Role of Artificial Intelligence in the Health Care Field: Sustainable Development Goals & Building Futures for ViksitBharat@2047
DOI:
https://doi.org/10.58213/vidhyayana.v10isi3.2223Keywords:
Artificial Intelligence (AI), Accessibility, Affordability, Healthcare delivery, Dystopian, UtopianAbstract
As India envisions becoming a developed nation by 2047, In the healthcare industry, artificial intelligence (AI) is emerging as a disruptive force that can help with issues of quality, price, and accessibility. By overcoming the gaps in healthcare delivery between rural and urban areas, The diagnosis, treatment, and care of patients might all be revolutionized by artificial intelligence (AI). AI-driven technologies comprise machine learning, natural language processing, and predictive analytics, which may help with personalized treatment plans, early disease detection, and efficient hospital management. Innovations like telemedicine, AI-powered wearable devices, and mobile health applications enhance accessibility, particularly in remote and underserved areas. Furthermore, AI can optimize drug discovery processes, reduce costs, and accelerate the development of affordable medicines. In a future-ready healthcare system, AI can streamline operations, improve resource allocation, and ensure equitable care However, resolving ethical challenges, data privacy issues, and the need for a competent workforce are necessary to realize this goal. With strategic implementation, AI can significantly contribute to creating a robust, inclusive, and sustainable healthcare ecosystem in Viksit Bharat 2047, ensuring health and well-being for all citizens To advance and even lead the world, Bharat has its own medical ideology, medical scientists, and medical history. This was demonstrated by Bharat during the COVID-19 pandemic. Nevertheless, The medical future of Bharat is full with opportunities and challenges. This study looks at the state of applications of Technologies based on artificial intelligence (AI) today and how they affect the healthcare industry. This study conducted a thorough literature analysis and examined a number of actual instances of AI use in healthcare. According to the research, big hospitals are now implementing AI-powered tools to assist medical professionals in identifying and treating a variety of diseases. Additionally, hospital management and nursing operations are becoming more efficient thanks to AI technologies. Although AI is being avidly adopted by professionals, its applications present both dystopian (problems to be solved) and utopian (new prospects).
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References
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Escalante, H.; Montes-y-Gómez, M.; González, J.; Gómez-Gil, P.; Altamirano, L.; Reyes, C.; Reta, C.; Rosales, A. Acute Leukemia Classification by Ensemble Particle Swarm Model Selection. Artif. Intell. Med. 2012, 55, 163–175. [Google Scholar] [CrossRef] [PubMed]
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Kermany, D.; Goldbaum, M.; Cai, W.; Lewis, M.; Xia, H.; Zhang, K. Identifying Medical Diagnoses and Treatable Diseases by Mage-based Deep Learning. Cell 2018, 172, 1122–1131. [Google Scholar] [CrossRef] [PubMed]
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Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410.https://doi.org/10.1001/jama.2016.17216
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Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., & Tang, W. W. (2020). Deep learning for cardiovascular medicine: A practical primer.
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Sharma, S., Sharma, D., & Kaushik, P. (2021). Artificial intelligence applications in Indian healthcare: Opportunities and challenges. AI & Society, 36(4), 1021–1034. https://doi.org/10.1007/s00146-020-01104-4
Adams, Scott J.; Henderson, Robert D. E.; Yi, Xin; Babyn, Paul (February 2021). "Artificial Intelligence Solutions for Analysis of X-ray Images". Canadian Association of Radiologists Journal. 72 (1): 60– 72. doi:10.1177/0846537120941671. ISSN 0846-5371. PMID 32757950. S2CID 221036912.
Mullainathan S, Obermeyer Z (May 2022). "Solving medicine's data bottleneck: Nightingale Open Science". Nature Medicine. 28 (5): 897–899. Doi: 10.1038/s41591-022-018044.PMID 35534570S2CID.
Coiera E (1997). Guide to medical informatics, the Internet and telemedicine. Chapman & Hall, Ltd.
