Author(s):
Lakshmidevi Sigatapu, S. Sundar, K. Padmalatha, Sravya. K, D. Ooha, P. Uha Devi
Email(s):
vipwlakshmidevi77@gmail.com
DOI:
10.52711/2231-5713.2023.00039
Address:
Lakshmidevi Sigatapu, S. Sundar, K. Padmalatha, Sravya. K, D. Ooha, P. Uha Devi
Department of Pharmacology, Vijaya Institute of Pharmaceutical Sciences for Women, Enikepadu, Vijayawada, Krishna (Dt), Andhra Pradesh, India.
*Corresponding Author
Published In:
Volume - 13,
Issue - 3,
Year - 2023
ABSTRACT:
Artificial intelligence (AI) has been developing fleetly in recent times in terms of software algorithms, tackle preparation, and operations in a vast number of areas. In this review, we epitomize the rearmost of operations of AI in biomedicine, including complaint diagnostics, living backing, biomedical information processing, and biomedical exploration. The end of this review is to keep track of new scientific accomplishments, to understand the vacuity of technologies, to appreciate the tremendous eventuality of AI in biomedicine, and to give experimenters in affiliated field’s alleviation. It can be asserted that, just like AI itself, the operation of AI in biomedicine is still in its early stage. New progress and improvements will continue to push the frontier and widen the compass of AI operations, and fast developments are envisaged in the near future.AI in healthcare is an umbrella term to describe the application of machine learning (ML) algorithms and other cognitive technologies in medical settings. In the simplest sense, AI is when computers and other machines mimic human cognition, and are capable of learning, thinking, and making decisions or taking actions. Artificial intelligence (AI) is gradationally changing medical practice. With recent progress in digitized data accession, machine literacy and computing structure, AI operations are expanding into areas that were preliminary allowed to be only the fiefdom of mortal experts. In this Review composition, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and epitomize the profitable, legal and counteraccusations of AI in healthcare.
Cite this article:
Lakshmidevi Sigatapu, S. Sundar, K. Padmalatha, Sravya. K, D. Ooha, P. Uha Devi. Artificial Intelligence in Healthcare- An Overview. Asian Journal of Pharmacy and Technology. 2023; 13(3):218-2. doi: 10.52711/2231-5713.2023.00039
Cite(Electronic):
Lakshmidevi Sigatapu, S. Sundar, K. Padmalatha, Sravya. K, D. Ooha, P. Uha Devi. Artificial Intelligence in Healthcare- An Overview. Asian Journal of Pharmacy and Technology. 2023; 13(3):218-2. doi: 10.52711/2231-5713.2023.00039 Available on: https://ajptonline.com/AbstractView.aspx?PID=2023-13-3-12
REFERENCES:
1. Coiera E (1997). Guide to medical informatics, the Internet and telemedicine. Chapman & Hall, Ltd. Taylor and Francis Group3rd Edition18 March 2015, 620.
2. Rashmi R., Nirmal Raj VK. A Study on the Implementation and the Impact of Artificial Intelligence in Banking Processes. Asian Journal of Management. 2021; 12(1):47-54
3. Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification. A conference presentation. The 30th International Conference on Machine Learning, 2013.
4. Davenport TH, Glaser J. Just-in-time delivery comes to knowledge management. Harv Bus Rev. 2002 Jul; 80(7):107-11, 126.
5. Wang H, Zu Q, Chen J, Yang Z, Ahmed MA. Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review. Adv Ther. 2021 Oct; 38(10):5078-5086. https://doi.org/10.1007/s12325-021-01908-2
6. Stewart J, Lu J, Goudie A, Bennamoun M, Sprivulis P, Sanfillipo F, Dwivedi G. Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review. PLoS One. 2021 Aug 24; 16(8):e0252612. doi: 10.1371/journal.pone.0252612
7. Amaq Fadholly, Arif N. M. Ansori, Teguh H. Sucipto. An Overview of Naringin: Potential Anticancer compound of Citrus Fruits. Research J. Pharm. and Tech. 2020; 13(11):5613-5619.
8. Han SS, Moon IJ, Lim W, Suh IS, Lee SY, Na JI, Kim SH, Chang SE. Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. JAMA Dermatol. 2020 Jan 1; 156(1):29-37. doi: 10.1001/jamadermatol.2019.3807.
9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Jun 28; 546(7660):686. doi: 10.1038/nature22985.
10. Försch S, Klauschen F, Hufnagl P, Roth W. Artificial Intelligence in Pathology. DtschArztebl Int. 2021 Mar 26; 118(12):194-204.
11. Pivovarov R, Elhadad N. Automated methods for the summarization of electronic health records. J Am Med Inform Assoc. 2015 Sep; 22(5):938-47. doi: 10.1093/jamia/ocv032.
12. Han SS, Moon IJ, Lim W, Suh IS, Lee SY, Na JI, Kim SH, Chang SE. Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. JAMA Dermatol. 2020 Jan 1; 156(1):29-37.
13. Halima Afroz Lari, Kuhu Vaishnava, Manu K S. Artifical Intelligence in E-commerce: Applications, Implications and Challenges. Asian Journal of Management. 2022; 13(3):235-4.
14. Sahil Mahajan, Heemani Dave, Santosh Bothe, Debarshikar Mahpatra, Sandeep Sonawane, Sanjay Kshirsagar, Santosh Chhajed. Objective Monitoring of Cardiovascular Biomarkers using Artificial Intelligence (AI). Asian Journal of Pharmaceutical Research. 2022; 12(3):229-4.
15. Ajay I. Patel, Pooja K. Khunti, Amit J. Vyas, Ashok B. Patel. Explicating Artificial Intelligence: Applications in Medicine and Pharmacy. Asian Journal of Pharmacy and Technology; 12(4):401-6.
16. Anitha A, Revathi SV, Jeevanantham S, Eliza Godwin E. Intrusion Detection System based on Artificial Intelligence. Int. J. Tech. 2017; 7(1): 20-2.
17. Patel Minesh. A Review on Importance of Artificial Intelligence in Alzheimer’s Disease and it’s Future Outcomes for Alzheimer’s Disease. Research Journal of Pharmacology and Pharmacodynamics.2022; 14(1):13-2.
18. Rohitas Deshmukh, Sujata Kumari, Ranjit K. Harwansh. Inflammatory Bowel Disease: A Snapshot of Current Knowledge. Research J. Pharm. and Tech 2020; 13(2):956-962.
19. S Narendra Kumar, Chetan D M, Lingayya Hiremath, Ajeet Kumar Srivastava, Muralidhara P L, Jyothsana R, Rithika Pravin Iye, Ruchika Pravin Iyer. Molecular Docking studies of THC-HCA on Cancer Receptors. Research Journal of Pharmacy and Technology. 2022; 15(7):3195-9.
20. Neha Bhateja, Nishu Sethi, Shivangi Kaushal. Machine Learning and its role in Diverse Business Systems. Research Journal of Science and Technology. 2021; 13(3):213-7.