Artificial Intelligence in Healthcare: A Review of Diagnostic Applications and Impact on Clinical Practice
Tufael1*, Atikur Rahman Sunny2
Journal of Primeasia 2(1) 1-5 https://doi.org/10.25163/primeasia.219816
Submitted: 12 July 2021 Revised: 10 September 2021 Published: 16 September 2021
Abstract
Background: Early detection of health issues is essential for effective treatment, necessitating advanced hospital service quality subsystems, ideally through an integrated Hospital Management Information System (HMIS). Artificial Intelligence (AI) has become a crucial element in clinical medicine, particularly in diagnostics. AI technologies, especially in medical imaging, are poised to revolutionize diagnostic and predictive analysis in healthcare. Methods: This study examines the application of AI in enhancing diagnostic accuracy and predictive capabilities in healthcare. It draws on evidence from pathology and dermatology studies, as well as research on AI's utility in mental health risk assessment and medical imaging. Results: AI has demonstrated superior performance compared to human experts in accurately detecting and classifying various cancers, such as those identified through pathology and dermatology studies. AI's capabilities extend beyond cancer detection to include the differentiation between benign and malignant conditions, thus aiding in more precise disease diagnosis. Additionally, AI shows promise in predicting mental health risks, including mental illnesses and suicide risks among vulnerable populations, such as psychiatric patients, prisoners, and soldiers. These applications highlight AI's potential to provide timely and precise disease information, leading to quicker and more accurate diagnoses. Conclusion: AI integration into healthcare significantly enhances diagnostic accuracy and optimizes disease management efficiency, improving patient outcomes and streamlining healthcare delivery. The technology's potential in medical imaging and mental health assessment underscores its value in providing early and accurate diagnoses, thereby supporting effective treatment and patient care. Continued research and development in AI applications are crucial to further realize its benefits in clinical settings.
Keywords: Artificial Intelligence (AI), Diagnostics, Clinical Decision Support Systems (CDSS), Medical Imaging, User Resistance
References
Angermueller, C., Arnamaa, T. P., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12, 878. https://doi.org/10.15252/msb.0156651
Ayaad, O., Alloubani, A., & Abu Alhajaa, E. (2019). The role of electronic medical records in improving the quality of health care services: Comparative study. International Journal of Medical Informatics, 127, 63–67. https://doi.org/10.1016/j.ijmedinf.2019.04.014
Ayanlade, O. S., Oyebisi, T. O., & Kolawole, B. A. (2019). Health Information Technology Acceptance Framework for diabetes management. Heliyon, 5(5), e01735. https://doi.org/10.1016/j.heliyon.2019.e01735
Bansler, J. P. (n.d.). Challenges in user-driven optimization of EHR: A case study of a large Epic implementation in Denmark. International Journal of Medical Informatics, 148. https://doi.org/10.1016/j.ijmedinf.2021.104394
Bhattacherjee, A., & Hikmet, N. (2017). Physicians' resistance toward healthcare information technology: A theoretical model and empirical test. European Journal of Information Systems, 16, 725–737. https://doi.org/10.1057/palgrave.ejis.3000717
Centers for Disease Control and Prevention. (2014). Global Health-Bangladesh. Retrieved from https://www.cdc.gov/globalhealth/countries/bangladesh/default.htm
Collazos, C. A., Gutiérrez, F. L., Gallardo, J., Ortega, M., Fardoun, H. M., & Molina, A. I. (2019). Descriptive theory of awareness for groupware development. Journal of Ambient Intelligence and Humanized Computing, 10(12), 4789–4818. https://doi.org/10.1007/s12652-019-01374-3
Currie, G., & Croft, C. (2015). Examining hybrid nurse managers as a case of identity transition in healthcare: Developing a balanced research agenda. Work, Employment & Society, 29(5), 855–865. https://doi.org/10.1177/0950017015572581
Dupret, K. (2017). Working around technologies—invisible professionalism? New Technology, Work and Employment, 32, 174–187. https://doi.org/10.1111/ntwe.12093
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115. https://doi.org/10.1038/nature21056
Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine: Insights into future medical technologies. Medical Technology Concepts, Integration, 69, S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Hazarika, I. (2020). Artificial intelligence: Opportunities and implications for the health workforce. International Health, 12(4), 241–245. https://doi.org/10.1093/inthealth/ihaa007
Islam, J. Y., Zaman, M. M., Moniruzzaman, M., Shakoor, S. A., & Hossain, A. H. M. E. (2020). Estimation of total cardiovascular risk using the 2019 WHO CVD prediction charts and comparison of population-level costs based on alternative drug therapy guidelines: A population-based study of adults in Bangladesh. BMJ Open, 10(e035842). https://doi.org/10.1136/bmjopen-2019-035842
Kohli, A., & Jha, S. (2018). Why CAD failed in mammography. Data Science, Big Data, and Machine Learning in Artificial Intelligence, 15, 535–537. https://doi.org/10.1016/j.jacr.2017.12.029
Marlon, R., Gautama, J., & Renando, R. (2020). Application of artificial intelligence (AI) in NAR nursing care robots in increasing the effectiveness of work performance in hospitals. Journal of Information, 1(2), 169–175. Retrieved from https://journal.uib.ac.id/index.php/joint/article/view/4319
Miller, D. D., & Brown, E. W. (2018). Artificial intelligence in medical practice: The question to the answer? American Journal of Medicine, 131, 129–133. https://doi.org/10.1016/j.amjmed.2017.10.035
Molleman, E., & Rink, F. (2015). The antecedents and consequences of a strong professional identity among medical specialists. Social Theory & Health, 13(1), 46–61. https://doi.org/10.1057/sth.2014.16
National Institute of Population Research and Training (NIPORT), Mitra and Associates, & ICF International. (2013). Bangladesh Demographic and Health Survey 2011. NIPORT.
Nekoel-Moghadam, M., & Amiresmaili, M. (2018). Hospital services quality assessment. Journal of Health Care Quality Assurance, 24(1), 57–66.
Pan, J., Ding, S., Wu, D., Yang, S., & Yang, J. (2019). Exploring behavioural intentions toward smart healthcare services among medical practitioners: A technology transfer perspective. International Journal of Production Research, 57, 5801–5820. https://doi.org/10.1080/00207543.2018.1550272
Park, Y. T., Kim, D., & Park, R. W. (2020). Association between full electronic medical record system adoption and drug use: Antibiotics and polypharmacy. Healthcare Informatics Research, 26, 68–77. https://doi.org/10.4258/hir.2020.26.1.68
Russell, B., Trusson, C., & De, S. (2016). The ambiguities of 'managed professionalism': Working in and with IT. In A. Wilkinson, D. Hislop, & C. Coupland (Eds.), Perspectives on Contemporary Professional Work: Challenges and Experiences (pp. 361–380). Edward Elgar Publishing.
Samhan, B. (2018). Revisiting technology resistance: Current insights and future directions. Australasian Journal of Information Systems, 22. https://doi.org/10.3127/ajis.v22i0.1655
Schmidt, K., Aumann, I., Hollander, I., Damm, K., & von der Schulenburg, M. G. (2015). Applying the analytic hierarchy process in healthcare research: A systematic literature review and evaluation of reporting. BMC Medical Informatics and Decision Making, 15, 112. https://doi.org/10.1186/s12911-015-0112-3
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25, 44–56. https://doi.org/10.1038/s41591-018-0300-7
Van Ginneken, B. (2017). Fifty years of computer analysis in chest imaging: Rule-based, machine learning, deep learning. Radiological Physics and Technology, 10, 23–32. https://doi.org/10.1007/s12194-017-0394-5
Venkatesh, S. S., Levenback, B. J., Sultan, L. R., Bouzghar, G., & Sehgal, C. M. (2017). Going beyond a first reader: A machine learning methodology for optimizing cost and performance in breast ultrasound diagnosis. Ultrasound in Medicine & Biology, 41, 3148–3162. https://doi.org/10.1016/j.ultrasmedbio.2015.07.020
Yulianti, A., & Muhardi. (2019). Hospital management system analysis in effort to improve service quality by using structured design life cycle method (A case study of Al-Mulk Regional Public Hospital in Sukabumi City). Advances in Social Science, Education and Humanities Research, 409, 207-209.
Zhou, S. K., Greenspan, H., & Shen, H. (2017). Deep learning for medical image analysis (1st ed.). Elsevier.
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