ARTIFICIAL INTELLIGENCE IN PATIENT CARE FUTURE PROSPECTIVES
Main Article Content
Abstract
In recent years, Artificial intelligence (AI) technologies have greatly advanced and become a reality in human life. AI is one of the tools to analyze various diseases in the healthcare field, numerous efforts are being implemented for practical medical treatments. The technologies such as machine learning and deep learning profoundly optimize the existing mode of drug research and also AI-enabled tools to assist and ideally improve the patient experience including diagnosis, treatment, and outcomes. In this review, we summarize the future aspects, latest development, and its limitations of AI in disease management of cardiovascular disease, cancer, diabetes, respiratory disease and neurological disorders.
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1.
Samundi S. ARTIFICIAL INTELLIGENCE IN PATIENT CARE FUTURE PROSPECTIVES. IJPBR [Internet]. 9May2024 [cited 8Aug.2025];11(04). Available from: https://ijpbr.in/index.php/IJPBR/article/view/1053
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Review Articles
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References
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2. Sudhakar P, Selva Preethi S, Anjali G. Artificial Intelligence in Clinical Trials- Future Prospectives. Bioequiv & Bioavailab Int J 2023; 7(1):000196.
3. Kaieski N, da Costa CA, da Rosa Righi R, Lora PS, Eskofier B. Application of artificial intelligence methods in vital signs analysis of hospitalized patients: A systematic literature review. Appl Soft Comput 2020; 96:106612.
4. Turner CR, Fuggetta A, Lavazza L, Wolf AL. A conceptual basis for feature engineering. J Syst Softw 1999; 49(1):3-15.
5. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM 2017; 60(6):84-90.
6. Tompson JJ, Jain A, LeCun Y, Bregler C. Joint training of a convolutional network and a graphical model for human pose estimation. Adv. Neural Inf. Process 2014; 27:1-9.
7. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, Bonny A. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019. J Am Coll Cardiol 2020; 76(25):2982-3021.
8. Murdoch TB, Detsky AS. The inevitable application of big data to health care. Jama 2013; 309(13):1351-2.
9. Burke LE, Ma J, Azar KM, Bennett GG, Peterson ED, Zheng Y, Riley W, Stephens J, Shah SH, Suffoletto B, Turan TN. Current science on consumer use of mobile health for cardiovascular disease prevention: a scientific statement from the American Heart Association. Circulation 2015; 132(12):1157-213.
10. Kagiyama N, Shrestha S, Farjo PD, Sengupta PP. Artificial intelligence: practical primer for clinical research in cardiovascular disease. J Am Heart Assoc 2019; 8(17):e012788.
11. Adeoye J, Akinshipo A, Thomson P, Su YX. Artificial intelligence-based prediction for cancer-related outcomes in Africa: Status and potential refinements. J Glob Health 2022; 12:03017.
12. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca Cancer J Clin 2018; 68(6):394-424.
13. Iuga AI, Carolus H, Höink AJ, Brosch T, Klinder T, Maintz D, Persigehl T, Baeßler B, Püsken M. Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Med Imaging 2021; 21(1):1-2.
14. Soares F, Becker K, Anzanello MJ. A hierarchical classifier based on human blood plasma fluorescence for non-invasive colorectal cancer screening. Artif Intell Med 2017; 82:1-0.
15. Hmida M, Hamrouni K, Solaiman B, Boussetta S. Mammographic mass segmentation using fuzzy contours. Comput Methods Programs Biomed 2018; 164:131-42.
16. Nartowt BJ, Hart GR, Muhammad W, Liang Y, Stark GF, Deng J. Robust machine learning for colorectal cancer risk prediction and stratification. Front Big Data 2020; 3:6.
17. Welch HG, Prorok PC, O’Malley AJ, Kramer BS. Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness. N Engl J Med 2016; 375(15):1438-47.
18. Zarzeczny A, Babyn P, Adams SJ, Longo J. Artificial intelligence-based imaging analytics and lung cancer diagnostics: Considerations for health system leaders. Healthc Manag Forum 2021; 34(3):169-174.
19. Pachiappan S, Sukumaran A, Mathew SR, Varghese SD, Aloysious T. Effect of Diabetic Medication on Cardiovascular Risk and Microvascular Complication in Diabetic Patients: Retrospective Cohort Study. J Appl Pharm Sci 2018; 8(3):031-6.
20. Nimri R, Battelino T, Laffel LM, Slover RH, Schatz D, Weinzimer SA, Dovc K, Danne T, Phillip M. Next DREAM Consortium: Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med 2020; 26(9):1380-4.
21. Abbasi A, Peelen LM, Corpeleijn E, Van Der Schouw YT, Stolk RP, Spijkerman AM, Moons KG, Navis G, Bakker SJ, Beulens JW. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ 2012; 345:e5900.
22. Nomura A, Noguchi M, Kometani M, Furukawa K, Yoneda T. Artificial intelligence in current diabetes management and prediction. Curr Diab Rep 2021; 21(12):61.
23. Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med 2018; 24(2):117-23.
24. Milne S, King GG. Advanced imaging in COPD: insights into pulmonary pathophysiology. J Thorac Dis 2014; 6(11):1570-85.
25. Aikins JS, Kunz JC, Shortliffe EH, Fallat RJ. PUFF: an expert system for interpretation of pulmonary function data. Comput Biomed Res 1983; 16(3):199-208.
26. Brashier B, Salvi S. Measuring lung function using sound waves: role of the forced oscillation technique and impulse oscillometry system. Breathe 2015; 11(1):57-65.
27. Montuschi P, Santonico M, Mondino C, Pennazza G, Mantini G, Martinelli E, Capuano R, Ciabattoni G, Paolesse R, Di Natale C, Barnes PJ. Diagnostic performance of an electronic nose, fractional exhaled nitric oxide, and lung function testing in asthma. Chest 2010; 137(4):790-6.
28. Pifferi M, Bush A, Pioggia G, Di Cicco M, Chinellato I, Bodini A, Macchia P, Boner AL. Monitoring asthma control in children with allergies by soft computing of lung function and exhaled nitric oxide. Chest 2011; 139(2):319-27.
29. Wu W, Bleecker E, Moore W, Busse WW, Castro M, Chung KF, Calhoun WJ, Erzurum S, Gaston B, Israel E, Curran-Everett D. Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data. J Allergy Clin Immunol 2014; 133(5):1280-8
30. Sudhakar P, Hari CK, Divya R. Role of Indian spices in CNS disorders: A review. J Med Plants 2020; 8(4):171-5.
31. Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. Sensors 2023; 23(6):3062.
32. Aung YY, Wong DC, Ting DS. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull 2021; 139(1):4-15.