Advances in Nano-BioElectronics, Robotic Surgery, and Technologies for Medical and Healthcare

Authors

  • Soltani Sharif Abadi Ali

    Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw 00-661, Poland

  • Agata Koguciuk

    Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw 00-661, Poland

  • Mateusz Jangas

    Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw 00-661, Poland

  • Patryk Korycki

    Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw 00-661, Poland

  • Katarzyna Tylicka

    Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw 00-661, Poland

  • Piotr Targos

    Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warsaw 00-661, Poland

DOI:

https://doi.org/10.30564/jeis.v7i1.9353
Received: 2 February 2025; Revised: 25 February 2025; Accepted: 3 March 2025; Published Online: 13 March 2025

Abstract

The convergence of electronics and digital technologies with healthcare has revolutionized medical services, enhancing patient care, streamlining operations, and expanding access to critical resources. Over the years, groundbreaking innovations in medical devices, diagnostic tools, remote health monitoring, telemedicine, and electronic health records (EHRs) have significantly improved healthcare efficiency and patient outcomes. These advancements have not only optimized clinical workflows but also facilitated the widespread dissemination of medical knowledge, making healthcare more accessible and data-driven. A thorough exploration of electronic applications in medicine is essential to understanding their transformative impact and future potential. This review provides an in-depth analysis of recent progress in medical electronics and digital health technologies, examining key developments, current applications, and emerging trends. The discussion encompasses vital topics such as wearable health technologies, smart sensors, robotic-assisted surgery, the role of digital tools during the COVID-19 pandemic, the Medical Internet of Things (M-IoT), electronic health record systems, the influence of social media and digital platforms, and the rise of mobile health applications. The review paper presents recent electronic technologies in the medical and healthcare fields and compares them across various aspects. Each section offers a comparative evaluation or detailed assessment of various innovations, highlighting their functionalities, advantages, and challenges in modern healthcare.

Keywords:

Robotic Surgery; Bioelectronics; M-IoT; Wearable Technologies; COVID-19

References

[1] Raghupathi, W., Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential. Health Information Science and Systems. 2, 1–10. DOI: https://doi.org/10.1186/2047-2501-2-3

[2] Mehta, N., Pandit, A., 2018. Concurrence of big data analytics and healthcare: A systematic review. International Journal of Medical Informatics. 114, 57–65. DOI: https://doi.org/10.1016/j.ijmedinf.2018.03.013

[3] Topol, E., 2019. Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK: London, UK.

[4] Patel, S., Park, H., Bonato, P., et al., 2012. A review of wearable sensors and systems with application in rehabilitation. Journal of Neuroengineering and Rehabilitation. 9, 1–17. DOI: https://doi.org/10.1186/1743-0003-9-21

[5] Piwek, L., Ellis, D.A., Andrews, S., et al., 2016. The rise of consumer health wearables: promises and barriers. PLoS Medicine. 13(2), e1001953. DOI: https://doi.org/10.1371/journal.pmed.1001953

[6] Yang, G.-Z., Cambias, J., Cleary, K., et al., 2017. Medical robotics—Regulatory, ethical, and legal considerations for increasing levels of autonomy. Science Robotics. 2, eaam8638.

[7] Wosik, J., Fudim, M., Cameron, B., et al., 2020. Telehealth transformation: COVID-19 and the rise of virtual care. Journal of the American Medical Informatics Association. 27(6), 957–962. DOI: https://doi.org/10.1093/jamia/ocaa067

[8] Kruse, C.S., Karem, P., Shifflett, K., et al., 2018. Evaluating barriers to adopting telemedicine worldwide: a systematic review. Journal of Telemedicine and Telecare. 24(1), 4–12. DOI: https://doi.org/10.1177/1357633X16674087

[9] Adler-Milstein, J., Jha, A.K., 2017. HITECH Act drove large gains in hospital electronic health record adoption. Health Affairs. 36(8), 1416–1422. DOI: https://doi.org/10.1377/hlthaff.2016.1651

[10] Chou, W.-Y.S., Prestin, A., Lyons, C., et al., 2013. Web 2.0 for health promotion: reviewing the current evidence. American Journal of Public Health. 103(1), e9–e18. DOI: https://doi.org/10.2105/AJPH.2012.301071

[11] Boulos, M.N.K., Brewer, A.C., Karimkhani, C., et al., 2014. Mobile medical and health apps: state of the art, concerns, regulatory control and certification. Online Journal of Public Health Informatics. 5(3), 229.

