Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery
Source abstract
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 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Non-Invasive Vital Sign Monitoring · COVID-19 diagnosis using AI · Machine Learning in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Ahmadu Bello University, Zaria 810107, Nigeria
School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
Department of Family Medicine, University Medical Centre, Ahmadu Bello University, Zaria 810107, Nigeria
Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria
Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
Department of Electrical Engineering, University of Jos, Bauchi Road, Jos 930105, Nigeria
SEHA Abu Dhabi Health Services Co., Abu Dhabi 109090, United Arab Emirates
Department of Chemistry, Gombe State University, Gombe 760253, Nigeria
Institute of Health Science, Kaduna State University, Tafawa Balewa Way, 800283 Kaduna, Nigeria
Patient Care Department, General Ward, Saudi German Hospital Cairo, Taha Hussein Rd, Huckstep, El Nozha, Cairo 4473303, Egypt
Funding
Tertiary Education Trust Fund (TETFund) Nigeria, via National Research Fund (NRF)
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