Key result
Artificial intelligence algorithms applied to bio-signals from IoT sensors offer potential for early prediction, detection, and monitoring of health conditions, though new diagnostic challenges remain.
Why the study?
The scope of bio-signals using artificial intelligence in medicine, related IoT sensor case studies, and emerging diagnostic challenges needed to be explored.
This paper reviews the scope, case studies, and challenges of using artificial intelligence to analyze bio-signals for medical diagnosis and health monitoring.
AI bio-signal tools may aid monitoring and prediction; leaves open validation before cardiovascular practice change.
Artificial Intelligence (AI) has broadly connected the medical field at various levels of diagnosis based on the congruous data generated. Different types of bio-signal can be used to monitor a patient’s condition and in decision making. Medical equipment uses signals to communicate information to care staff. AI algorithms and approaches will help to predict health problems and check the health status of organs, while AI prediction, classification, and regression algorithms are helping the medical industry to protect from health hazards. The early prediction and detection of health conditions will guide people to stay healthy. This paper represents the scope of bio-signals using AI in the medical area. It will illustrate possible case studies relevant to bio-signals generated through IoT sensors. The bio-signals that retrospectively occur are discussed, and the new challenges of medical diagnosis using bio-signals are identified.
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Mudrakola et al. (2022) reported a review. Artificial Intelligence for bio-signals was evaluated. Artificial intelligence algorithms applied to bio-signals from IoT sensors offer potential for early prediction, detection, and monitoring of health conditions, though new diagnostic challenges remain.