AI-Powered Innovation in Flexible and Implantable Electronics: From Sensors to Systems
DOI:
https://doi.org/10.64060/jestt.v2i2.1Keywords:
Artificial Intelligence, Biomedical Sensors, Flexible Electronics, Intelligent Healthcare Systems, Wearable and Implantable DevicesAbstract
A significant change in wearable and implantable biomedical technology is being driven by the combination of flexible electronics and artificial intelligence (AI). Real-time, non-invasive monitoring of vital signs, including temperature, heart rate, and mobility, is made possible by flexible devices because of their soft, biocompatible, and conformable architecture. These devices improve wearability and user comfort while providing ongoing healthcare tracking. By facilitating intelligent data processing, adaptable functionality, and expedited material discovery, AI greatly improves gadget performance. AI-driven, context-aware wearable devices support applications such as motion tracking, gesture recognition, and health monitoring. AI enhances quality control in manufacturing by using neural networks and computer vision to find flaws in parts like flexible PCBs. The creation of ecologically friendly devices is aided by AI-assisted predictive modelling and autonomous labs, which also help find high-performance, sustainable materials. The future of self-sustaining biomedical devices is being shaped by significant improvements in energy harvesting, electronic skin (e-skin), and intelligent interfaces, despite persistent constraints such as hardware integration, power management, data privacy, and limited computing capability. In order to create intelligent, sustainable, and high-performing healthcare systems, this paper examines the relationship between AI and flexible electronics, highlighting significant technical developments, existing constraints and new potential.
References
1. “Complementing the Genome with an ‘Exposome’: The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epi-demiology | Cancer Epidemiology, Biomarkers & Prevention | American Association for Cancer Research.” Accessed: Jun. 24, 2025. [Online]. Availa-ble:https://aacrjournals.org/cebp/article/14/8/1847/258124/Complementing-the-Genome-with-an-Exposome-The
2. “AI-Enabled Wearable and Flexible Electronics for Assessing Full Per-sonal Exposures: The Innovation.” Accessed: Jun. 12, 2025. [Online]. Available: https://www.cell.com/the-innovation/fulltext/S2666-6758(20)30031-X
3. Z. Shen et al., “Progress of flexible strain sensors for physiological signal monitoring,” Biosens. Bioelectron., vol. 211, p. 114298, Sep. 2022, doi: 10.1016/j.bios.2022.114298.
4. K. Huang, Z. Ma, and B. L. Khoo, “Advancements in Bio-Integrated Flexible Electronics for Hemodynamic Monitoring in Cardiovascular Healthcare,” Adv. Sci., vol. n/a, no. n/a, p. 2415215, doi: 10.1002/advs.202415215.
5. J. Wang et al., “Ultrasensitive, Highly Stable, and Flexible Strain Sensor Inspired by Nature,” ACS Appl. Mater. Interfaces, vol. 14, no. 14, pp. 16885–16893, Apr. 2022, doi: 10.1021/acsami.2c01127.
6. “Recent Progress in Energy Harvesting Technologies for Self‐Powered Wearable Devices: The Significance of Polymers - Afshar - 2025 - Pol-ymers for Advanced Technologies - Wiley Online Library.” Accessed: Jun. 24, 2025. [Online]. Available: https://onlinelibrary.wiley.com/doi/full/10.1002/pat.70187?casa_token=KUwbZy_6jYEAAAAA%3AGMaNYCSCkB55Qb4KIZogZG5Vo7yjZUAoWN05HZbR4qnDyesbYFLDLdn6o-0v1OpVBLQyRoi499aRD8A
7. F. Akhtar and M. H. Rehmani, “Energy Harvesting for Self-Sustainable Wireless Body Area Networks,” IT Prof., vol. 19, no. 2, pp. 32–40, Mar. 2017, doi: 10.1109/MITP.2017.34.
8. Y. H. Kwon et al., “Triboelectric energy harvesting technology for self-powered personal health management,” Int. J. Extreme Manuf., vol. 7, no. 2, p. 022005, Dec. 2024, doi: 10.1088/2631-7990/ad92c7.
