The utilization of artificial intelligence (AI) to drive progress in electronic skin technology is revolutionising health monitoring and diagnostics, as stated in a recent review article published in the journal Nature Medicine Intelligence. The article, authored by researchers from the California Institute of Technology, underscores the significant role of AI in the development of next-generation electronic skin (e-skin) and the analysis of health data gathered through these innovative devices.
Electronic skin (e-skin) refers to integrated electronics that mimic and surpass the functions of human skin. E-skins are flexible and comfortable, making them suitable for placement on robotic and human body locations to continuously and non-invasively record biosignals. These e-skins are utilised in various applications such as smart bandages, wristbands, tattoo-like stickers, textiles, rings, face masks, as well as customised smart socks and shoes.
Despite the simplification of the procedure for gathering large-scale health data through real-time recording, the analysis and interpretation of this data continue to be time-consuming and challenging. The review article highlights the application of machine learning algorithms in recent multimodal e-skin platforms for the analysis of health data. With advancements in big data and digital medicine, AI technologies can optimise e-skin design and create personalised health profiles.
One of the main difficulties in emulating vital human skin properties in artificial skin is related to material selection. AI has been proposed as a solution to optimise materials discovery and sensor designs for producing new e-skin patches. The review article emphasises the use of machine learning to identify promising materials with specific properties and to optimise material synthesis and fabrication methods.
Furthermore, the review article discusses the role of AI technologies in processing biosensor data. Machine learning algorithms can remove noise from signals, segregate data from multiple sources, and eliminate artefacts in the e-skin data, thereby improving data quality. These algorithms can also increase the sensitivity and specificity of e-skin sensors, particularly for biochemical sensors involving enzymes with narrow working ranges.
The application of AI technologies extends beyond the design and signal processing of e-skins. AI plays a critical role in bridging the gap between human and machine interactions, enabling rapid analysis and interpretation of multimodal data obtained from e-skin patches to manipulate robotics and provide human aid.
AI-powered e-skins also hold promise for diagnosis and treatment in healthcare. The review article highlights the ability of AI-powered e-skins to detect subtle cardiovascular changes and monitor stress hormone levels in real-time, predicting mental health issues. These e-skins can also monitor multiple biological parameters and predict biomarkers through machine learning algorithms, supporting personalised therapy.
In conclusion, the integration of AI into electronic skin technology represents a significant advancement in the field of health monitoring and diagnostics. While these innovations hold great promise, there are challenges related to data accessibility, security, and the need for strict regulations in clinical applications. As AI-based models continue to evolve, the review article emphasises the importance of establishing trust in AI-generated predictions and ensuring patient safety.
Reference:
Xu C. 2023. Artificial intelligence-powered electronic skin. Nature Medicine Intelligence. Retrieved from https://www.nature.com/articles/s42256-023-00760-z
It is encouraging to witness how AI innovations are transforming healthcare, with electronic skin technology poised to revolutionise health monitoring and diagnostics. As these advancements continue to unfold, it is essential to prioritise the ethical and safe application of AI technologies, ensuring that patient care and privacy are always protected.
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