Artificial Intelligence in Healthcare Analytics

Krishna Priya Kumar

Industry Academia Cell, National Institute of Food Technology, Entrepreneurship and Management- Thanjavur (NIFTEM-T), Thanjavur- 613005, India


Padmashree Baskaran

Department of Biotechnology, Rajalakshmi Engineering College, Chennai- 602 105, India.


Dr Mahesh Pawar

Associate Professor, Department of Biotechnology, Rajalakshmi Engineering College, Chennai- 602 105, India.


Abstract :

The evolution of lifestyle had eventually turned down the hale and health statement of humans. This led to the gradual upsurge of various diseases in humans irrespective of their age. On the other hand, innumerous healthcare data generated from the wide range of medical sectors challenged the human brains. To combat those human setbacks in data handling there arose the revolutionary solution through machines using mathematical algorithm entitled as Artificial Intelligence (AI). The employment of Artificial Intelligence is traced in medicine pipeline commencing from diagnosis of disease until treatment. AI registered its pivotal role in clinical section by processing (diagnosis, image processing, drug discovery, digital pathology, oncology, mutation identifications) such huge data using algorithm. One of the major subset of AI is Machine Learning (ML), which competes with the humans cognitive skills using higher order algorithms comprising of Artificial Neural Network (ANN). The complicated nature behind the diseases like cancer, diabetes, cardiology, neurological and psychological disorders can also be unveiled with the assist of AI. The processing of healthcare related database executed by AI provides data with high accuracy and clarity. Overall, human intelligence assess their vast health database requirements using the Artificial Intelligence.


[1] Abid Haleem, Javaid, M., & Khan, I. H. 2019. Current status and applications of Artificial Intelligence (AI) in medical field: An overview. Current Medicine Research and Practice, 9(6): 231-237.

[2] Hwang, E. J., Park, S., Jin, K., Kim, J., Choi, S., Lee, J., et al. 2019. Development and validation of a deep learning–based automated detection algorithm for major thoracic Diseases on chest radiographs. JAMA Network Open, advance online publication March 22. doi:10.1001/jamanetworkopen.2019.1095.

[3] Abramoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., Massin, P., Cochener, B., Gain, P., Tang, L., Lamard, M., Moga, D. C., Quellec, G., & Niemeijer, M. 2013. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmology, 131: 351-357.

[4] Abramoff, M. D., Garvin, M. K., & Sonka, M. 2010. Retinal imaging and image analysis. IEEE Reviews in Biomedical Engineering, 3: 169-208.

[5] Alberich-Bayarri, A., Pastor, A. J., Gonzalez, R. L., & Castro, F. G. 2019. How to develop artificial intelligence applications. In E. R. Ranschaert, S. Morozov, P.R. Algra (Ed.), Artificial Intelligence in Medical Imaging; 49-59. Switzerland, Springer.

[6] Alexander, A., Jiang, A., Ferreira, C., & Zurkiya, D. 2020. An intelligent future for medical imaging: A market outlook on artificial intelligence for medical imaging. Journal of the American College of Radiology, 17(1): 165-170.

[7] Barlow, H. 1989. Unsupervised Learning. Neural Computation, 1(3): 295-311.

[8] Bartneck, C., Lutge, C., Wagner, A., & Welsh, S. 2020. An Introduction to Ethics in Robotics and AI, 5-16. Switzerland: Springer.

[9] Bartoletti, I. 2019. AI in healthcare: ethical and privacy challenges. In D. Riano, S. Wilk, & A. Ten Teije (Ed.), Artificial Intelligence in Medicine; Lecture notes in computer science.11526, Cham, Springer.

[10] Basu, J. K., Bhattacharyya, D., & Kim, T. 2010. Use of artificial neural network in pattern recognition. International Journal of Software Engineering and its Applications, 4(2): 23-34.

[11] Bohr, A., & Memarzadeh, K. 2020. The rise of artificial intelligence in healthcare applications. In A. Bohr, & K. Memarzadeh (Ed.), Artificial Intelligence in Healthcare: 25-60. Academic Press.

[12] Borana, J. 2016. Applications of artificial intelligence & associated technologies. Proceeding of International Conference on Emerging Technologies in Engineering, Biomedical, Management and Science, 64-67.

[13] Cavaliere, F., Cioppa, A. D., Marcelli, A., Parziale, A., & Senatore, R. 2020. Parkinson’s disease diagnosis: towards grammar-based explainable artificial intelligence. 2020 IEEE Symposium on Computers and Communications (ISCC), advance online publication July 10. doi: 10.1109/ISCC50000.2020.9219616.

[14] Cavasotto, C., & Phatak, S. 2009. Homology modeling in drug discovery: current trends and applications. Drug Discovery Today, 14(13-14): 676-683.

[15] Celi, L. A., Cellini, J., Charpignon, M-L., Dee, E. C., Dernoncourt, F., Eber, R., et al. 2022. Sources of bias in artificial intelligence that perpetuate healthcare disparities- A global review. PLOS Digit Health, 1(3): e0000022.

