Bio-Signals in Medical Applications and Challenges Using Artificial Intelligence
Open accessAbstract
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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AI has emerged as a crucial tool for signal processing and noise reduction in wearable devices by identifying patterns in sensor data, detecting anomalies, and distinguishing between signal and noise.
“AI has emerged as a crucial tool for signal processing and noise reduction in wearable devices by identifying patterns in sensor data, detecting anomalies, and distinguishing between signal and noise [200,201].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [201]
The results indicate that 90.12% of displayed images were correctly identified, and 90.51% of emotionally responsive individuals accurately answered questions about time and image type.
“The results indicate that 90.12% of displayed images were correctly identified, and 90.51% of emotionally responsive individuals accurately answered questions about time and image type [201].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [201]
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- Authors
- Swapna, M., Viswanadhula, U. M., Aluvalu, R., Vardharajan, V., & Kotecha, K.
- Affiliations
- Department of Computer Science and Engineering, Matrusri Engineering College, Hyderabad 500058, India
- Vardhaman College of Engineering (IN)
- Chaitanya Bharathi Institute of Technology (IN)
- UNSW Sydney (AU)
- Symbiosis International University (IN)
- Corresponding author
- Vardharajan, Vijayakumar
- Kotecha, Ketan
- ORCID
- Venue
- Journal of Sensor and Actuator Networks
- Publisher
- MDPI AG
- Published
- 2022-02-25
- Volume
- 11
- Issue
- 1
- Pages
- 17
- ISSN
- 2224-2708 (ISSN-L)
- OpenAlex
- W4214748105
- Cited by
- 60
- References
- 89
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 20 · 2023: 14 · 2022: 6 · 2021: 1
- FWCI
- 13.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Sensor and Actuator Networks (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Russian Science Foundation
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- physiological signals, artificial intelligence in healthcare, diagnosis, IoT sensors, health monitoring, disease prediction, clinical decision support
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