[12] Islam, S.R., Kwak, D., Kabir, M.H., et al., 2015. The internet of things for health care: a comprehensive survey. IEEE Access. 3, 678–708. DOI: https://doi.org/10.1109/ACCESS.2015.2437951

[13] Zhang, A., Lee, J.-H., Lieber, C.M., 2021. Nanowire-enabled bioelectronics. Nano Today. 38, 101135.

[14] Zhou, W., Dai, X., Lieber, C.M., 2016. Advances in nanowire bioelectronics. Reports on Progress in Physics. 80(1), 016701.

[15] Zhang, A., Lieber, C.M., 2016. Nano-bioelectronics. Chemical Reviews. 116(1), 215–257. DOI: https://doi.org/10.1021/acs.chemrev.5b00608

[16] Wu, D., Fei, F., Zhang, Q., et al., 2022. Nanoengineered on-demand drug delivery system improves efficacy of pharmacotherapy for epilepsy. Science Advances. 8(2), eabm3381.

[17] Nagy, D., Espineira, G., Indalecio, G., et al., 2020. Benchmarking of FinFET, nanosheet, and nanowire FET architectures for future technology nodes. IEEE Access. 8, 53196–53202.

[18] Gao, A., Chen, S., Wang, Y., et al., 2018. Silicon Nanowire Field-effect-transistor-based Biosensor for Biomedical Applications. Sensors & Materials. 30(8), 1841–1854.

[19] Marzana, M., Khan, M.M.A., Ahmed, A., et al., 2022. Nanocarbon for bioelectronics and biosensing. In: Castro, G.R., Nadda, A.K., Nguyen, T.A., et al. (eds.). Nanomaterials for Biocatalysis. Elsevier: Amsterdam, Netherlands. pp. 689–714.

[20] Abadi, A.S.S., Dehkordi, P.H., Hajiyan, R., et al., 2025. Design and real-time evaluation of a novel observer-based predefined-time controller for the industrial processes. ISA Transactions. 156, 551–564.

[21] Rastogi, S.K., Kalmykov, A., Johnson, N., et al., 2018. Bioelectronics with nanocarbons. Journal of Materials Chemistry B. 6(44), 7159–7178.

[22] Bareket-Keren, L., Hanein, Y., 2013. Carbon nanotube-based multi electrode arrays for neuronal interfacing: progress and prospects. Frontiers in Neural Circuits. 6, 122.

[23] Zhao, X., Liu, R., 2012. Recent progress and perspectives on the toxicity of carbon nanotubes at organism, organ, cell, and biomacromolecule levels. Environment International. 40, 244–255.

[24] Kalan, S., Chauhan, S., Coelho, R.F., et al., 2010. History of robotic surgery. Journal of Robotic Surgery. 4, 141–147.

[25] Abadi, A.S.S., Ordys, A., Kukielka, K., et al., 2023. Review on challenges for robotic eye surgery; surgical systems, technologies, cost‐effectiveness, and controllers. The International Journal of Medical Robotics and Computer Assisted Surgery. 19(4), e2524.

[26] Abadi, A.S.S., Ordys, A., Pierscionek, B., 2023. Controlling a teleoperated robotic eye surgical system under a communication channel’s unknown time delay. Proceedings of the 2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR); 22–25 August 2023; Międzyzdroje, Poland. IEEE: Piscataway, NJ, USA. pp. 211–215.

