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.

Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce the rate of blindness in cataract patients. However, the professional knowledge of ophthalmologists is necessary for the clinical cataract detection. Therefore, the potential costs may make it difficult for the widespread use of cataract detection to prevent blindness. Artificial intelligence assisted diagnosis based on medical images has attracted more and more attention of researchers. Many studies have focused on the use of pre-defined feature sets for cataract classification, but the predefined feature sets can be incomplete or redundant. On account of the above issues, some studies have proposed deep learning methods to automatically extract image features, but all based on global features and none has analyzed the layer-by-layer transformation process of the middle-tier features. This paper uses Convolutional Neural Networks (CNN) to learn useful features directly from input data, and Deconvolution Network (DN) method is employed to investigate how CNN characterizes cataract layer-by-layer. We found that compared to the global feature set, the detail vascular information which is lost after multi-layer convolution calculation also plays an important role in cataract grading task. And this finding fits with the morphological definition of fundus image. Through the finding, we gained insights into the design of hybrid Global-local feature representation model to improve the recognition performance of automatic cataract grading.

Exploring the temporal relationship among events in patient Electronic Medical Records (EMR) is an important problem in biomedical informatics and the results can reveal patients' impending disease conditions. In this paper, we investigate the problem of mining patterns from a sequence of point events, i.e., we only have the information on when the event happens but no duration or numerical value available. We propose a whole pipeline, including event preprocessing, pattern mining and outcome analysis to mine the patterns and evaluate their effectiveness and discriminative power. Finally we treat those mined patterns as additional features and evaluate them in a predictive modeling task for the early detection of Congestive Heart Failure. On a real world EMR data warehouse, we found that by adding those sequential pattern features, the prediction performance could be significantly improved (approximately 0.1).