Angiogenesis, Inflammation & Therapeutics | Online ISSN  2207-872X
REVIEWS   (Open Access)

Advancements, Applications, and Future Directions of Artificial Intelligence in Healthcare

Syed Mosaddik Hossain Ifty 1, Farhana Irin 2, Md Shihab Sadik Shovon 3, Mohammad Hamid Hasan Amjad 3, Proshanta Kumar Bhowmik 3*, Raju Ahmed 4, Md Rahatul Ashakin 5, Bayazid Hossain 6, Mushfiq 1, Abdus Sattar 7, Redoyan Chowdhury 8, Atiqur Rahman Sunny 7*

+ Author Affiliations

Journal of Angiotherapy 8(8) 1-18 https://doi.org/10.25163/angiotherapy.889843

Submitted: 10 June 2024  Revised: 02 August 2024  Published: 05 August 2024 

Abstract

Background: The integration of artificial intelligence (AI) into healthcare represents a transformative shift in medical procedures, offering substantial benefits across various domains. With advancements in AI technologies such as machine learning (ML), deep learning (DL), and natural language processing (NLP), healthcare systems are witnessing improvements in early detection, patient treatment, and overall administration. This article traces the evolution of AI, from foundational contributions by Alan Turing during World War II to contemporary applications like ChatGPT, and examines the impact of AI in enhancing diagnostic accuracy and treatment outcomes. Methods: This comprehensive review analyzes the existing literature on AI applications in healthcare, focusing on various AI methodologies and their integration into clinical settings. It evaluates the effectiveness of AI in processing large datasets, improving diagnostic precision, and facilitating data-driven decision-making. The study also explores the ethical, legal, and technical challenges associated with AI deployment in medical environments. Results: AI technologies have demonstrated significant improvements in healthcare, particularly in early disease detection, personalized treatment plans, and resource management. The use of AI in analyzing vast medical datasets has enhanced diagnostic accuracy, reduced costs, and optimized patient care. However, challenges related to ethical considerations, patient privacy, and system reliability remain critical barriers to full-scale AI adoption. Conclusion: Despite the challenges, AI is positioned as an indispensable tool in modern medicine, capable of enhancing preventive care, personalizing treatments, and improving healthcare delivery. This review proposes a framework for evaluating the benefits, challenges, and strategies of AI integration in healthcare. Further research is essential to maximize AI's potential while addressing ethical and practical concerns, ensuring safe and effective implementation in clinical settings.

Keywords: Artificial Intelligence, Medical System, Smart Healthcare, Diagnosis, Machine Learning

References

Alexander, A., Jiang, A., Ferreira, C., & Zurkiya, D. (2020). An intelligent future for medical imaging: A market outlook on artificial intelligence for medical imaging. Journal of the American College of Radiology, 17(1PB), 165-170. https://doi.org/10.1016/j.jacr.2019.07.019

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938

Ahsan, M. M., & Siddique, Z. (2022). Industry 4.0 in Healthcare: A systematic review. International Journal of Information Management Data Insights, 2, (1) 100079.

Aiken, R. M., & Epstein, R. G. (2000). Ethical guidelines for AI in education: Starting a conversation. The International Journal of Artificial Intelligence in Education, 11, 163–176.

Ain, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J. (2015). Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdisciplinary Reviews: Computational Molecular Science, 5(6), 405–424. https://doi.org/10.1002/wcms.1225

Alam, K., Chowdhury, M. Z. A., Jahan, N., Rahman, K., Chowdhury, R., Mia, M. T., & Mithun, M. H. (2023). Relationship between Brand Awareness and Customer Loyalty in Bangladesh: A Case Study of Fish Feed Company. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(3), 212-222.

Alam, K., Jahan, N., Chowdhury, R., Mia, M.T., Saleheen, S., Hossain, N.M & Sazzad, S.A. (2023a). Impact of Brand Reputation on Initial Perceptions of Consumers. Pathfinder of Research, 1 (1), 1-10.