[27] Hashizume, M., Konishi, K., Tsutsumi, N., et al., 2002. A new era of robotic surgery assisted by a computer-enhanced surgical system. Surgery. 131(1), S330–S333.

[28] Abadi, A.S.S., Hosseinabadi, P.A., Mekhilef, S., 2020. Fuzzy adaptive fixed-time sliding mode control with state observer for a class of high-order mismatched uncertain systems. International Journal of Control, Automation and Systems. 18, 2492–2508.

[29] Abadi, A.S.S., Ordys, A., Pierscionek, B., 2023. Novel off-line self-tuning controller with guaranteed stability. International Journal of Automotive Technology. 24(3), 851–862.

[30] Abadi, A.S.S., Ordys, A., Pierscionek, B., 2025. Controlling of Applied Force and Cornea Displacement Estimation in Robotic Corneal Surgery With a Gripper Surgical Instrument. The International Journal of Medical Robotics and Computer Assisted Surgery. 21(1), e70038.

[31] Abadi, A.S.S., Ordys, A., Pierscionek, B., et al., 2025. Delayed teleoperated robotic eye surgical system; controller design and real-time experiments. IET Control Theory & Applications. 19(1), e70003. DOI: https://doi.org/10.1049/cth2.70003

[32] Farinha, R., Puliatti, S., Mazzone, E., et al., 2022. Potential contenders for the leadership in robotic surgery. Journal of Endourology. 36(3), 317–326.

[33] Leddy, L.S., Lendvay, T.S., Satava, R.M., 2010. Robotic surgery: applications and cost effectiveness. Open Access Surgery. 3, 99–107.

[34] Kwak, Y.H., Lee, H., Seon, K., et al., 2022. Da Vinci SP single-port robotic surgery in gynecologic tumors: Single surgeon’s initial experience with 100 cases. Yonsei Medical Journal. 63(2), 179.

[35] Alkatout, I., Salehiniya, H., Allahqoli, L., 2022. Assessment of the Versius robotic surgical system in minimal access surgery: a systematic review. Journal of Clinical Medicine. 11(13), 3754.

[36] Thomas, B.C., Slack, M., Hussain, M., et al., 2021. Preclinical evaluation of the versius surgical system, a new robot-assisted surgical device for use in minimal access renal and prostate surgery. European Urology Focus. 7(2), 444–452.

[37] Montlouis-Calixte, J., Ripamonti, B., Barabino, G., et al., 2019. Senhance 3-mm robot-assisted surgery: experience on first 14 patients in France. Journal of Robotic Surgery. 13(5), 643–647.

[38] McKechnie, T., Khamar, J., Daniel, R., et al., 2023. The Senhance surgical system in colorectal surgery: a systematic review. Journal of Robotic Surgery. 17(2), 325–334.

[39] Knežević, N., Penezić, L., Kuliš, T., et al., 2022. Senhance robot-assisted adrenalectomy: a case series. Croatian Medical Journal. 63(2), 197–201.

[40] Vinay, K., Vishal, K., 2013. Smartphone applications for medical students and professionals. Journal of Health and Allied Sciences NU. 3(1), 59–62.

[41] Martínez, F., Tobar, C., Taramasco, C., 2017. Implementation of a Smartphone application in medical education: a randomised trial (iSTART). BMC Medical Education. 17, 1–9.

[42] Jutel, A., Lupton, D., 2015. Digitizing diagnosis: a review of mobile applications in the diagnostic process. Diagnosis. 2(2), 89–96.

[43] Tang, H., Ng, J.H.K., 2006. Googling for a diagnosis—use of Google as a diagnostic aid: internet based study. BMJ. 333(7579), 1143–1145.

[44] Ahmed, A., Ali, N., Giannicchi, A., et al., 2021. Mobile applications for mental health self-care: A scoping review. Computer Methods and Programs in Biomedicine Update. 1, 100041.