Petersson L, Larsson I, Nygren JM, Nilsen P, Neher M, Reed JE, et al. (July 2022). "Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden". BMC Health Services Research. 22 (1): 850. doi:10.1186/s12913-022-08215-8. PMC 9250210. PMID 35778736.
Aabo, G. Being Patient-Centric in a Digitizing World; McKinsey and Company: New York, NY, USA, 2016. [Google Scholar]
Lee, S.; Lee, D. Healthcare wearable devices: An analysis of key factors for continuous use intention. Serv. Bus. 2020, 14, 503–531. [Google Scholar] [CrossRef]
Lee, D. Strategies for Technology-driven Service Encounters for Patient Experience Satisfaction in Hospitals. Technol. Forecast. Soc. Chang. 2018, 137, 118–127. [Google Scholar] [CrossRef]
Lee, D. Effects of Key Value Co-creation Elements in the Healthcare System: Focusing on Technology Applications. Serv. Bus. 2019, 13, 389–417. [Google Scholar] [CrossRef]
Lee, S.; Lim, S. Living Innovation: From Value Creation to the Greater Good; Emerald Publishing Limited: Bingley, UK, 2018. [Google Scholar]
Aruba. IoT Heading for Mass Adoption by 2019 Driven by Better-than-Expected Business Results. 2017. Available online: https://news.arubanetworks.com/press-release/arubanetworks/iot-heading-mass-adoption-2019-driven-better-expected-business-results (accessed on 10 April 2020).
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Ramesh, A.; Kambhampati, C.; Monson, J.; Drew, P. Artificial Intelligence in Medicine. Ann. R. Coll. Surg. Engl. 2004, 86, 334–338. [Google Scholar] [CrossRef] [Green Version]
Safavi, K.; Kalis, B. How AI can Change the Future of Health Care. Harv. Bus. Rev. 2019. Available online: https://hbr.org/webinar/2019/02/how-ai-can-change-the-future-of-health-care (accessed on 15 June 2020).
RSNA Newsroom. 2018. Available online: https://press.rsna.org/timssnet/media/rsna/-newsroom2018.cfm (accessed on 5 May 2020).
Mesko, B. Artificial Intelligence Will Redesign Healthcare. 2016. Available online: https://www.linkedin.com/pulse/artificial-intelligence-redesign-healthcare-bertalan-mesk%C3%B3-md-phd (accessed on 10 May 2020).
Liang, H.; Tsui, B.; Ni, H.; Valentim, C.; Baxter, S.; Liu, G. Evaluation and Accurate Diagnoses of Pediatric Diseases Using Artificial Intelligence. Nat. Med. 2019, 25, 433–438. [Google Scholar] [CrossRef]
Accenture. AI: An Engine for Growth. 2018. Available online: https://www.accenture.com/fi-en/insight-artificial-intelligence-healthcare (accessed on 25 July 2020).
Amato, F.; López, A.; Peña-Méndez, E.; Vaňhara, P.; Hampl, A.; Havel, J. Artificial Neural Networks in Medical Diagnosis. J. Appl. Biomed. 2013, 11, 47–58. [Google Scholar] [CrossRef]
Bennett, C.; Hauser, K. Artificial Intelligence Framework for Simulating Clinical Decision-Making: AMarkov Decision Process Approach. Artif. Intell. Med. 2013, 57, 9–19. [Google Scholar] [CrossRef] [Green Version]
Dilsizian, S.; Siegel, E. Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment. Curr. Cardiol. Rep. 2014, 16, 441. [Google Scholar] [CrossRef]
Shiraishi, J.; Li, Q.; Appelbaum, D.; Doi, K. Computer-aided Diagnosis and Artificial Intelligence in Clinical Imaging. Semin. Nucl. Med. 2011, 41, 449–462. [Google Scholar] [CrossRef]
Esteva, A.; Kuprel, B.; Novoa, R.; Ko, J.; Swetter, S.; Blau, H.; Thrun, S. Dermatologist-level Classification of Skin cancer with Deep Neural Networks. Nature 2017, 542, 115–118. [Google Scholar] [CrossRef] [PubMed]
Rigby, M. Ethical Dimensions of Using Artificial Intelligence in Healthcare. AMA J. Ethics 2019, 21, E121–E124. [Google Scholar]
Luxton, D. Artificial Intelligence in Psychological Practice: Current and Future Applications and Implications. Prof. Psychol. Res. Pract. 2014, 45, 332–339. [Google Scholar] [CrossRef] [Green Version]
Miyashita, M.; Brady, M. The Health Care Benefits of Combining Wearables and AI. Harv. Bus. Rev. 2019. Available online: https://hbr.org/2019/05/the-health-care-benefits-of-combining-wearables-and-ai (accessed on 18 June 2020).