Alam, K., Jahan, N., Chowdhury, R., Mia, M.T., Saleheen, S., Sazzad, S.A. Hossain, N.M & Mithun, M.H. (2023b). Influence of Product Design on Consumer Purchase Decisions. Pathfinder of Research, 1 (1), 23-36

Alami, H., Gagnon, M.-P., & Fortin, J.-P. (2017). Digital health and the challenge of health systems transformation. mHealth, 3(31).

Ali, O., Murray, P. A., Muhammed, S., Dwivedi, Y. K., & Rashiti, S. (2022). Evaluating organizational level IT innovation adoption factors among global firms. Journal of Innovation & Knowledge, 7(3), 100213.

Aljaaf, A. J., Al-Jumeily, D., Hussain, A. J., Fergus, P., Al-Jumaily, M., & Abdel-Aziz, K (2015). Toward an optimal use of artificial intelligence techniques within a clinical decision support system. Science and Information Conference (pp. 548−554).

Altman, W. T. (2019). Graph Convolutional Neural Networks for Predicting Drug-Target Interactions. Journal of Chemical Information and Modeling, 4131-4149.

Andresen, S. L. (2002). John McCarthy: Father of AI. IEEE Intelligent Systems, 17(5), 84-85.

Antoniou, Z. C., Panayides, A. S., Pantzaris, M., Constantinides, A. G., Pattichis, C. S., & Pattichis, M. S. (2018). Real-Time adaptation to time-varying constraints for medical video communications. IEEE Journal of Biomedical and Health Informatics, 22(4), 1177–1188.

Bari, K. F., Salam, M. T., Hasan, S. E., & Sunny, A. R. (2023). Serum zinc and calcium level in patients with psoriasis. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(3), 7-14.

Barnett, K. M. (2012). Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. The Lancet, , 380(9836), 37-43.

Basaia, S., Agosta, F., Wagner, L., Canu, E., Magnani, G., Santangelo, R., & Filippi, M. (2019). Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical, 21, 101645. https://doi.org/10.1016/j.nicl.2018.101645

Bashiri, A., Ghazisaeedi, M., Safdari, R., Shahmoradi, L., & Ehtesham, H. (2017). Improving the prediction of survival in cancer patients by using machine learning techniques: Experience of gene expression data: A narrative review. Iranian Journal of Public Health, 46(2), 165–172.

Bernardini, M., Romeo, L., Frontoni, E., & Amini, M. R. (2021). A semi-supervised multitask learning approach for predicting short-term kidney disease evolution. IEEE Journal of Biomedical and Health Informatics, 25(10), 3983–3994.

Bhaduri, K., Stefanski, M. D., & Srivastava, A. N. (2011). Privacy-preserving outlier detection through random nonlinear data distortion. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1), 260–272.

Billah, M., Waheed, S., & Rahman, M. M. (2017). An automatic gastrointestinal polyp detection system in video endoscopy using fusion of color wavelet and convolutional neural network features. International Journal of Biomedical Imaging, 2017. https://doi.org/10.1155/2017/9545920

Blanco-González, A. (2023). The Role of AI in Drug Discovery: Challenges, Opportunities, pharmaceuticals.

Breazeal, C. (2003). Toward sociable robots. Robotics and Autonomous Systems, 42(3-4), 167-175.

Brighter Agyemang, W.-P. W. (2020). Multi-view self-attention for interpretable drug–target interaction prediction. Biomedical Informatics, ISSN 1532-0464.

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., ... Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877-1901.

Buchanan, B. G., & Shortliffe, E. H. (1984). Rule based expert systems: the mycin experiments of the stanford heuristic programming project (the Addison-Wesley series in artificial intelligence). Addison-Wesley Longman Publishing Co., Inc..

Cai, T., Giannopoulos, A. A., Yu, S., Kelil, T., Ripley, B., Kumamaru, K. K., Rybicki, F. J., & Mitsouras, D. (2016). Natural Language Processing Technologies in Radiology Research and Clinical Applications. Radiographics, 36(1), 176-191. https://doi.org/10.1148/rg.2016150096

Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2), 48-57.

Campbell, M., Hoane Jr, A. J., & Hsu, F. H. (2002). Deep blue. Artificial Intelligence, 134(1-2), 57-83.

Carlos Roca, V. R. (2021). AI in drug development: a multidisciplinary perspective. Molecular Diversity, 1461–1479.

