Next Generation IoMT enabled Smart HealthCare using Machine Learning Techniques
Keywords:
Artificial Intelligence (AI), Internet of Things ( IoT), Medical System, Healthcare, Opportunities , ChallengesAbstract
AI Enabled Internet of Medical Things (AIEIOMT) are playing a very crucial character in medical industry to increase exactness, productivity, and reliability of the electronics instruments. Recent advances and development in conceptual and design science , Technology and connectivity have led to the emergence of Artificial Intelligence and Internet of Things ( IoT) applications in many industries and with an emerging field with great development outlet potential in future years . Nowadays scientists are focusing to establish a digital-physical healthcare system by the interconnections of available medical resources, various healthcare services and digitally smart devices . This paper studies the impart of Technologies such as IoT and AI in healthcare. This analysis further reveals that the application of these technologies in disease diagnosis, forecasting, detection, and treatment, wearables and connectivity, patient care , sensor networks, identified gaps and future research directions related to technical design, acceptance, regulations for data security and privacy and systems efficacy and safety .The relevant impact factors in the blueprint and development of magnified healthcare systems are the related Research fields Artificial intelligence ( AI) , Big Data ( BD), and Internet of Things ( IoT). In the paper the concentration is focused on AI in IoT and healthcare system, which includes utilization and execution of AI methodologies many disciplines of healthcare. This paper work exhibits the principal areas of AI methodology in disease detection, prediction, medicine, robotic surgery, and personalized treatment. Furthermore AIEIOMT addresses numerous heath conditions like diabetes, activated parameters of biophysical supervisions along with subsistence system in decision making. As IoT has various converging domain but our focusing domain is the contribution of IOT in healthcare fields. The Internet of Medical Things has convergence with several domains but our research contribution correlated to AI and IoT in healthcare, previous contribution, ultra-modern contributions in Covid19 Epidemic, Opportunities, applications and subsequent challenges in terms of medical services in healthcare industry. AI Enabled Internet of Medical Things depute the medically interconnected communication devices and their integration in health network towards patient's health improvement. Even so, due to critical behavior of health-related systems, AIEIOMT still facing various challenges specially in terms of security, safety and reliability. In this literature we represent the comprehensive scientific research, new contributions in order to improve AIEIOMT by the usage of traditional methodologies furnished by cyber-physical systems. We outline remarkable experimental and realistic applications of standardization of medical devices for patient itself, guardians of patient, doctors, nurses and healthy people too. We also try to recognize Unexposed research oriented direction and trending potentials to solve uncharted research complications.
References
- Internet of Things (IoT): number of connected devices worldwide from 2012 to 2020 (in billions). https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/
- Institute of health metrics and evaluation, 2015 http://www.healthdata.org/pakistan
- Kaur, P., Kumar, R., Kumar, M.: A healthcare monitoring system using random forest and internet of things (IoT). Multimed Tools Appl. 78, 19905–19916 (2019). https://doi.org/10.1007/s110420197327-8
- Manogaran, G., Chilamkurti, N., Hsu, C.-H.: Emerging trends, issues, and challenges in internet of medical things and wireless networks. Pers. Ubiquit. Comput. (2018). https://doi.org/10 1007/s00779018-1178-637. Kaur, P., Sharma, N., Singh
- Cielen, D., Meysman, A., Ali, M.: Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools. Manning Publications, Shelter Island, NY (2016)
- Attia, Z.I., Kapa, S., Lopez-Jimenez, F., McKie, P.M., Ladewig, D.J., Satam, G., Pellikka,P.A., EnriquezSarano, M., Noseworthy, P.A., Munger, T.M., Asirvatham, S.J., Scott, C.G., Carter, R.E., Friedman, P.A.: Screening for cardiac contractile dysfunction using an artificial intelligence– enabled electrocardiogram. Nat. Med. 25, 70–74 (2019). https://doi.org/10.1038/s415910180240-2
- Allouzi, M.A., Khan, J.I.: Soter: trust discovery framework for internet of medical things (IoMT). In: 2019 IEEE 20th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), pp. 1–9. IEEE, Washington, DC, USA (2019). https://doi.org/10.1109/WoWMoM.2019.8792971
- Yanambaka, V.P., Mohanty, S.P., Kougianos, E., Puthal, D.: PMsec: physical unclonable functionbased robust and lightweight authentication in the internet of medical things. IEEE Trans. Consumer Electron. 65, 388–397 (2019). https://doi.org/10.1109/TCE.2019.2926192
- Manogaran, G., Varatharajan, R., Priyan, M.K.: Hybrid recommendation system for heart disease diagnosisbased on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimed Tools Appl. 77, 4379–4399 (2018). https://doi.org/10.1007/s11042-017-5515-y
- Jin, Y., Yu, H., Zhang, Y., Pan, N., Guizani, M.: Predictive analysis in outpatients assisted by the internet of medical things. Future Gener. Comput. Syst. 98, 219–226 (2019). https://doi.org/10.1016/j.future.2019.01.019
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