Abomhara, M.; Køien, G. Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks. J. Cyber Secur. 2015, 4, 65–88. [Google Scholar] [CrossRef]
Evolution of Artificial Intelligence. Available online: https://towardsdatascience.com/-artificial-intelligence-vs-machine-learning-vs-deep-learning-2210ba8cc4ac (accessed on 22 December 2020).
Lee, S.; Lee, D.; Kim, Y. The Quality Management Ecosystem for Predictive Maintenance in the Industry 4.0 Era. Int. J. Qual. Innov. 2019, 5, 1–11. [Google Scholar] [CrossRef]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning. Nature 2015, 251, 434–444. [Google Scholar] [CrossRef]
Chosun Biz. 24 November 2018. Available online: http://biz.chosun.com/site/-data/html_dir/2018/11/23/2018112302467.html (accessed on 3 July 2020).
Palanica, A.; Flaschner, P.; Thommandram, A.; Li, M.; Fossat, Y. Physicians’ Perceptions of Chatbots in Health Care: Cross-sectional Web-based Survey. J. Med Internet Res. 2019, 21, e12887. [Google Scholar] [CrossRef]
Forbes. The Hospital Will See Vou Now. 2019. Available online: https://www.forbes.com/sites/insights-intelai/2019/02/11/the-hospital-will-see-you-now/#4c9b42ae408a (accessed on 15 November 2020).
Reflecting the Past, Shaping the Future: Making AI Work for International Development. Available online: https://www.usaid.gov/sites/default/files/documents/15396/AI-ML-in-Development.pdf (accessed on 15 November 2020).
Coventry, L.; Branley, D. Cybersecurity in Healthcare: A Narrative Review of Trends, Threats and Ways Forward. Maturitas 2018, 113, 48–52. [Google Scholar] [CrossRef] [PubMed]
Musa, M. Opinion: Rise of the Robot Radiologists. Science. 2018. Available online: https://www.the-scientist.com/news-opinion/opinion--rise-of-the-robot-radiologists-64356 (accessed on 15 November 2020).
IBM News. Watson to Gain Ability to “See” with Planned $1B Acquisition of Merge Healthcare. 2015. Available online: https://www-03.ibm.com/press/us/en/pressrelease/-47435.wss (accessed on 15 November 2020).
Dawes, T.; de Marvao, A.; Shi, W.; Fletcher, T.; Watson, G.; Wharton, J.; Rhodes, C.; Howard, L.; Gibbs, J.; Rueckert, D.; et al. Machine Learning of Three-Dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MRI Imaging Study. Radiology 2017, 283, 381–390. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Guo, J.; Li, B. The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Health Equity 2018, 2, 174–181. [Google Scholar] [CrossRef] [Green Version]
Pesapane, F.; Codari, M.; Sardanelli, F. Artificial Intelligence in Medical Imaging: Threat or Opportunity? Radiologists again at the Forefront of Innovation in Medicine. Eur. Radiol. Exp. 2018, 2, 1–10. [Google Scholar] [CrossRef] [Green Version]
Michaelides, A.; Raby, C.; Wood, M.; Farr, K.; Toro-Ramos, T. Weight Loss Efficacy of a Novel Mobile Diabetes Prevention Program Delivery Platform with Human Ccoaching. BMJ Open Diabetes Res. Care 2016, 4, 1–5. [Google Scholar] [CrossRef] [Green Version]
Noom. Available online: https://web.noom.com (accessed on 15 November 2020).
