###### Abstract  
Artificial intelligence (AI) and wearable sensors are gradually transforming healthcare service delivery from the traditional hospital-centred model to the personal-portable-device-centred model. Studies have revealed that this transformation can provide an intelligent framework with automated solutions for clinicians to assess patients' general health. Often, electronic systems are used to record numerous clinical records from patients. Vital sign data, which are critical clinical records are important traditional bioin indicators for assessing a patient's general physical health status and the degree of derangement happening from the baseline of the patient. The vital signs include blood pressure, body temperature, respiratory rate, and heart pulse rate. Knowing vital signs is the first critical step for any clinical evaluation, they also give clues to possible diseases and show progress towards illness recovery or deterioration. Techniques in machine learning (ML), a subfield of artificial intelligence (AI), have recently demonstrated an ability to improve analytical procedures when applied to clinical records and provide better evidence supporting clinical decisions. This literature review focuses on how researchers are exploring several benefits of embracing AI techniques and wearable sensors in tasks related to modernizing and optimizing healthcare data analyses. Likewise, challenges concerning issues associated with the use of ML and sensors in healthcare data analyses are also discussed. This review consequently highlights open research gaps and opportunities found in the literature for future studies. artificial intelligence; machine learning; vital signs; wearable sensors +
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responsible for these achievements by ML algorithms [3]. The discovery of beneficial healthcare knowledge brought by the application of AI techniques in analyzing medical datasets has attracted immense attention [4]. Clinicians are widely inclined to adopt the application of ML algorithms to process clinical datasets for their accuracy, robustness, and interpretability [5]. Often, the atmosphere for delivering medical care by health professionals is hasty, with the availability of boundless arrays of technologies and varying individual conclusions and judgments [6]. During a patient's examination, important information is continuously collected by health professionals either automatically or manually using appropriate devices. Different approaches are being used worldwide to gather patients' vital information [7]. However, the situation in an intensive care unit (ICU) differs as it requires more devices for medical diagnoses and the continuous monitoring of each subject [8]. Accordingly, the electronic health form (EHF) system accepts and stores the volume of measured medical data in real-time [9; 10]. Health forms might contain high-dimensional health data ranging from vital signs data such as blood glucose level, blood pressure, heart rate, oxygen saturation level, and body temperature, to other demographic data such as family medical history, laboratory tests, and medication records [11]. Thus, volumes of such high-dimensional health data pose an interpretational challenge to health professionals during the diagnosis and treatment of patients [12]. Amongst the health data parameters are vital signs, also referred to as bioindicators. These are important pieces of clinical information used to objectively measure and assess the general physical health of a person. These vital signs give clues to possible diseases and show progress toward recovery. Likewise, acute, and protracted diseases can also be monitored via vital signs. Thus, they serve as tools for crucial communication concerning the health status of patients [13; 14], and they are usually the first intervention commonly observed by health professionals.