Application of artificial intelligence in medical care: review of current status

Authors

  • Hetal Pandya Department of General Medicine, Dr. N. D. Desai Faculty of Medical Science and Research, Nadiyad, Gujarat, India https://orcid.org/0000-0003-1288-175X
  • Tanay Pandya Department of Information Technology, Dharamsinh Desai Institute of Technology, Nadiyad, Gujarat, India

DOI:

https://doi.org/10.18203/2349-3933.ijam20230073

Keywords:

Artificial intelligence, Machine learning, Medical care

Abstract

Artificial intelligence (AI) has transformed almost all spheres of our life and has the potential to radically alter the field of health care. The increasing availability of healthcare data and rapid development of big data analytic methods has made possible the recent successful applications of AI in healthcare. Guided by relevant clinical questions, powerful AI techniques can unlock clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making. To date, many AI systems have been developed in healthcare, but use and adoption in clinical practice has been limited. In this article, we tried to review few of promising AI techniques and tools, which can have a great impact on our health care system and in turn on quality of life.

 

References

McCarthy J. Programs with Common Sense. In Mechanisation of Thought Processes, Proceedings of the Symposium of the National Physics Laboratory, London, U.K. Her Majesty’s Stationery Office. 1959;77-84.

McCarthy J. What is artificial intelligence? Available at: http://www-formal.stanford.edu/jmc/ whatisai. pdf. Accessed on 14 October 2022.

What is Artificial Intelligence in 2023? Types, Trends, and Future of it? Available at: https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/. Accessed on 14 October 2022.

Darcy AM, Louie AK, Roberts LW. Machine Learning and the Profession of Medicine. JAMA. 2016;315:551-2.

Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA. 2011;306(8):848-55.

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2:e000101.

Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digital Health. 2021;3:e195-203.

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441-6.

Dekker A, Vinod S, Holloway L, Oberije C, George A, Goozee G, Delaney GP, Lambin P, Thwaites D. Rapid learning in practice: a lung cancer survival decision support system in routine patient care data. Radiother Oncol. 2014;113(1):47-53.

Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, Tan GSW, Schmetterer L, Keane PA, Wong TY. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-75.

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016;316(22):2402-10.

Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211-23.

Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8.

Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc. 2000;7(6):593-604.

Rehme AK, Volz LJ, Feis DL, Bomilcar-Focke I, Liebig T, Eickhoff SB, Fink GR, Grefkes C. Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques. Cereb Cortex. 2015;25(9):3046-56.

Griffis JC, Allendorfer JB, Szaflarski JP. Voxel-based gaussian naïve Bayes classification of ischemic stroke lesions in individual T1- weighted MRI scans. J Neurosci Methods. 2016;257:97-108.

Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal. 2017;36:61-78.

Rondina JM, Filippone M, Girolami M, Ward NS. Decoding post-stroke motor function from structural brain imaging. Neuroimage Clin. 2016;12:372-80.

Marr B. First FDA approval for clinical Cloud-Based Deep Learning in Healthcare. 2017. Available at: https://www. forbes. com/ sites/ bernardmarr/ 2017/ 01/ 20/ first- fda- approval- for- clinical- cloud- based- deep- learning- inhealthcare/#7a0ed8dc161c. Accessed on 12 November 2022.

MedicinePlus. What is precision medicine? Available at: https://medlineplus.gov/genetics/ understanding/precisionmedicine/definition/. Accessed on 12 November 2022.

Devenport T, Kolakota R. The potential for artificial intelligence in health care. Future Healthc J. 2019;6(2):94-8.

Lohr S. IBM is counting on its bet on Watson, and Paying Big Money for It. 2016. Available at: https://www. nytimes. com/ 2016/ 10/ 17/ technology/ ibm- is- counting- on- its- bet- on- watson- and- paying- big- money- for- it. Accessed on 06 May 2022.

Otake T. IBM Big Data used for rapid diagnosis of rare leukemia case in Japan. 2016. Available at: http://www. japantimes. co. jp/ news/ 2016/ 08/ 11/ national/ science- health/ ibm- big- data- used- for- rapid- diagnosis- ofrare- leukemia- case- in- Japan. Accessed on 06 May 2022.

Ross C, Swetlitz I. IBM pitched its Watson supercomputer as a revolution in cancer care. It's nowhere close. Stat 2017. Available at: www.statnews.com/2017/09/05/watson-ibm-cancer. Accessed on 06 May 2022.

Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104‑16.

Anthimopoulos M, Dehais J, Shevchik S, Ransford BH, Duke D, Diem P, et al. Computer vision‑based carbohydrate estimation for type 1 patients with diabetes using smartphones. J Diabetes Sci Technol. 2015;9:507‑15.

Woldaregay AZ, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data‑driven blood glucose pattern classification and anomalies detection: Machine‑learning applications in type 1 diabetes. J Med Internet Res. 2019;21:e11030.

Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. Sensors (Basel). 2020;20(14):3870.

