A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Abstract

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.

Efficacy of machine learning algorithms versus conventional assessment techniques in predicting postoperative complications in general surgery: a comprehensive literature review

“…Challenges remain in standardizing methodologies and ensuring robust validation across diverse surgical contexts to optimize the integration of these technologies into clinical practice effectively. Machine learning and AI technology excel conventional methods in handling and processing large, high-dimensional data, integrating diverse types like imaging and clinical notes for a holistic analysis of patients who are subjected to surg ery Efficacy of machine learning algorithms versus conventional assessment techniques in predicting postoperative complications i n general surgery comprehensive literature review to estimate post-surgical risks (Zitnik et al, 2019) (Xu et al, 2024). Stam and colleagues' (2022) systematic research revealed that AI exceeds traditional approaches regarding accuracy in predicting postoperative complications since it can identify patterns missed by conventional methods.…”