[16] Chan, H., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. 2019. Advancing drug discovery via artificial intelligence. Trends in Pharmacological Sciences, 40(8): 592-604.

[17] Chandra Kaushik, A., & Raj, U. 2020. AI-driven drug discovery: A boon against COVID-19? AI Open, 1: 1-4. August 3. doi: 10.1016/j.aiopen.2020.07.001

[18] Ching, T., Zhu, X., & Garmire, L. X. 2018. Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data. PLoS Computer Biology, advance online publication April 10. doi: 10.1371/journal.pcbi.1006076

[19] Colombo, S. 2020. Applications of artificial intelligence in drug delivery and pharmaceutical development. In A. Bohr, & K. Memarzadeh (Ed.), Artificial Intelligence in Healthcare: 85-116. Elsevier.

[20] Currie, G., Hawk, K. E., Rohren, E., Vial, A., & Klein, R. 2019. Machine learning and deep learning in medical imaging: Intelligent imaging. Journal of Medical Imaging and Radiation Sciences, 50(4): 477-487.

[21] Dick, S. 2019. Artificial Intelligence. Harvard Data Science Review, 1(1): 1-9.

[22] Dongare, A. D., Kharde, R. R., & Kachare, A. D. 2012. Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1): 189-194.

[23] Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. 2020. What is AI? Applications of artificial intelligence to dermatology. British Journal of Dermatology, 183(3): 423-430.

[24] El Naqa, I., & Murphy, M. J. 2015. What Is Machine Learning?. In I. El Naqa, R. Li, J. Martin (Ed.), Machine Learning In Radiation Oncology: 3-11. Springer.

[25] Flowers, J. C. 2019. Strong and weak AI: Deweyan considerations. In AAAI Spring Symposium: Towards Conscious AI Systems, 2287: 1-7.

[26] Goodman, D., & Keene, R. 1997. Man versus machine: Kasparov versus deep blue. Journal of the International Computer Games Association, 20(3): 186-187.

[27] Gulshan, V., Peng, L., Coram, M., Stumpe, M., Wu, D., Narayanaswamy, A., et al. 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22): 2402-2410.

[28] Haenlein, M., & Kaplan, A. 2019. A Brief history of AI: On the past, present, and future of artificial intelligence. California Management Review, 61(4): 5-14.

[29] Hillisch, A., Pineda, L. F., & Hilgenfeld, R. 2004. Utility of homology models in the drug discovery process. Drug Discovery Today, 9(15): 659-669.

[30] Ho, C., Soon, D., Caals, K., & Kapur, J. 2019. Governance of automated image analysis and artificial intelligence analytics in healthcare. Clinical Radiology, 74(5): 329-337.

[31] Huang, S., Yang, J., Fong, S., & Zhao, Q. 2020. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Letters, 471: 61-71.

[32] Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., et al. 2018. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5): 1122-1131.

[33] Khan, Z. F., & Alotaibi, S. R. 2020. Applications of artificial intelligence and big data analytics in m-health: A healthcare system perspective. Journal of Healthcare Engineering, advance online publication September 1.

[34] Kim, Y. S., Sohn, S. Y., & Yoon, C. N. 2003. Screening test data analysis for liver disease prediction model using growth curve. Biomedicine and Pharmacotherapy, 57:482-488.

[35] Klambauer, G., Hochreiter, S., & Rarey, M. 2019. Machine learning in drug discovery. Journal of Chemical Information and Modeling, 59(3): 945-946.

[36] Kumar, P., Yadav, A. K. S., & Singh, A. 2021. Prospective of artificial intelligence: Emerging trends in modern biosciences research. IOP Conference Series: Materials Science and Engineering, 1020(1): 012008.

[37] Kyrarini, M., Lygerakis, F., Rajavenkatanarayanan, A., Sevastopoulos, C., Nambiappan, H. R., Chaitanya, K. K., et al. 2021. A survey of robots in healthcare. Technologies, 9(1): 8. doi: 10.3390/technologies9010008

[38] Lee, D., & Yoon, S. N. 2021. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 18(1):271.

[39] Lin, R. H. 2009. An intelligent model for liver disease diagnosis. Artificial Intelligence in Medicine, 47(1): 53-62.

[40] Mayo, R. C., Kent, D., Sen, L. C., Kapoor, M., Leung, J. W. T., & Watanabe, A. T. 2019. Reduction of false-positive markings on mammograms: a retrospective comparison study using an artificial intelligence-based CAD. Journal of Digital Imaging, 32(4): 618-624.

[41] Mishra, M., & Srivastava, M. 2014. A view of artificial neural network. International Conference on Advances in Engineering & Technology Research (ICAETR-2014), 1-3.

[42] Mohanty, S., Harun Ai Rashid, M., Mridul, M., Mohanty, C., & Swayamsiddha, S. 2020. Application of artificial intelligence in COVID-19 drug repurposing. Diabetes & Metabolic Syndrome, 14(5): 1027-1031.

