Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

The emergence of large language models (LLMs) has provided robust support for application tasks across various domains, such as name entity recognition (NER) in the general domain. However, due to the particularity of the medical domain, the research on understanding and improving the effectiveness of LLMs on biomedical named entity recognition (BNER) tasks remains relatively limited, especially in the context of Chinese text. In this study, we extensively evaluate several typical LLMs, including ChatGLM2-6B, GLM-130B, GPT-3.5, and GPT-4, on the Chinese BNER task by leveraging a real-world Chinese electronic medical record (EMR) dataset and a public dataset. The experimental results demonstrate the promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for Chinese BNER tasks. More importantly, instruction fine-tuning significantly enhances the performance of LLMs. The fine-tuned offline ChatGLM2-6B surpassed the performance of the task-specific model BiLSTM+CRF (BC) on the real-world dataset. The best fine-tuned model, GPT-3.5, outperforms all other LLMs on the publicly available CCKS2017 dataset, even surpassing half of the baselines; however, it still remains challenging for it to surpass the state-of-the-art task-specific models, i.e., Dictionary-guided Attention Network (DGAN). To our knowledge, this study is the first attempt to evaluate the performance of LLMs on Chinese BNER tasks, which emphasizes the prospective and transformative implications of utilizing LLMs on Chinese BNER tasks. Furthermore, we summarize our findings into a set of actionable guidelines for future researchers on how to effectively leverage LLMs to become experts in specific tasks.

Feature sparse problem is commonly existing in patient similarity calculation task with clinical data, to track which, some approaches have been proposed to use Graph Neural Network (GNN) to model the complex structural information in patient Electronic Medical Records (EMRs). These GNN based approaches usually treat medical concepts (i.e., symptoms, diseases) as nodes to learn spatial features and adopt Recurrent Neural Network (RNN) to learn temporal sequence of these concepts. However, in many cases, several sequential concepts contained in EMR text are considered as occur simultaneously in the clinical diagnosis (i.e., some symptoms are detected simultaneously by once test), learning temporal sequence of these sequential concepts might cause noise for patient similarity calculation. Furthermore, the limited discriminative capability of concepts cannot provide sufficient indicative information for similarity learning. To this end, we propose a Knowledge-guided Event-relation Graph Learning Network (KEGLN) for patient similarity calculation. Specifically, after event extraction, we first construct element-relation graphs and use the first Graph Convolutional Network (GCN) and Graph Attention Network (GAT) layer to aggregate features from each event and its involved elements for reducing the noise produced by temporal sequence of concepts. Meanwhile, the entity description and attribute-value structure are extracted to supplement background knowledge of elements (concepts and trigger words). For the updated event nodes, we then design a event-relation graph and adopt the second GCN and GAT layer to aggregate information from events and their directly neighbors to extract spatial features of events at the current moment. Finally, the Bidirectional Long Short-Term Memory (BiLSTM) model is adopted to learn temporal dependency of event nodes to capture the dynamic change of disease progress. Through diverse datasets and extensive experiments, our KEGLN model outperforms all baselines for Chinese patient similarity calculation.