9. “Green Artificial Intelligence | 15 | Biodegradable and Biocompatible M.” Accessed: Jun. 24, 2025. [Online]. Available: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003546382-15/green-artificial-intelligence-hamid-kanchan-bhatt-naseer-ahmed-priyanka-chauhan
10. S. Lee, Q. Shi, and C. Lee, “From flexible electronics technology in the era of IoT and artificial intelligence toward future implanted body sensor networks,” APL Mater., vol. 7, no. 3, p. 031302, Feb. 2019, doi: 10.1063/1.5063498.
11. “Recent Progress and Challenges of Implantable Biodegradable Biosen-sors.” Accessed: Jun. 24, 2025. [Online]. Available: https://www.mdpi.com/2072-666X/15/4/475
12. “Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform | Bioelectronic Medi-cine.” Accessed: Jun. 24, 2025. [Online]. Available: https://link.springer.com/article/10.1186/s42234-023-00118-1
13. U. Afzal et al., “Chemical engineering for advanced flexible sensors: Novel carbon-metal oxide nanocomposites with superior multi-sensing behavior,” Sens. Actuators B Chem., vol. 431, p. 137449, May 2025, doi: 10.1016/j.snb.2025.137449.
14. S. Zhao, F. Blaabjerg, and H. Wang, “An Overview of Artificial Intelli-gence Applications for Power Electronics,” IEEE Trans. Power Elec-tron., vol. 36, no. 4, pp. 4633–4658, Apr. 2021, doi: 10.1109/TPEL.2020.3024914.
15. C. C. Vu, “Embedded-machine learning and soft, flexible sensors for wearable devices - viewing from an AI engineer,” Mater. Today Phys., vol. 42, p. 101376, Mar. 2024, doi: 10.1016/j.mtphys.2024.101376.
16. S. Lin et al., “An ultralight, flexible, and biocompatible all-fiber motion sensor for artificial intelligence wearable electronics,” Npj Flex. Elec-tron., vol. 6, no. 1, pp. 1–8, Apr. 2022, doi: 10.1038/s41528-022-00158-8.
17. C. C. Vu, “Embedded-machine learning and soft, flexible sensors for wearable devices - viewing from an AI engineer,” Mater. Today Phys., vol. 42, p. 101376, Mar. 2024, doi: 10.1016/j.mtphys.2024.101376.
18. Y. Wang, Y. Wang, M. Xu, F. Dai, and Z. Li, “Flat Silk Cocoon Pressure Sensor Based on a Sea Urchin-like Microstructure for Intelligent Sens-ing,” ACS Sustain. Chem. Eng., vol. 10, no. 51, pp. 17252–17260, Dec. 2022, doi: 10.1021/acssuschemeng.2c05540.
19. Y. Ma et al., “Flexible all-textile dual tactile-tension sensors for monitor-ing athletic motion during taekwondo,” Nano Energy, vol. 85, p. 105941, Jul. 2021, doi: 10.1016/j.nanoen.2021.105941.
20. X. Zhang, W. Xia, C. Cao, P. Che, H. Pan, and Y. Chen, “Graphene doping to enhance the mechanical energy conversion performances of GR/KNN/P(VDF-TrFE) flexible piezoelectric sensors,” Phys. Chem. Chem. Phys., vol. 25, no. 2, pp. 1257–1268, Jan. 2023, doi: 10.1039/D2CP05091A.
21. C. Li, P. Wang, and D. Zhang, “Self-healable, stretchable triboelectric nanogenerators based on flexible polyimide for energy harvesting and self-powered sensors,” Nano Energy, vol. 109, p. 108285, May 2023, doi: 10.1016/j.nanoen.2023.108285.
22. “A Fully Integrated Flexible Tunable Chemical Sensor Based on Gold-Modified Indium Selenide Nanosheets | ACS Sensors.” Accessed: Jun. 24, 2025. [Online]. Available: https://pubs.acs.org/doi/full/10.1021/acssensors.2c00281?casa_token=3pw285GPDvwAAAAA%3AQdg1YJJvevrsQHkodNXYzkzh35zpP-e8Riu7KjrZzbDUTqrEefTEkavfsbfMdPQ0oqqs82Hq1Iz7tiE
23. L. Zhang et al., “A Fully Integrated Flexible Tunable Chemical Sensor Based on Gold-Modified Indium Selenide Nanosheets,” ACS Sens., vol. 7, no. 4, pp. 1183–1193, Apr. 2022, doi: 10.1021/acssensors.2c00281.