[43] Nakano, H., Okamoto, Y., Nakabayashi, H., Takamatsu, S., & Tsujii, H. 1996. Application of neural network to the interpretation of laboratory data for the diagnosis of two forms of chronic active hepatitis. International Hepatology Communications, 5(3): 160-165.

[44] Nguyen, T. T., Larrivee, N., Lee, A., Bilaniuk, O., & Durand, R. 2021. Use of artificial intelligence in dentistry: current clinical trends and research advances. Journal of Canadian Dental Association, 87: l7.

[45] Pacis, D. M. M., Subido, E. D. C., & Bugtai, N. T. 2018. Trends in telemedicine utilizing artificial intelligence. AIP Conference Proceedings, 1933(1): 040009.

[46] Panesar, A. 2021. Machine Learning and AI for Healthcare. Big data for improved health outcomes, UK: Apress.

[47] Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. 2021. Artificial intelligence in drug discovery and development. Drug discovery today, 26(1): 80-93.

[48] Pennington, K. L., & DeAngelis, M. M., 2016. Epidemiology of age-related macular degeneration (AMD): associations with cardiovascular disease phenotypes and lipid factors. Eye and Vision, 3: 34.

[49] Ranschaert, E.R., Duerinckx, A.J., Algra, P., Kotter, E., Kortman, H., Morozov, S. (2019). Advantages, challenges, and risks of artificial intelligence for radiologists. In E. Ranschaert, S. Morozov, P. Algra (Ed.), Artificial Intelligence in Medical Imaging: 329-346. Springer, Cham.

[50] Reddy, S., Fox, J., & Purohit, M. P. 2018. Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1): 22-28.

[51] Rong, G., Mendez, A., Bou Assi, E., Zhao, B., & Sawan, M. 2020. Artificial intelligence in healthcare: review and prediction case studies. Engineering, 6(3): 291-301.

[52] Russell, S. J., & Norvig, P. 2010. Artificial Intelligence: A modern approach. France: Pearson.

[53] Safdar, S., Zafar, S., Zafar, N., & Khan, N. F. 2017. Machine learning based decision support systems (DSS) for heart disease diagnosis: a review. Artificial Intelligence Review, 50(4): 597-623.

[54] Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Ashqar, B. A. M. 2020. Covid-19 detection using artificial intelligence. International Journal of Academic Engineering Research (IJAER), 4(3): 18-25.

[55] Shaheen, M. Y. 2021. Applications of Artificial Intelligence (AI) in healthcare: A review. Scienceopen Preprints, advance online publication September 25. doi:10.14293/S2199-1006.1.SOR-.PPVRY8K.v1

[56] Sinha, S., & Vohora, D. 2018. Drug discovery and development: An overview. In V. Divya & S. Gursharan (Ed.), Pharmaceutical Medicine and Translational Clinical Research: 19-32. Academic Press, Elsevier.

[57] Ting, D. S. W., Liu, Y., Burlina, P., Xu, X., Bressler, N. M., & Wong, T. Y. 2018. AI for medical imaging goes deep. Nature Medicine, 24(5): 539-540.

[58] Tran, V. -T., Riveros, C., & Ravaud, P. 2019. Patients’ views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort. NPJ Digital Medicine, advance online publication June 14. doi:10.1038/s41746-019-0132-y

[59] Trinh, M., Ghassibi, M., & Lieberman, R. 2021. Artificial Intelligence in Retina. Advances in Ophthalmology and Optometry, 6: 175-185.

[60] Vyas, M., Thakur, S., Riyaz, B., Bansal, K., Tomar, B., & Mishra, M. 2018. Artificial intelligence: The beginning of a new era in pharmacy profession. Asian Journal of Pharmaceutics, 12(2): 72-76.

[61] Wang, S. 2003. Artificial Neural Network. Interdisciplinary Computing In Java Programming, 81-100.

[62] Wani, S. U. D., Khan, N. A., Thakur, G., Gautam, S. P., Ali, M., Alam, P., Alshehri, S., Ghoneim, M. M., Shakeel, F. 2022. Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce. Healthcare, 10: 608.

[63] Wu, Y., & Feng, J. 2017. Development and Application of Artificial Neural Network. Wireless Personal Communications, 102(2): 1645-1656.

[64] Xie, Y., Chen, M., Kao, D., Gao, G., & Chen, X. 2020. CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1-13.

[65] Yu, K. -H., Beam, A. L., & Kohane, I. S. 2018. Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10): 719-731.

[66] Zang, Q., Mansouri, K., Williams, A. J., Judson, R. S., Allen, D. G., Casey, W. M., & Kleinstreuer, N. C. 2017. In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning. Journal of Chemical Information and Modeling, 57(1): 36-49.

© The Author(s), under exclusive license to Technoarete Publishers 2022
  • ISBN - 978-93-92104-06-0
  • Instant PDF download
  • Readable on all devices