TY - JOUR
T1 - Utilization of Artificial Intelligence in Disease Prevention
T2 - Diagnosis, Treatment, and Implications for the Healthcare Workforce
AU - Wani, Shahid Ud Din
AU - Khan, Nisar Ahmad
AU - Thakur, Gaurav
AU - Gautam, Surya Prakash
AU - Ali, Mohammad
AU - Alam, Prawez
AU - Alshehri, Sultan
AU - Ghoneim, Mohammed M.
AU - Shakeel, Faiyaz
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/4
Y1 - 2022/4
N2 - Artificial intelligence (AI) has been described as one of the extremely effective and promis-ing scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, as well as regulatory procedures within providers, payers, and pharmaceutical companies, may be transformed by these innovations. As a result, the purpose of this review is to identify the potential machine learning applications in the field of infectious diseases and the general healthcare system. The literature on this topic was extracted from various databases, such as Google, Google Scholar, Pubmed, Scopus, and Web of Science. The articles having important information were selected for this review. The most challenging task for AI in such healthcare sectors is to sustain its adoption in daily clinical practice, regardless of whether the programs are scalable enough to be useful. Based on the summarized data, it has been concluded that AI can assist healthcare staff in expanding their knowledge, allowing them to spend more time providing direct patient care and reducing weariness. Overall, we might conclude that the future of “conventional medicine” is closer than we realize, with patients seeing a computer first and subsequently a doctor.
AB - Artificial intelligence (AI) has been described as one of the extremely effective and promis-ing scientific tools available to mankind. AI and its associated innovations are becoming more popular in industry and culture, and they are starting to show up in healthcare. Numerous facets of healthcare, as well as regulatory procedures within providers, payers, and pharmaceutical companies, may be transformed by these innovations. As a result, the purpose of this review is to identify the potential machine learning applications in the field of infectious diseases and the general healthcare system. The literature on this topic was extracted from various databases, such as Google, Google Scholar, Pubmed, Scopus, and Web of Science. The articles having important information were selected for this review. The most challenging task for AI in such healthcare sectors is to sustain its adoption in daily clinical practice, regardless of whether the programs are scalable enough to be useful. Based on the summarized data, it has been concluded that AI can assist healthcare staff in expanding their knowledge, allowing them to spend more time providing direct patient care and reducing weariness. Overall, we might conclude that the future of “conventional medicine” is closer than we realize, with patients seeing a computer first and subsequently a doctor.
KW - artificial intelligence
KW - computer methods
KW - disease treatment
KW - healthcare
KW - infectious disease
UR - http://www.scopus.com/inward/record.url?scp=85127651691&partnerID=8YFLogxK
U2 - 10.3390/healthcare10040608
DO - 10.3390/healthcare10040608
M3 - Review article
AN - SCOPUS:85127651691
SN - 2227-9032
VL - 10
JO - Healthcare (Switzerland)
JF - Healthcare (Switzerland)
IS - 4
M1 - 608
ER -
