Evaluating the Feasibility and Accuracy of Large Language Models for Medical History-Taking in Obstetrics and Gynecology

Effective physician-patient communications in pre-diagnostic environments, and most specifically in complex and sensitive medical areas such as infertility, are critical but consume a lot of time and, therefore, cause clinic workflows to become inefficient. Recent advancements in Large Language Models (LLMs) offer a potential solution for automating conversational medical history-taking and improving diagnostic accuracy. This study evaluates the feasibility and performance of LLMs in those tasks for infertility cases. An AI-driven conversational system was developed to simulate physician-patient interactions with ChatGPT-4o and ChatGPT-4o-mini. A total of 70 real-world infertility cases were processed, generating 420 diagnostic histories. Model performance was assessed using F1 score, Differential Diagnosis (DDs) Accuracy, and Accuracy of Infertility Type Judgment (ITJ). ChatGPT-4o-mini outperformed ChatGPT-4o in information extraction accuracy (F1 score: 0.9258 vs. 0.9029, p = 0.045, d = 0.244) and demonstrated higher completeness in medical history-taking (97.58% vs. 77.11%), suggesting that ChatGPT-4o-mini is more effective in extracting detailed patient information, which is critical for improving diagnostic accuracy. In contrast, ChatGPT-4o performed slightly better in differential diagnosis accuracy (2.0524 vs. 2.0048, p > 0.05). ITJ accuracy was higher in ChatGPT-4o-mini (0.6476 vs. 0.5905) but with lower consistency (Cronbach's $\alpha$ = 0.562), suggesting variability in classification reliability. Both models demonstrated strong feasibility in automating infertility history-taking, with ChatGPT-4o-mini excelling in completeness and extraction accuracy. In future studies, expert validation for accuracy and dependability in a clinical setting, AI model fine-tuning, and larger datasets with a mix of cases of infertility have to be prioritized.

CMAB: A First National-Scale Multi-Attribute Building Dataset Derived from Open Source Data and GeoAI

Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height, and structure, as well as indicative attributes like function, quality, and age, is essential for accurate urban analysis, simulations, and policy updates. Existing large-scale building datasets lack accuracy, extensibility and indicative attributes. This paper presents a geospatial artificial intelligence (GeoAI) framework for large-scale building modeling, introducing the first Multi-Attribute Building dataset (CMAB) in China at a national scale. The dataset covers 3,667 natural cities with a total rooftop area of 21.3 billion square meters with an F1-Score of 89.93% in rooftop extraction through the OCRNet. We trained bootstrap aggregated XGBoost models with city administrative classifications, incorporating building features such as morphology, location, and function. Using multi-source data, including billions of high-resolution Google Earth imagery and 60 million street view images (SVI), we generated rooftop, height, function, age, and quality attributes for each building. Accuracy was validated through model benchmarks, existing similar products, and manual SVI validation. The results support urban planning and sustainable development.