24. “Sensing–transducing coupled piezoelectric textiles for self-powered humidity detection and wearable biomonitoring - Materials Horizons (RSC Publishing).” Accessed: Jun. 24, 2025. [Online]. Available: https://pubs.rsc.org/en/content/articlelanding/2023/mh/d2mh01466a/unauth
25. X. Wang, L. Gong, Z. Li, Y. Yin, and D. Zhang, “A room temperature ammonia gas sensor based on cerium oxide/MXene and self-powered by a freestanding-mode triboelectric nanogenerator and its multifunctional monitoring application,” J. Mater. Chem. A, vol. 11, no. 14, pp. 7690–7701, Apr. 2023, doi: 10.1039/D2TA07917H.
26. H.-Y. Lin, “Embedded Artificial Intelligence: Intelligence on Devices,” Computer, vol. 56, no. 9, pp. 90–93, Sep. 2023, doi: 10.1109/MC.2023.3280397.
27. H. Ren, D. Anicic, and T. Runkler, “TinyOL: TinyML with Online-Learning on Microcontrollers,” arXiv.org. Accessed: Jun. 12, 2025. [Online]. Available: https://arxiv.org/abs/2103.08295v3
28. C. C. Vu, “Embedded-machine learning and soft, flexible sensors for wearable devices - viewing from an AI engineer,” Mater. Today Phys., vol. 42, p. 101376, Mar. 2024, doi: 10.1016/j.mtphys.2024.101376.
29. “Enhancing PCB Quality Control through AI-Driven Inspection: Lever-aging Convolutional Neural Networks for Automated Defect Detection in Electronic Manufacturing Environments by Harshit Kumar Ghelani: SSRN.” Accessed: Jun. 12, 2025. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5160737
30. H. Ghelani, AI-Driven Quality Control in PCB Manufacturing: Enhanc-ing Production Efficiency and Precision. 2024.
31. A. Goti, “AI-Driven PCB Reliability Testing for IPC-9701 Compliance,” Mar. 10, 2025, Social Science Research Network, Rochester, NY: 5237687. doi: 10.2139/ssrn.5237687.
32. J. Song, Gao, Shaohua, Zhu , Yunqiang, and C. and Ma, “A survey of remote sensing image classification based on CNNs,” Big Earth Data, vol. 3, no. 3, pp. 232–254, Jul. 2019, doi: 10.1080/20964471.2019.1657720.
33. R. Mishra, D. Mishra, and R. Agarwal, “An Artificial Intelligence-Powered Approach to Material Design,” 2024, pp. 62–89.
34. X. Bai and X. Zhang, “Artificial Intelligence-Powered Materials Sci-ence,” Nano-Micro Lett., vol. 17, no. 1, p. 135, Feb. 2025, doi: 10.1007/s40820-024-01634-8.
35. Imran, F. Qayyum, D.-H. Kim, S.-J. Bong, S.-Y. Chi, and Y.-H. Choi, “A Survey of Datasets, Preprocessing, Modeling Mechanisms, and Simula-tion Tools Based on AI for Material Analysis and Discovery,” Materials, vol. 15, no. 4, Art. no. 4, Jan. 2022, doi: 10.3390/ma15041428.
36. D. Baran, D. Corzo, and G. T. Blazquez, “Flexible Electronics: Status, Challenges and Opportunities,” Front. Electron., vol. 1, Sep. 2020, doi: 10.3389/felec.2020.594003.
37. A. Yadav, GEN AI-DRIVEN ELECTRONICS: INNOVATIONS, CHALLENGES AND FUTURE PROSPECTS. 2023. doi: 10.5281/zenodo.8165255.
38. K. Potter, M. Lord, and G. O. Olaoye, “Future Unraveled The Wonders of Wearable and Flexible Electronics,” Oct. 05, 2023, OSF. doi: 10.31219/osf.io/5xkfw.
39. Y. Zang, F. Zhang, C. Di, and D. Zhu, “Advances of flexible pressure sensors toward artificial intelligence and health care applications,” Ma-ter. Horiz., vol. 2, no. 2, pp. 140–156, 2015, doi: 10.1039/C4MH00147H.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Engineering, Science and Technological Trends

This work is licensed under a Creative Commons Attribution 4.0 International License.