Carter, P., Laurie, G. T., & Dixon-Woods, M. (2015). The social licence for research: Why care.data ran into trouble. Journal of Medical Ethics, 41(5), 404-409.  https://doi.org/10.1136/medethics-2014-102374

Charan, S., Khan, M. J., & Khurshid, K. (2018). Breast cancer detection in mammograms using convolutional neural networks. The International Conference on Computing, Mathematics and Engineering Technologies (pp. 1−5).

Chen, Y., Argentinis, J. E., & Weber, G. (2016). IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clinical therapeutics, 38(4), 688-701.

Chen, Z. (2018). An AI-Based heart failure treatment adviser system. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–10.

Cheon, S., Kim, J., & Lim, J. (2019). The use of deep learning to predict stroke patient mortality. International Journal of Environmental Research and Public Health, 16(11), 1876. https://doi.org/10.3390/ijerph16111876

Chien, C. F., Dauzere-P er es, S., Huh, W. T., Jang, Y. J., & Morrison, J. R. (2020). Artificial intelligence in manufacturing and logistics systems: algorithms, applications, and case studies. International Journal of Production Research, 58(9), 2730–2731.

Chin, C. L., Lin, B. J., Wu, G. R., Weng, T. C., Yang, C. S., Su, R. C., & Pan, Y. J. (2017, November). An automated early ischemic stroke detection system using CNN deep learning algorithm. In 2017 IEEE 8th International conference on awareness science and technology (iCAST) (pp. 368-372). IEEE.

Comito, C., Falcone, D., & Forestiero, A. (2020, December). Current trends and practices in smart health monitoring and clinical decision support. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2577-2584). IEEE.

Daltayanni, M., Wang, C., & Akella, R. (2012, July). A fast interactive search system for healthcare services. In 2012 Annual SRII Global Conference (pp. 525-534). IEEE.

Davies, N., Manthorpe, J., Sampson, E. L., & Iliffe, S. (2015). After the Liverpool Care Pathway—development of heuristics to guide end of life care for people with dementia: protocol of the ALCP study. BMJ Open, 5(9), e008832. https://doi.org/10.1136/bmjopen-2015-008832

Davies, N., Manthorpe, J., Sampson, E. L., Lamahewa, K., Wilcock, J., Mathew, R., & Iliffe, S. (2018). Guiding practitioners through the end of life care for people with dementia: The use of heuristics. PLOS ONE, 13(11), e0206422. https://doi.org/10.1371/journal.pone.0206422

Davies, N., Mathew, R., Wilcock, J., Manthorpe, J., Sampson, E. L., Lamahewa, K., & Iliffe, S. (2016). A co-design process developing heuristics for practitioners providing end of life care for people with dementia. BMC Palliative Care, 15, 68. https://doi.org/10.1186/s12904-016-0146-z

Deng, Y., Sun, Y., Zhu, Y., Xu, Y., Yang, Q., Zhang, S., ... & Yuan, K. (2019). A new framework to reduce doctor’s workload for medical image annotation. IEEE Access, 7, 107097-107104.

Dharani, N., & Krishnan, G. (2021). ANN based COVID -19 prediction and symptoms relevance survey and analysis. The 5th International Conference on Computing Methodologies and Communication (pp. 1805−1808).

Dhieb, N., Ghazzai, H., Besbes, H., & Massoud, Y (2020). A Secure AI-Driven architecture for automated insurance systems: Fraud detection and risk measurement. IEEE Access, 8, 58546–58558.

Duan, L., Street, W. N., & Xu, E. (2011). Health-care information systems: data mining methods in the creation of a clinical recommender system. Enterprise Information Systems, 5, 169–181.

Dutta, S., Long, W. J., Brown, D. F., & Reisner, A. T. (2013). Automated detection using natural language processing of radiologists' recommendations for additional imaging of incidental findings. Annals of Emergency Medicine, 62(2), 162-169. https://doi.org/10.1016/j.annemergmed.2013.02.004

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.