[45] King, A.L.S., Pádua, M.K., Goncalves, L.L., et al., 2020. Smartphone use by health professionals: a review. Digital Health. 6, 2055207620966860.

[46] Abolfotouh, M.A., BaniMustafa, A.A., Salam, M., et al., 2019. Use of smartphone and perception towards the usefulness and practicality of its medical applications among healthcare workers in Saudi Arabia. BMC Health Services Research. 19(1), 826.

[47] Pudaruth, S., Mahomoodally, M.F., Kissoon, N., et al., 2021. MedicPlant: A mobile application for the recognition of medicinal plants from the Republic of Mauritius using deep learning in real-time. IAES International Journal of Artificial Intelligence. 10(4), 938.

[48] Masud, M., Alhumyani, H., Alshamrani, S.S., et al., 2020. Leveraging deep learning techniques for malaria parasite detection using mobile application. Wireless Communications and Mobile Computing. 2020(1), 8895429.

[49] Kousis, I., Perikos, I., Hatzilygeroudis, I., et al., 2022. Deep learning methods for accurate skin cancer recognition and mobile application. Electronics. 11(9), 1294.

[50] Sibilska-Mroziewicz, A., Drelich, E., Hameed, A., et al., 2023. The Future of Robotic Control: VR and MATLAB for Robot Manipulator Management. In: Biele, C., Kacprzyk, J., Kopeć, W., et al. (eds.). Digital Interaction and Machine Intelligence. MIDI 2023. Lecture Notes in Networks and Systems, vol 1076. Springer: Cham, Switzerland. pp. 285–289.

[51] Sibilska-Mroziewicz, A., Możaryn, J., Ordys, A., et al., 2025. VR-supported method for studying the MPC algorithm in controlling snake robot motion. Robotics and Autonomous Systems. 191, 105002.

[52] Rosen, K.R., 2008. The history of medical simulation. Journal of Critical Care. 23(2), 157–166.

[53] Li, L., Yu, F., Shi, D., et al., 2017. Application of virtual reality technology in clinical medicine. American Journal of Translational Research. 9(9), 3867.

[54] Bric, J.D., Lumbard, D.C., Frelich, M.J., et al., 2016. Current state of virtual reality simulation in robotic surgery training: a review. Surgical Endoscopy. 30, 2169–2178.

[55] Kim, Y.G., Mun, G., Kim, M., et al., 2022. A study on the VR goggle-based vision system for robotic surgery. International Journal of Control, Automation and Systems. 20(9), 2959–2971.

[56] Cho, J.S., Hahn, K.Y., Kwak, J.M., et al., 2013. Virtual reality training improves da Vinci performance: a prospective trial. Journal of Laparoendoscopic & Advanced Surgical Techniques. 23(12), 992–998.

[57] Laaki, H., Kaurila, K., Ots, K., et al., 2010. Augmenting virtual worlds with real-life data from mobile devices. Proceedings of the 2010 IEEE Virtual Reality Conference (VR); 20–24 March 2010; Boston, MA, USA. IEEE: Piscataway, NJ, USA. pp. 281–282.

[58] Marescaux, J., Leroy, J., Gagner, M., et al., 2001. Transatlantic robot-assisted telesurgery. Nature. 413(6854), 379–380.

[59] Abadi, A.S.S., Hosseinabadi, P.A., Hameed, A., et al., 2023. Fixed‐time observer‐based controller for the human–robot collaboration with interaction force estimation. International Journal of Robust and Nonlinear Control. DOI: https://doi.org/10.1002/rnc.6719

[60] Abadi, A.S.S., 2023. A novel control system for synchronizing chaotic systems in the presence of communication channel time delay; case study of Genesio-Tesi and Coullet systems. Nonlinear Analysis: Hybrid Systems. 50, 101408.

[61] Samadbeik, M., Yaaghobi, D., Bastani, P., et al., 2018. The applications of virtual reality technology in medical groups teaching. Journal of Advances in Medical Education & Professionalism. 6(3), 123.

