of 42 % in comparison with examiner-based evaluation (TI-RADS 1-5), the results show that ChatGPT has moderate potential for predicting the risk of malignancy in thyroid nodules using text-based reports. The chatbot model achieved a sensitivity of 86.7 %, a specificity of 10.7 %, and an overall accuracy of 68 % when distinguishing between low-risk (TI-RADS 2 and 3) and high-risk (TI-RADS 4 and 5) categories. Interrater reliability was calculated with a Cohen's kappa of 0.686. CONCLUSION: This study highlights the potential of ChatGPT in assisting clinicians with risk stratification of thyroid nodules. The results suggest that ChatGPT can facilitate personalized treatment decisions, although the agreement rate is still low. Further research and validation studies are necessary to establish the clinical applicability and generalizability of ChatGPT in routine practice. The integration of ChatGPT into clinical workflows has the potential to enhance thyroid nodule risk assessment and improve patient care.

Quo vadis, smallholder forest landscape? An introduction to the LPB-RAP model.

The impacts of the Anthropocene on climate and biodiversity pose societal and ecological problems that may only be solved by ecosystem restoration. Local to regional actions are required, which need to consider the prevailing present and future conditions of a certain landscape extent. Modeling approaches can be of help to support management efforts and to provide advice to policy making. We present stage one of the LaForeT-PLUC-BE model (Landscape Forestry in the Tropics-PCRaster Land Use Change-Biogeographic & Economic model; in short: LPB) and its thematic expansion module RAP (Restoration Areas Potentials). LPB-RAP is a high-resolution pixel-based scenario tool that relies on a range of explicit land use types (LUTs) to describe various forest types and the environment. It simulates and analyzes future landscape configurations under consideration of climate, population and land use change long-term. Simulated Land Use Land Cover Change (LULCC) builds on dynamic, probabilistic modeling incorporating climatic and anthropogenic determinants as well as restriction parameters to depict a sub-national regional smallholder-dominated forest landscape. The model delivers results for contrasting scenario settings by simulating without and with potential Forest and Landscape Restoration (FLR) measures. FLR potentials are depicted by up to five RAP-LUTs. The model builds on user-defined scenario inputs, such as the Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). Model application is here exemplified for the SSP2-RCP4.5 scenario in the time frame 2018-2100 on the hectare scale in annual resolution using Esmeraldas province, Ecuador, as a case study area. The LPB-RAP model is a novel, heuristic Spatial Decision Support System (SDSS) tool for smallholder-dominated forest landscapes, supporting near-time top-down planning measures with long-term bottom-up modeling. Its application should be followed up by FLR on-site investigations and stakeholder participation across all involved scales.