Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

• Published in: Applied sciences Vol. 12; no. 20; p. 10271
• Main Authors: Junaid, Sahalu Balarabe, Imam, Abdullahi Abubakar, Abdulkarim, Muhammad, Surakat, Yusuf Alhaji, Balogun, Abdullateef Oluwagbemiga, Kumar, Ganesh, Shuaibu, Aliyu Nuhu, Garba, Aliyu, Sahalu, Yusra, Mohammed, Abdullahi, Mohammed, Tanko Yahaya, Abdulkadir, Bashir Abubakar, Abba, Abdallah Alkali, Iliyasu Kakumi, Nana Aliyu, Hashim, Ahmad Sobri
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2022
• Subjects: Algorithms; Artificial intelligence; Bioindicators; Biosensors; Blood pressure; Body temperature; Data mining; Datasets; Digital libraries; Electronic health records; Electronic systems; Health care; Health care delivery; Health services; Heart rate; Literature reviews; machine learning; Medical personnel; Modernization; Patients; Portable equipment; Research methodology; Respiration; Respiratory rate; Sensors; vital signs; wearable sensors; Wearable technology
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122010271

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 bioindicators 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.