The field of health monitoring and disease detection is rapidly advancing, with a growing focus on wearable-based technologies and AI-driven approaches. Recent research has explored the use of smartwatches and other wearables to detect intoxication levels, antidepressant use, and other health metrics. Additionally, there has been significant progress in the development of AI models for disease detection, including epilepsy, Alzheimer's disease, and depression. These models often leverage multimodal data, such as EEG signals, speech, and text, to improve detection accuracy. Notable papers in this area include: Advancing Intoxication Detection, which introduced a smartwatch-based approach to detecting intoxication levels. Transformer Model Detects Antidepressant Use From a Single Night of Sleep, which presented a noninvasive biomarker for detecting antidepressant intake. DistilCLIP-EEG, which proposed a multimodal model for epilepsy detection that integrates EEG signals and text descriptions. TRI-DEP, which conducted a trimodal comparative study for depression detection using speech, text, and EEG.