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Smart Healthcare: The Role of AI in Medical Systems Integration

I.V. Dwaraka Srihith

Abstract


This paper delves into how healthcare stakeholders interact with intelligent medical systems and how these systems affect healthcare delivery. Sparked by the growing interest and investment in intelligent agents such as those developed by Siemens, the study looks at the pros and cons of these technologies, along with the ethical issues they raise. It also explores the socio-technical implications of integrating intelligent systems in healthcare, comparing Convolutional Neural Networks with other cutting-edge methods. Additionally, the paper highlights the critical role of decision-makers in assessing the attitudes of medical personnel towards these systems before their final implementation, stressing the importance of stakeholder feedback for successful technology integration.


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References


Berwick, D.M.: What ‘patient-centered’should mean: confessions of an extremist: a seasoned clinician and expert fears the loss of his humanity if he should become a patient. Health Aff. 28(Suppl1), w555–w565 (2009)

Lamas, E., Salinas, R., Coquedano, C., Simon, M.P., Bousquet, C., Ferrer, M., Zorrilla, S.: The meaning of patient empowerment in the digital age: the role of online patient-communities. Stud. Health Technol. Inform. 244, 43–7 (2017)

Sittig, D.F., Singh, H.: A new socio-technical model for studying health information technology in complex adaptive healthcare systems. In: Cognitive informatics for biomedicine, pp. 59–80. Springer (2015)

Eccles, D.W., Groth, P.T.: Problem solving systems theory: implications for the design of socio-technological systems. Technol. Instruct. Cognit. Learn. 3(3/4), 323 (2006)

Ramos, G., Boratto, L., Caleiro, C.: On the negative impact of social influence in recommender systems: a study of bribery in collaborative hybrid algorithms. Inform. Proces. Manag. 57(2), 102058 (2020)

Paraschakis, D.: Recommender systems from an industrial and ethical perspective. In: Proceedings of the 10th ACM conference on recommender systems, pp. 463–466 (2016)

Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender systems handbook, pp. 217–

Hofmann, B.: The myth of technology in health care. Sci. Eng. Eth. 8(1), 17–29 (2002)

FDA: U.s. food and drug administration: proposed regulatory framework for modifications to artificial intelligence/machine learning (ai/ml)-based software as medical device (samd)

Musco, C., Musco, C., Tsourakakis, C.E.: Minimizing polarization and disagreement in social networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 369–378 (2018)


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