[62] Ulbrich, M., Van den Bosch, V., Bönsch, A., et al., 2023. Advantages of a training course for surgical planning in virtual reality for oral and maxillofacial surgery: crossover study. JMIR Serious Games. 11, e40541.

[63] Oberg, P.A., Togawa, T., Spelman, F.A., 2006. Sensors in medicine and health care. John Wiley & Sons: Hoboken, NJ, USA.

[64] Vijayalakshmi, K., Uma, S., Bhuvanya, R., et al., 2018. A demand for wearable devices in health care. International Journal of Engineering & Technology. 7(1–7), 4.

[65] Sprenger, N., Sepehri Shamloo, A., Schäfer, J., et al., 2022. Feasibility and reliability of smartwatch to obtain precordial lead electrocardiogram recordings. Sensors. 22(3), 1217.

[66] Husain, K., Zahid, M.S.M., Hassan, S.U., et al., 2021. Advances of ECG sensors from hardware, software and format interoperability perspectives. Electronics. 10(2), 105.

[67] Debdas, A., 2014. Scientific Basis of Ultrasonography. In: Malhotra, N., Shah, P.K., Kumar, P., et al. (eds.). Ultrasound in Obstetrics & Gynecology. JP Medical Ltd: London, UK. pp. 1–12.

[68] Cysewska-Sobusiak, A., Skrzywanek, P., Sowier, A., 2006. Utilization of miniprobes in modern endoscopic ultrasonography. IEEE Sensors Journal. 6(5), 1323–1330.

[69] Chi, Y.M., Wang, Y.-T., Wang, Y., et al., 2011. Dry and noncontact EEG sensors for mobile brain–computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 20(2), 228–235.

[70] Kumar, A., 2021. Flexible and wearable capacitive pressure sensor for blood pressure monitoring. Sensing and Bio-Sensing Research. 33, 100434.

[71] Min, S., Kim, D.H., Joe, D.J., et al., 2023. Clinical validation of a wearable piezoelectric blood‐pressure sensor for continuous health monitoring. Advanced Materials. 35(26), 2301627.

[72] Tremper, K.K., 1989. Pulse oximetry. Chest. 95(4), 713–715.

[73] Lochner, C.M., Khan, Y., Pierre, A., et al., 2014. All-organic optoelectronic sensor for pulse oximetry. Nature Communications. 5(1), 5745.

[74] Weisfield, R.L., Hartney, M.A., Street, R.A., et al., 1998. New amorphous-silicon image sensor for x-ray diagnostic medical imaging applications. Proceedings of the Medical Imaging 1998: Physics of Medical Imaging; 24 July 1998; San Diego, CA, United States. SPIE: Bellingham, WA, USA. pp. 444–452. DOI: https://doi.org/10.1117/12.317044

[75] Katti, G., Ara, S.A., Shireen, A., 2011. Magnetic resonance imaging (MRI)–A review. International Journal of Dental Clinics. 3(1), 65–70.

[76] Vaughan, J.T., Griffiths, J.R., 2012. RF coils for MRI. John Wiley & Sons: Hoboken, NJ, USA.

[77] Greer, M., Chen, C., Mandal, S., 2019. An easily reproducible, hand-held, single-sided, MRI sensor. Journal of Magnetic Resonance. 308, 106591.

[78] Yaeger, K., Martini, M., Rasouli, J., et al., 2019. Emerging blockchain technology solutions for modern healthcare infrastructure. Journal of Scientific Innovation in Medicine. 2(1), 1. DOI: https://doi.org/10.29024/jsim.7

[79] Engelhardt, M.A., 2017. Hitching healthcare to the chain: An introduction to blockchain technology in the healthcare sector. Technology Innovation Management Review. 7(10), 1–3.

[80] Nakamoto, S., 2008. Bitcoin: A peer-to-peer electronic cash system. Available from: https://bitcoin.org/bitcoin.pdf (cited 01 May 2024).