Ebigbo, A., Mendel, R., Probst, A., et al. (2018). Computer-aided diagnosis using deep learning in the evaluation of early esophageal adenocarcinoma. Gut. https://doi.org/10.1136/gutjnl-2018-317573

Edo-Osagie, O., De La Iglesia, B., Lake, I., & Edgemere, O. (2019). Deep learning for relevance filtering in syndromic surveillance: A case study in asthma/difficulty breathing. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (pp. 491-500). https://doi.org/10.5220/0007608904910500

Elbasi, E., Mathew, S., Topcu, A. E., & Abdelbaki, W. (2021, May). A survey on machine learning and internet of things for COVID-19. In 2021 IEEE World AI IoT Congress (AIIoT) (pp. 0115-0120). IEEE.

El-Rashidy, N., El-Sappagh, S., Islam, S., M. El-Bakry, H., & Abdelrazek, S. (2021). Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges. Diagnostics. Diagnostics, https://doi.org/10.3390/diagnostics11040607.

Erickson, B. J., Korfiatis, P., Akkus, Z., & Kline, T. L. (2017). Machine learning for medical imaging. Radiographics, 37(2), 505–515. https://doi.org/10.1148/rg.2017160130

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(7639), 115-118.

Floridi, L. (2018). Soft ethics, the governance of the digital and the general data protection regulation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 1-10.

Frank W. Pun, I. V. (2023). AI-powered therapeutic target discovery. Pharmacological Sciences, 561-572.

Gomoi, V., & Stoicu-Tivadar, V. (2010). A new method in automatic generation of medical protocols using artificial intelligence tools and a data manager. The International Joint Conference on Computational Cybernetics and Technical Informatics. doi:10.1109/ICCCYB.2010.5491290.

Greaves, F., Joshi, I., Campbell, M., Roberts, S., Patel, N., & Powell, J. (2018). What is an appropriate level of evidence for a digital health intervention. The Lancet, 392(10165), 2665-2667.

Grover, P., Kar, A. K., & Dwivedi, Y. K. (2020). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research, 1–37.

Gu, X., Ni, T., & Wang, H. (2014). New fuzzy support vector machine for the class imbalance problem in medical datasets classification. Scientific World Journal. https://doi.org/10.1155/2014/536434

Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65-69. https://doi.org/10.1038/s41591-018-0268-3

Harada, Y., Katsukura, S., Kawamura, R., & Shimizu, T. (2021). Efficacy of artificial-intelligence-driven differential-diagnosis list on the diagnostic accuracy of physicians: An open-label randomized controlled study. International Journal of Environmental Research and Public Health, 18(4), 2086. https://doi.org/10.3390/ijerph18042086

Harris, M., Qi, A., Jeagal, L., Torabi, N., Menzies, D., Korobitsyn, A., ... & Khan, F. A. (2019). A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PLOS ONE, 14(9), e0221339. https://doi.org/10.1371/journal.pone.0221339

Heckerling, P. S., Canaris, G. J., Flach, S. D., et al. (2007). Predictors of urinary tract infection based on artificial neural networks and genetic algorithms. International Journal of Medical Informatics, 76(4), 289-296. https://doi.org/10.1016/j.ijmedinf.2006.03.006

Heintzelman, N. H., Taylor, R. J., Simonsen, L., Lustig, R., Anderko, D., Haythornthwaite, J. A., Childs, L. C., & Bova, G. S. (2013). Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text. Journal of the American Medical Informatics Association, 20(5), 898-905. https://doi.org/10.1136/amiajnl-2012-001076

Herath, H. M. K. K. M. B., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2, (1) 100076.

Hosny A, Aerts HJWL. Artificial intelligence for global health. Science. 2019 Nov 22;366(6468):955-956. doi: 10.1126/science. aay5189. PMID: 31753987; PMCID: PMC7790340.

Hossain Ifty, S. M., Ashakin, M. R., Hossain, B., Afrin, S., Sattar, A., Chowdhury, R., ... & Sunny, A. R. (2023). IOT-Based Smart Agriculture in Bangladesh: An Overview. Applied Agriculture Sciences, 1(1), 1-6.

