Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges

This study examines the current state of artificial intelligence (AI)-based technology applications and their impact on the healthcare industry. In addition to a thorough review of the literature, this study analyzed several real-world examples of AI applications in healthcare. The results indicate that major hospitals are, at present, using AI-enabled systems to augment medical staff in patient diagnosis and treatment activities for a wide range of diseases. In addition, AI systems are making an impact on improving the efficiency of nursing and managerial activities of hospitals. While AI is being embraced positively by healthcare providers, its applications provide both the utopian perspective (new opportunities) and the dystopian view (challenges to overcome). We discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. Nevertheless, effective applications of AI will require effective planning and strategies to transform the entire care service and operations to reap the benefits of what technologies offer.

[1]  E. Siegel,et al.  Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced Computing to Provide Personalized Medical Diagnosis and Treatment , 2013, Current Cardiology Reports.

[2]  The WHO cross-national study of health behavior in school-aged children from 35 countries: findings from 2001-2002. , 2004, The Journal of school health.

[3]  P. Sourtzi,et al.  Patients’ perceptions and preferences of participation in nursing care , 2016 .

[4]  D. Luxton Artificial intelligence in psychological practice: Current and future applications and implications. , 2014 .

[5]  J. Stephens,et al.  Rare disease landscape: will the blockbuster model be replaced? , 2014 .

[6]  T. Toro-Ramos,et al.  Weight loss efficacy of a novel mobile Diabetes Prevention Program delivery platform with human coaching , 2016, BMJ Open Diabetes Research and Care.

[7]  Daniel S. Kermany,et al.  Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning , 2018, Cell.

[8]  Michael Lupton,et al.  Some ethical and legal consequences of the application of artificial intelligence in the field of medicine , 2018 .

[9]  Seongbae Lim,et al.  Living Innovation: From Value Creation to the Greater Good , 2018 .

[10]  H. Abe,et al.  Lifestyle medicine – An evidence based approach to nutrition, sleep, physical activity, and stress management on health and chronic illness , 2019, Personalized Medicine Universe.

[11]  DonHee Lee,et al.  Healthcare wearable devices: an analysis of key factors for continuous use intention , 2020, Service Business.

[12]  DonHee Lee,et al.  Strategies for technology-driven service encounters for patient experience satisfaction in hospitals , 2018, Technological Forecasting and Social Change.

[13]  M. Rigby Ethical Dimensions of Using Artificial Intelligence in Health Care , 2019, AMA Journal of Ethics.

[14]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[15]  Seong No Yoon,et al.  Artificial intelligence and robots in healthcare: What are the success factors for technology-based service encounters? , 2018, International Journal of Healthcare Management.

[16]  Geir M. Køien,et al.  Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks , 2015, J. Cyber Secur. Mobil..

[17]  Richard Colbaugh,et al.  Learning to Identify Rare Disease Patients from Electronic Health Records , 2018, AMIA.

[18]  Youn Sung Kim,et al.  The quality management ecosystem for predictive maintenance in the Industry 4.0 era , 2019, International Journal of Quality Innovation.

[19]  Hugo Jair Escalante,et al.  Acute leukemia classification by ensemble particle swarm model selection , 2012, Artif. Intell. Medicine.

[20]  Sajal K. Das,et al.  Handbook on Securing Cyber-Physical Critical Infrastructure , 2012 .

[21]  Terrence J. Sejnowski,et al.  Evolution of artificial intelligence , 1995, Nature.

[22]  Jonathan Guo,et al.  The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries , 2018, Health equity.

[23]  DonHee Lee,et al.  Lessons Learned from Battling COVID-19: The Korean Experience , 2020, International journal of environmental research and public health.

[24]  A. Kumar,et al.  551PD Validation study to assess performance of IBM cognitive computing system Watson for oncology with Manipal multidisciplinary tumour board for 1000 consecutive cases: An Indian experience , 2016 .

[25]  K. Doi,et al.  Computer-aided diagnosis and artificial intelligence in clinical imaging. , 2011, Seminars in nuclear medicine.

[26]  T. Kaptchuk,et al.  Placebo Effects in Medicine. , 2015, The New England journal of medicine.

[27]  Geoffrey J. Gordon,et al.  Artificial intelligence in medicine , 1989, Springer US.

[28]  Xin Sun,et al.  Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence , 2019, Nature Medicine.

[29]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[30]  S. Kajihara,et al.  Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma , 2019, Scientific Reports.

[31]  Stuart Speedie,et al.  Quantifying the Effect of Data Quality on the Validity of an eMeasure , 2017, Applied Clinical Informatics.

[32]  T. Bucknall,et al.  Patients' perceptions of participation in nursing care on medical wards. , 2016, Scandinavian journal of caring sciences.

[33]  Yan Fossat,et al.  Physicians’ Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey , 2018, Journal of medical Internet research.

[34]  Sara Reardon,et al.  Rise of Robot Radiologists , 2019, Nature.

[35]  Matthew P. Manary,et al.  Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. , 2011, The American journal of managed care.

[36]  Ray Hutchison An engine for growth , 2010 .

[37]  J. Havel,et al.  Artificial neural networks in medical diagnosis , 2013 .

[38]  DonHee Lee Effects of key value co-creation elements in the healthcare system: focusing on technology applications , 2018, Service Business.

[39]  Kris K. Hauser,et al.  Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach , 2013, Artif. Intell. Medicine.

[40]  Daniel Rueckert,et al.  Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study , 2017, Radiology.

[41]  T. Berzin,et al.  Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study , 2019, Gut.

[42]  L. Coventry,et al.  Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. , 2018, Maturitas.

[43]  F. Sardanelli,et al.  Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine , 2018, European Radiology Experimental.