AI in Healthcare 5.0: Opportunities and Challenges
DOI:
https://doi.org/10.59890/ijeps.v1i4.910Keywords:
Precision Remedy, Interoperability, Moral AI, Data Privateness, Regulatory ComplianceAbstract
AI in Healthcare five.zero represents the next frontier inside the evolution of artificial intelligence within the healthcare enterprise. This paradigm leverages advanced technologies inclusive of quantum computing, augmented reality, and biotechnology to offer exceptional possibilities and confront unique challenges. possibilities include enhanced affected person care through personalised remedy plans, quicker drug discovery, and optimized useful resource allocation. demanding situations encompass moral issues associated with statistics privacy, bias in AI algorithms, and the need for regulatory frameworks to make sure accountable AI deployment. moreover, the integration of AI into healthcare workflows demands substantial investment in infrastructure and team of workers education
References
Challenges to implementing artificial intelligence in healthcare: a qualitative interview observe with healthcare leaders in Sweden(2022).
Blockchain for enterprise 4.zero: A comprehensive evaluation, IEEE(2022).
Sensors and healthcare five.zero: transformative shift in digital care via emerging digital fitness technologies(2021)
In the direction of actual-time and efficient cardiovascular tracking for COVID- 19 sufferers via 5G-enabled wearable medical devices: a deep learning approach(2021)
The net of factors in Healthcare: capability programs and challenges (2016).
Z. Che, S. Purushotham, R. Khemani, and Y. Liu, ‘‘Interpretable deep fashions for ICU final results prediction,’’ in Proc. AMIA Annu.Symp. Bethesda, MD, united states: American scientific Informatics affiliation, 2016, p. 371.
Y. Ming, H. Qu, and E. Bertini, ‘‘RuleMatrix: Visualizing and recognize ing classifiers with regulations,’’ IEEE Trans. Vis. Comput. photographs, vol. 25, no. 1, pp. 342–352, Jan. 2019.
C. Xiao, T. Ma, A. B. Dieng, D. M. Blei, and F. Wang, ‘‘Readmission prediction thru deep contextual embedding of clinical principles,’’ PLoS ONE, vol. thirteen, no. four, Apr. 2018, art.no. e0195024.
L. Pan, G. Liu, X. Mao, H. Li, J. Zhang, H. Liang, and X. Li, ‘‘Development of prediction models the usage of system getting to know algorithms for women with suspected vital precocious puberty: Retrospective have a look at,’’ JMIR Med. Informat., vol. 7, no. 1, Feb. 2019, artwork. no. e11728.
S. Ghafouri-Fard, M. Taheri, M. D. Omrani, A. Daaee, H. Mohammad Rahimi, and H. Kazazi, ‘‘utility of single-nucleotide polymorphisms in the diagnosis of autism spectrum issues: A initial look at with synthetic neural networks,’’ J. Mol. Neurosci., vol. 68, no. 4, pp. 515–521, Aug. 2019.
M. S. Kovalev, L. V. Utkin, and E. M. Kasimov, ‘‘SurvLIME: a method for explaining gadget getting to know survival fashions,’’ Knowl.-based totally Syst., vol. 203, Sep. 2020, art. no. 106164.
A. Meldo, L. Utkin, M. Kovalev, and E. Kasimov, ‘‘The herbal language clarification algorithms for the lung most cancers computer-aided analysis device,’’ Artif. Intell.Med., vol. 108, Aug. 2020, artwork.no. 101952.
C. Panigutti, A. Perotti, and D. Pedreschi, ‘‘doctor XAI: An ontology primarily based method to black- box sequential statistics classification explanations,’’ in Proc. Conf. fairness, responsibility, Transparency. the big apple, new york, united states: association for Computing machinery, Jan. 2020, pp. 629–639.
J. Zhang, k. Kowsari, J. H. Harrison, J. M. Lobo, and L. E. Barnes, ‘‘Patient2Vec: a personalized interpretable deep representation of the longitudinal electronic health record,’’ IEEE access, vol. 6, pp. 65333– 65346, 2018.
E. Choi, M. T. Bahadori, J. sun, J. Kulas, A. Schuetz, and W. Stewart, ‘‘retain: An interpretable predictive model for healthcare the usage of reverse time attention mechanism,’’ in Proc. Adv. Neural Inf. procedure. Syst., vol. 29, 2016, pp. 1–9.
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