Kolovos, P.; Kaitelidou, D.; Lemonidou, C.; Sachlas, A.; Sourtzi, P. Patients’ Perceptions and Preferences of Participation in Nursing care. J. Res. Nurs. 2016, 21, 290–303. [Google Scholar] [CrossRef]
Tobiano, G.; Bucknall, T.; Marshall, A.; Guinane, J.; Chaboyer, W. Patients’ Perceptions of Participation in Nursing Care on Medical Wards. Scand. J. Caring Sci. 2016, 30, 260–270. [Google Scholar] [CrossRef] [Green Version]
Boulding, W.; Glickman, S.; Manary, M.; Schulman, K.; Staelin, R. Relationship between Patient Satisfaction with Inpatient Care and Hospital Readmission within 30 Days. Am. J. Manag. Care 2011, 17, 41–48. [Google Scholar] [PubMed]
Sato, M.; Morimoto, K.; Kajihara, S.; Tateishi, R.; Shiina, S.; Koike, K.; Yatomi, Y. Machine-Learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma. Sci. Rep. 2019, 9, 7704. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Escalante, H.; Montes-y-Gómez, M.; González, J.; Gómez-Gil, P.; Altamirano, L.; Reyes, C.; Reta, C.; Rosales, A. Acute Leukemia Classification by Ensemble Particle Swarm Model Selection. Artif. Intell. Med. 2012, 55, 163–175. [Google Scholar] [CrossRef] [PubMed]
Manyika, J. Technology, Jobs, and the Future of Work; McKinsey Global Institute; McKinsey & Company: New York, NY, USA, 2017. [Google Scholar]
Purdy, S.; Wong, P.; Harris, P. Stop the Presses! KPMG: Amsterdam, The Netherlands, 2016; pp. 1–20. [Google Scholar]
Can AI Address Health Care’s Red-Tape Problem? Available online: https://hbr.org/2018/11/can-ai-address-health-cares-red-tape-problem (accessed on 15 November 2020).
Kermany, D.; Goldbaum, M.; Cai, W.; Lewis, M.; Xia, H.; Zhang, K. Identifying Medical Diagnoses and Treatable Diseases by Mage-based Deep Learning. Cell 2018, 172, 1122–1131. [Google Scholar] [CrossRef] [PubMed]
ABI Research. New Report Identifies Leading AI Applications for Healthcare. 2018. Available online: https://www.abiresearch.com/press/ai-save-healthcare-sector-us52-billion-2021/ (accessed on 15 August 2020).
Das, S.; Kant, K.; Zhang, N. Handbook on Securing Cyber-Physical Critical Infrastructure; Morgan Kaufmann: Waltham, MA, USA, 2012. [Google Scholar]
Ministry of Health and Family Welfare. (2022). Ayushman Bharat Digital Mission (ABDM). Government of India. Retrieved from ABDM WebsiteHealthIT.gov. (2018).
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410.https://doi.org/10.1001/jama.2016.17216
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510
Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., & Tang, W. W. (2020). Deep learning for cardiovascular medicine: A practical primer.
European Heart Journal, 41(42), 3934–3945. https://doi.org/10.1093/eurheartj/ehaa252
Sharma, S., Sharma, D., & Kaushik, P. (2021). Artificial intelligence applications in Indian healthcare: Opportunities and challenges. AI & Society, 36(4), 1021–1034. https://doi.org/10.1007/s00146-020-01104-4