[81] Swan, M., 2015. Blockchain: Blueprint for a new economy. O'Reilly Media, Inc.: Sebastopol, CA, USA.

[82] Gökalp, E., Gökalp, M.O., Çoban, S., et al., 2018. Analysing opportunities and challenges of integrated blockchain technologies in healthcare. In: Wrycza, S., Maślankowski, J. (eds.). Information Systems: Research, Development, Applications, Education. SIGSAND/PLAIS 2018. Lecture Notes in Business Information Processing, vol 333. Springer: Cham, Switzerland. pp. 174–183.

[83] Piccininni, M., Rohmann, J.L., Logroscino, G., et al., 2020. Blockchain-Based innovations for population-based registries for rare neurodegenerative diseases. Frontiers in Blockchain. 3, 20.

[84] Ivan, D., 2016. Moving toward a blockchain-based method for the secure storage of patient records. ONC/NIST Use of Blockchain for Healthcare and Research Workshop. ONC/NIST: Gaithersburg, MD, USA. pp. 1–11.

[85] Stewart, M., 2001. Towards a global definition of patient centred care: the patient should be the judge of patient centred care. BMJ. 322(7284), 444–445.

[86] Manyika, J., Chui, M., Brown, B., et al., 2011. Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Available from: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation (cited 01 May 2024).

[87] Hölbl, M., Kompara, M., Kamišalić, A., et al., 2018. A systematic review of the use of blockchain in healthcare. Symmetry. 10(10), 470.

[88] McGhin, T., Choo, K.-K.R., Liu, C.Z., et al., 2019. Blockchain in healthcare applications: Research challenges and opportunities. Journal of Network and Computer Applications. 135, 62–75.

[89] Roehrs, A., Da Costa, C.A., Righi, R.R., 2017. OmniPHR: A distributed architecture model to integrate personal health records. Journal of Biomedical Informatics. 71, 70–81.

[90] Azaria, A., Ekblaw, A., Vieira, T., et al., 2016. Medrec: Using blockchain for medical data access and permission management. In: 2016 2nd International Conference on Open and Big Data (OBD). Proceedings of the 2016 2nd International Conference on Open and Big Data (OBD); 22–24 August 2016; Vienna, Austria.IEEE: Piscataway, NJ, USA. pp. 25–30.

[91] Xia, Q., Sifah, E.B., Asamoah, K.O., et al., 2017. MeDShare: Trust-less medical data sharing among cloud service providers via blockchain. IEEE Access. 5, 14757–14767.

[92] Mendoza Arvizo, A.I., Avelar Sosa, L.A., García Alcaraz, J.L., et al., 2023. Beneficiary contracts on a lightweight blockchain architecture using smart contracts: a smart healthcare system for medical records. Applied Sciences. 13(11), 6694.

[93] Semenzin, S., Rozas, D., Hassan, S., 2022. Blockchain-based application at a governmental level: disruption or illusion? The case of Estonia. Policy and Society. 41(3), 386–401.

[94] Zhang, J., Xue, N., Huang, X., 2016. A secure system for pervasive social network-based healthcare. IEEE Access. 4, 9239–9250.

[95] Yue, X., Wang, H., Jin, D., et al., 2016. Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. Journal of Medical Systems. 40(8), 1–8.

[96] Uppal, A., Silvestri, D.M., Siegler, M., et al., 2020. Critical Care And Emergency Department Response At The Epicenter Of The COVID-19 Pandemic: New York City’s public health system response to COVID-19 included increasing the number of intensive care units, transferring patients between hospitals, and supplementing critical care staff. Health Affairs. 39(8), 1443–1449.

[97] Wei, E.K., Long, T., Katz, M.H., 2021. Nine lessons learned from the COVID-19 pandemic for improving hospital care and health care delivery. JAMA Internal Medicine. 181(9), 1161–1163.