Hossain Ifty, S.M., Bayazid, H., Ashakin, M.R., Tusher, M.I., Shadhin, R. H., Hoque, J., Chowdhury, R. & Sunny, A.R.     et al. (2023b). Adoption of IoT in Agriculture - Systematic Review, Applied Agriculture Sciences, 1(1), 1-10, 9676

Hossen, M. S., & Karmoker, D. (2020, December). Predicting the probability of Covid-19 recovered in south Asian countries based on healthy diet pattern using a machine learning approach. In 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1-6). IEEE.

Hradecky, D., Kennell, J., Cai, W., & Davidson, R. (2022). Organizational readiness to adopt artificial intelligence in the exhibition sector in Western Europe. International Journal of Information Management, 65, 102497. doi:10.1016/j.ijinfomgt.2022.102497

Huiying Zhang, J. Z. (2021). The Application Analysis of Medical Chatbots and. FSST, 11-16.

Hwang, E., Park, S., Jin, K., et al. (2019). Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Network Open, 2(3), e191095. https://doi.org/10.1001/jamanetworkopen.2019.1095

Jahan, R., & Tripathi, M. M. (2021, June). Brain tumor detection using machine learning in MR images. In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT) (pp. 664-668). IEEE.

Jaiman, V., & Urovi, V. (2020). A consent model for blockchain-based health data sharing platforms. IEEE Access, 8, 143734–143745.

Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685-695.

Jayakumar, S., Sounderajah, V., Normahani, P., Harling, L., Markar, S. R., Ashrafian, H., & Darzi, A. (2021).      Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: A meta-research study. npj Digital Medicine. https://doi.org/10.1038/s41746-021-00544-y

Jiang, Y., Zheng, W., & Xue, Y. (2020). Applications of deep learning approaches in genomics and precision medicine: Current and future aspects. In X. Zhang, & W. Bao (Eds.), Big Data Analytics in Precision Medicine (pp. 81-100). Springer.

Johnson, M., Albizri, A., Harfouche, A., & Fosso-Wamba, S. (2022). Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence. International Journal of Information Management, 64, 102479.

Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37–50.

Kar, A. K., & Kushwaha, A. K. (2021). Facilitators and barriers of artificial intelligence adoption in business-insights from opinions using big data analytics. Information Systems Frontiers, 1–24.

Kaur, A., Garg, R., & Gupta, P. (2021). Challenges facing AI and big data for resourcepoor healthcare systems. The 2nd International Conference on Electronics and Sustainable Communication Systems (pp. 1426−1433).

Kickingereder, P., Isensee, F., Tursunova, I., et al. (2019). Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: A multicentre, retrospective study. Lancet Oncology, 20(5), 728-740. https://doi.org/10.1016/S1470-2045(19)30098-1

Kinnings, S. L., Liu, N., Tonge, P. J., Jackson, R. M., Xie, L., & Bourne, P. E. (2011). A machine learning-based method to improve docking scoring functions and its application to drug repurposing. Journal of Chemical Information and Modeling, 51(2), 408–419. https://doi.org/10.1021/ci100369f

Kumar, J. N. A., & Suresh, S. (2019). A proposal of smart hospital management using hybrid cloud, IoT, ML, and AI. The International Conference on Communication and Electronics Systems (pp. 1082−1085).

Kumar, P., Sharma, S. K., & Dutot, V. (2023). Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation. International Journal of Information Management, 69, 102598.

Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96.

Li, D., Madden, A., Liu, C., Ding, Y., Qian, L., & Zhou, E. (2018). Modelling online user behaviour for medical knowledge learning. Industrial Management & Data Systems, 118(4), 889-911. https://doi.org/10.1108/IMDS-02-2018-0062

Li, H., Zhang, Z., & Liu, Z. (2017). Application of artificial neural networks for catalysis: A review. Catalysts, 7(10), 306. https://doi.org/10.3390/catal7100306

Liang, Y., Chen, Z., Ward, R., & Elgendi, M. (2018). Photoplethysmography and deep learning: Enhancing hypertension risk stratification. Biosensors, 8(4), 101. https://doi.org/10.3390/bios8040101

PDF
Full Text
Export Citation

View Dimensions


View Plumx



View Altmetric



11
Save
0
Citation
470
View
6
Share