Artificial Intelligence Accelerated Transformation in The Healthcare Industry

Authors

DOI:

https://doi.org/10.55054/ajpp.v3i01.630

Keywords:

Artificial Intelligence, Patient Engagement, Healthcare, Chatbots

Abstract

The healthcare industry was a pioneer in the deployment of artificial intelligence (AI) technology. Due to the nature of the services and the vulnerability of a sizable portion of end users, there has been a significant amount of research and discussion on the concept of artificial intelligence. A mixed-method approach has been used to pinpoint the components of moral AI in the healthcare sector and look into how it affects value creation and market performance. Since AI technology is still developing in India, analysis is conducted in an Indian context. The understanding of how various AI components supported healthcare organisations and deliver better patient-centered care and evidence-based medicine was aided by these in-depth studies and analyses of the patient perspective.

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References

Davenport TH, Hongsermeier TM, Mc Cord KA. Using AI to improve electronic health records. Harvard Business Review; 2018.

Wang F., Casalino L.P., Khullar D. Deep learning in medicine—promise, progress, and challenges. JAMA Intern Med. 2019;179:293–294. [PubMed] [Google Scholar] DOI: https://doi.org/10.1001/jamainternmed.2018.7117

Pham T, Tran T, Phung D, Venkatesh S. DeepCare: a deep dynamic memory model for predictive medicine. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer; 2016. DOI: https://doi.org/10.1007/978-3-319-31750-2_3

A.K. Triantafyllidis, A. Tsanas, Applications of machine learning in real-life digital health interventions: review of the literature, J Med Internet Res, 21 (2019), p. e12286, 10.2196/12286 DOI: https://doi.org/10.2196/12286

Rathore, R. (2022). A Study on Application of Stochastic Queuing Models for Control of Congestion and Crowding. International Journal for Global Academic & Scientific Research, 1(1), 1–6. https://doi.org/10.55938/ijgasr.v1i1.6 DOI: https://doi.org/10.55938/ijgasr.v1i1.6

Sharma, V. (2022). A Study on Data Scaling Methods for Machine Learning. International Journal for Global Academic & Scientific Research, 1(1), 23–33. https://doi.org/10.55938/ijgasr.v1i1.4 DOI: https://doi.org/10.55938/ijgasr.v1i1.4

Rathore, R. (2022). A Review on Study of application of queueing models in Hospital sector. International Journal for Global Academic & Scientific Research, 1(2), 1–6. https://doi.org/10.55938/ijgasr.v1i2.11 DOI: https://doi.org/10.55938/ijgasr.v1i2.11

Kaushik, P. (2022). Role and Application of Artificial Intelligence in Business Analytics: A Critical Evaluation. International Journal for Global Academic & Scientific Research, 1(3), 01–11. https://doi.org/10.55938/ijgasr.v1i3.15 DOI: https://doi.org/10.55938/ijgasr.v1i3.15

L.G. Pee, S.L. Pan, L. Cui, Artificial intelligence in healthcare robots: a social informatics study of knowledge embodiment, J Assoc Inf Sci Technol, 70 (2019), pp. 351-369, 10.1002/asi.24145 DOI: https://doi.org/10.1002/asi.24145

Giansanti, D. Rossi, I. & Monoscalo, L. (2021) “Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders.” Healthcare, vol. 9, no. DOI: https://doi.org/10.3390/healthcare9030331

Kaushik P., Deep Learning and Machine Learning to Diagnose Melanoma; International Journal of Research in Science and Technology, Jan-Mar 2023, Vol 13, Issue 1, 58-72, DOI: http://doi.org/10.37648/ijrst.v13i01.008 DOI: https://doi.org/10.37648/ijrst.v13i01.008

Gillan, C. Milne E, Harnett, N., Purdie, T., Jaffray, D. and Hodges B. (2018) “Professional Implications of Introducing Artificial Intelligence in Healthcare: An Evaluation Using Radiation Medicine as a Testing Ground.” Journal of Medical Imaging and Radiation Sciences, vol. 49 (1) DOI: https://doi.org/10.1016/j.jmir.2018.02.006

Nef T. Vol. 15. 2015. Evaluation of three state-of-the-art classifiers for recognition of activities of daily living from smart home ambient data; pp. 11725–11740. (Sensors (Basel)). [PMC free article] [PubMed] [Google Scholar] DOI: https://doi.org/10.3390/s150511725

Joseph A, Christian B, Abiodun AA, Oyawale F. A review on humanoid robotics in healthcare. In: MATEC Web of Conferences; 2018. https://www.matec-conferences.org/ DOI: https://doi.org/10.1051/matecconf/201815302004

D’Onofrio G. MARIO Project: validation and evidence of service robots for older people with dementia. J Alzheimers Dis. 2019;68:1587–1601. [PubMed] [Google Scholar] DOI: https://doi.org/10.3233/JAD-181165

Kaushik P., Enhanced Cloud Car Parking System Using ML and Advanced Neural Network; International Journal of Research in Science and Technology, Jan-Mar 2023, Vol 13, Issue 1, 73-86, DOI: http://doi.org/10.37648/ijrst.v13i01.009 DOI: https://doi.org/10.37648/ijrst.v13i01.009

Koumakis L., Chatzaki C., Kazantzaki E., Maniadi E., Tsiknakis M. Dementia care frameworks and assistive technologies for their implementation: a review. IEEE Rev Biomed Eng. 2019;12:4– [PubMed] [Google Scholar] DOI: https://doi.org/10.1109/RBME.2019.2892614

Garcia-Alonso J, Fonseca C, editors. Gerontechnology: First International Workshop. In: First international workshop on gerotechnology. Springer; 2018. DOI: https://doi.org/10.1007/978-3-030-16028-9

Rustagi, M. and Goel, N. (2022) “Predictive Analytics: A study of its Advantages and Applications”, IARS’ International Research Journal. Victoria, Australia, 12(01), pp. 60–63. doi: 10.51611/iars.irj.v12i01.2022.192. DOI: https://doi.org/10.51611/iars.irj.v12i01.2022.192

Published

2023-04-10

How to Cite

Kaushik, P. (2023) “Artificial Intelligence Accelerated Transformation in The Healthcare Industry”, Amity Journal of Professional Practices. Florida, USA, 3(01). doi: 10.55054/ajpp.v3i01.630.

Citations