[98] Schwartz, J., King, C.-C., Yen, M.-Y., 2020. Protecting healthcare workers during the coronavirus disease 2019 (COVID-19) outbreak: lessons from Taiwan’s severe acute respiratory syndrome response. Clinical Infectious Diseases. 71(15), 858–860.

[99] World Health Organization, 2020. Digital health platform handbook: building a digital information infrastructure (infostructure) for health. Available from: chrome-extension://bnjoienjhhclcabnkbhhfndecoipmcdg/background/jgpdf/layout/index.html?file=https://iris.who.int/bitstream/handle/10665/337449/9789240013728-eng.pdf?sequence=1 (cited 01 May 2024).

[100] Augenstein, J., 2020. Opportunities to expand telehealth use amid the coronavirus pandemic. Health Affairs Forefront. Available from: https://www.healthaffairs.org/content/forefront/opportunities-expand-telehealth-use-amid-coronavirus-pandemic (cited 01 May 2024).

[101] Mbunge, E., Batani, J., Gaobotse, G., et al., 2022. Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019 (COVID-19) pandemic in South Africa: a systematic review. Global Health Journal. 6(2), 102–113.

[102] Salem, M., Elkaseer, A., El-Maddah, I.A., et al., 2022. Non-Invasive Data Acquisition and IoT Solution for Human Vital Signs Monitoring: Applications, Limitations and Future Prospects. Sensors. 22(17), 6625.

[103] Hina, A., Saadeh, W., 2022. Noninvasive blood glucose monitoring systems using near-infrared technology—A review. Sensors. 22(13), 4855.

[104] Misra, S., Deb, P.K., Koppala, N., et al., 2020. S-NAV: Safety-aware IoT navigation tool for avoiding COVID-19 hotspots. IEEE Internet of Things Journal. 8(8), 6975–6982.

[105] N.V., R.K., E., B., J., S.J.P., et al., 2022. Detection and monitoring of the asymptotic COVID-19 patients using IoT devices and sensors. International Journal of Pervasive Computing and Communications. 18(4), 407–418.

[106] Munzert, S., Selb, P., Gohdes, A., et al., 2021. Tracking and promoting the usage of a COVID-19 contact tracing app. Nature Human Behaviour. 5(2), 247–255.

[107] Sabukunze, I.D., Setyohadi, D.B., Sulistyoningsih, M., 2021. Designing an Iot based smart monitoring and emergency alert system for Covid19 patients. Proceedings of the 2021 6th International Conference for Convergence in Technology (I2CT); 02–04 April 2021; Maharashtra, India. pp. 1–5.

[108] Wahle, J.P., Ashok, N., Ruas, T., et al., 2022. Testing the generalization of neural language models for COVID-19 misinformation detection. In: Smits, M. (ed.). Information for a Better World: Shaping the Global Future. iConference 2022. Lecture Notes in Computer Science, vol 13192. Springer: Cham, Switzerland. pp. 381–392.

[109] Shiraishi, J., Li, Q., Appelbaum, D., et al., 2011. Computer-aided diagnosis and artificial intelligence in clinical imaging. Seminars in Nuclear Medicine. 41(6), 449–462.

[110] Santos, M.K., Ferreira, J.R., Wada, D.T., et al., 2019. Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira. 52(6), 387–396.

[111] Ghaderzadeh, M., Aria, M., Asadi, F., 2021. X-ray equipped with artificial intelligence: changing the COVID-19 diagnostic paradigm during the pandemic. BioMed Research International. 2021(1), 9942873.

[112] Ferretti, L., Wymant, C., Kendall, M., et al., 2020. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science. 368(6491), eabb6936.

Downloads

How to Cite

Ali, S. S. A., Koguciuk, A., Jangas, M., Korycki, P., Tylicka, K., & Targos, P. (2025). Advances in Nano-BioElectronics, Robotic Surgery, and Technologies for Medical and Healthcare. Journal of Electronic & Information Systems, 7(1), 1–21. https://doi.org/10.30564/jeis.v7i1.9353