Schmidt S, Nørgaard K. Bolus calculators. J Diabetes Sci Technol. 2014;8:1035‑41.

Frøisland DH, Arsand E. Integrating visual dietary documentation in mobile-phone-based self-management application for adolescents with type 1 diabetes. J Diabetes Sci Technol. 2015;9(3):541-8.

Mei J, Zhao S, Jin F, Zhang L, Liu H, Li X, et al. Deep diabetologist: Learning to prescribe hypoglycemic medications with recurrent neural networks. Stud Health Technol Inform. 2017;245:1277.

Wright AP, Wright AT, McCoy AB, Sittig DF. The use of sequential pattern mining to predict next prescribed medications. J Biomed Inform. 2015;53:73‑80.

Jindal D, Gupta P, Jha D, Ajay VS, Goenka S, Jacob P, et al. Development of mWellcare: An mHealth intervention for integrated management of hypertension and diabetes in low‑resource settings. Glob Health Action. 2018;11:1517930.

Prabhakaran D, Jha D, Prieto‑Merino D, Roy A, Singh K, Ajay VS, et al. Effectiveness of an mHealth‑based electronic decision support system for integrated management of chronic conditions in primary care: The mWellcare cluster‑randomized controlled trial. Circulation 2018;139(3).

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma H, Wang Y, et al. Artificial intelligence in health care: Past, present and future. Stroke and Vasc Neurol. 2017;2:e000101.

Bentley P, Ganesalingam J, Carlton Jones AL, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin. 2014;4:635-40.

Love A, Arnold CW, El-Saden S, Liebeskind DS, Andrada L, Saver J, et al. Unifying acute stroke treatment guidelines for a bayesian belief network. Stud Health Technol Inform. 2013;192:1012.

Ye H, Shen H, Dong Y. Using Evidence-Based medicine through Advanced Data Analytics to work toward a National Standard for Hospital-based acute ischemic Stroke treatment. Mainland China. 2017.

Asadi H, Dowling R, Yan B, Mitchell P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS One. 2014;9(2):e88225.

Asadi H, Kok HK, Looby S, Brennan P, O'Hare A, Thornton J. Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence. World Neurosurg. 2016;96:562-9.

Birkner MD, Kalantri S, Solao V, Badam P, Joshi R, Goel A, Pai M, Hubbard AE. Creating diagnostic scores using data-adaptive regression: An application to prediction of 30-day mortality among stroke victims in a rural hospital in India. Ther Clin Risk Manag. 2007;3(3):475-84.

Chen Y, Dhar R, Heitsch L, Ford A, Fernandez-Cadenas I, Carrera C, et al. Automated quantification of cerebral edema following hemispheric infarction: Application of a machine-learning algorithm to evaluate CSF shifts on serial head CTs. Neuroimage Clin. 2016;12:673-80.

Siegel JS, Ramsey LE, Snyder AZ, Metcalf NV, Chacko RV, Weinberger K, Baldassarre A, Hacker CD, Shulman GL, Corbetta M. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc Natl Acad Sci U S A. 2016;113(30):E4367-76.

Cichosz SL, Johansen MD, Hejlesen O. Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications. J Diabetes Sci Technol. 2015;10(1):27-34.

Wang HH, Wang YH, Liang CW, Li YC. Assessment of deep learning using nonimaging information and sequential medical records to develop a prediction model for nonmelanoma skin cancer. JAMA Dermatol. 2019;155:1277-83.

Roffman D, Hart G, Girardi M, Ko CJ, Deng J. Predicting non-melanoma skin cancer via a multi-parameterized artificial neural network. Sci Rep. 2018;8:1701.

Emam S, Du AX, Surmanowicz P, Thomsen SF, Greiner R, Gniadecki R. Predicting the Long-term Outcomes of Biologics in Psoriasis Patients using Machine Learning. Br J Dermatol. 2020;182:1305-7.

Gabarron E, A rsand E, Wynn R. Social media use in interventions for diabetes: rapid evidence-based review. J Med Internet Res. 2018;20(8):e10303.

Berman MA, Guthrie NL, Edwards KL, Appelbaum KJ, Njike VY, Eisenberg DM, et al. Change in glycemic control with use of a digital therapeutic in adults with type 2 diabetes: cohort study. JMIR Diabetes. 2018;3(1):e4.

Fagherazzi G, Ravaud P. Digital diabetes: perspectives for diabetes prevention, management and research. Diabetes Metab. 2019;45(4):322-9.

Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-94.

Reisman M. EHRs: The Challenge of Making Electronic Data Usable and Interoperable. Pharm Therap. 2017;42(9):572-5.

Davenport TH, Dreyer KJ. AI will change radiology, but it won't replace radiologists. Harvard Business Review. 2018. Available at: https://hbr.org/ 2018/03/ai-will-change-radiology-but-it-wont-replace-radiologists. Accessed on 06 May 2022.

Downloads

Published

2023-01-23

Issue

Section

Review Articles