Objective To explore the use of ChatGPT (Chat Generative Pre-trained Transformer) in pediatric diagnosis, treatment and doctor-patient communication, evaluate the professionalism and accuracy of the medical advice provided, and assess its ability to provide psychological support. Methods The knowledge databases of ChatGPT 3.5 and 4.0 versions as of April 2023 were selected. A total of 30 diagnosis and treatment questions and 10 doctor-patient communication questions regarding the pediatric urinary system were submitted to ChatGPT versions 3.5 and 4.0, and the answers to ChatGPT were evaluated. Results The answers to the 40 questions answered by ChatGPT versions 3.5 and 4.0 all reached the qualified level. The answers to 30 diagnostic and treatment questions in ChatGPT 4.0 version were superior to those in ChatGPT 3.5 version (P=0.024). There was no statistically significant difference in the answers to the 10 doctor-patient communication questions answered by ChatGPT 3.5 and 4.0 versions (P=0.727). For prevention, single symptom, and disease diagnosis and treatment questions, ChatGPT’s answer scores were relatively high. For questions related to the diagnosis and treatment of complex medical conditions, ChatGPT’s answer scores were relatively low. Conclusion ChatGPT has certain value in assisting pediatric diagnosis, treatment and doctor-patient communication, but the medical advice provided by ChatGPT cannot completely replace the professional judgment and personal care of doctors.
Generative artificial intelligence (AI) technology plays a significant role in enhancing data application capabilities, improving disease diagnosis and treatment plans, and advancing health management, drug development, genetic analysis, and precision medicine. However, due to the diagnostic complexity, treatment diversity, and high technical demands of orthopedic diseases, the application of generative AI in orthopedics is still in its early exploration stage. This paper, based on the experience of applying generative AI, summarizes the concept, working principles, progress of application in orthopedics, as well as the existing shortcomings and optimization strategies, aiming to provide valuable insights for the application of generative AI in orthopedics clinical practice.
Guideline implementation with decision support checklist (GUIDES) aims to assist the self-reflection of evidence-based clinical decision support system (CDSS) related professionals to enhance the process monitor and continuous improvement of evidence-based CDSS. This paper interpreted the development process, target user, and assessment method of GUIDES, analyzed the practical value of GUIDES through a typical example, and then reflected on the GUIDES and current studies on evidence-based CDSS in China. It is expected to provide references for future studies.
Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.
Objective To develop a computer-aided diagnosis model for lung cancer based on routine health examination data for identifying individuals with a current high risk of lung cancer in health screening settings, thereby providing decision support for subsequent clinical confirmation. Methods Individuals who underwent health examinations at the Health Management Center of West China Hospital, Sichuan University, between 2010 and 2022 were enrolled. After screening, a retrospective cohort of 5257 subjects was retained, comprising 1307 patients with lung cancer and 3950 non-lung cancer controls. A three-tier feature fusion model was designed: Heterogeneous feature encoding module: a multi-layer perceptron and bidirectional encoder representations from transformers (BERT) were employed to extract feature vectors from structured data and unstructured data (medical records and imaging report texts), respectively. Heterogeneous feature fusion architecture: dimensional expansion concatenation coupled with a gated recurrent unit based gating network was implemented to achieve multi-scale feature alignment and deep interaction, thereby addressing dimensional discrepancies and information redundancy. Attention-based decision mechanism: word-level attention with weighted pooling was applied to dynamically capture key features and generate risk probability distributions. Model performance was evaluated using precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Results The proposed model significantly outperformed both single-data-type models and simple concatenation approaches. On the test set, the proposed model achieved a recall of 0.861, an F1-score of 0.882, and an AUC-ROC of 0.972, substantially surpassing the best-performing model trained on structured data alone (extreme gradient boosting: recall=0.630, F1-score=0.725, AUC-ROC=0.916) and the model trained on unstructured data alone (BERT coupled with a bidirectional long short-term memory network: recall=0.833, F1-score=0.846, AUC-ROC=0.944). Feature elimination experiments demonstrated minimal performance variation across different feature subsets, confirming the model’s capability to effectively identify and mitigate the impact of irrelevant features. Subgroup analyses revealed that the model performed optimally in female subjects (recall=0.835, F1-score=0.838, AUC-ROC=0.950) and individuals aged >69 years (recall=0.913, F1-score=0.875, AUC-ROC=0.911). Conclusion The proposed model based on heterogeneous health examination data can identify high-risk individuals for lung cancer among health examination populations using only routine screening data, thereby facilitating the early diagnosis of lung cancer in this population.