Journal of Angiotherapy
Integrative Biomedical Research | Online ISSN  2207-872X
  1. You are here:
  2. Home
  3. Journals
  4. Biological Sciences
REVIEWS   (Open Access)
Contents Vol 8 (12)

Potentiality And Challenges of Machine Learning in Healthcare

Bayazid Hossain 1, Tanveer Ehsan Chowdhury 2, MD. Amir Hossain Fahad 3, Md. Emran Hossen 4, Rafiu Ahmed 5, Aolatun Nesa 6, Mahi Nur 7, Fuad Mohammad Rafiq 8, Md. Abu Sufian 9, Nasrullah Masud 10, Nafis Tasnim Turjo 11, Atiqur Rahman Sunny 12*  

+ Author Affiliations

Journal of Angiotherapy 8 (12) 1-8 https://doi.org/10.25163/angiotherapy.81210205

Submitted: 07 October 2024 Revised: 28 November 2024  Published: 02 December 2024 

Abstract

Background: Machine Learning (ML) is transforming healthcare by improving diagnosis, treatment methodologies, and patient management. It has seen significant success in medical imaging, predictive analytics, and robotic-assisted operations, improving accuracy and efficiency. Still, problems like data security, algorithmic bias, regulatory compliance, and the need for explainable artificial intelligence (XAI) make it hard to use on a large scale. Methods: This review study employs systematic literature review methodology, examining contemporary research on machine learning applications in healthcare. Peer-reviewed publications, case studies, and regulatory guidelines were analyzed to evaluate the advantages, constraints, and possible solutions for incorporating machine learning in medical practice. Primary focal points encompass precision, ethical implications, and technical progressions in machine learning-driven healthcare systems. Results: Research demonstrates that machine learning substantially improves medical decision-making by augmenting diagnostic accuracy, facilitating personalized therapies, and streamlining healthcare procedures. Federated learning has arisen as a viable method for protecting data privacy while enabling collaborative model training. AI-powered wearable devices are essential for real-time health surveillance and early illness identification. Nonetheless, ethical dilemmas, interoperability challenges, and the absence of standardized regulatory frameworks remain significant barriers to the widespread use of machine learning in healthcare. Conclusion: Explainable AI, federated learning, and global AI governance frameworks are crucial for cultivating trust and ensuring responsible AI integration. Confronting these problems via interdisciplinary cooperation, uniform legislation, and policy formulation will improve AI-driven healthcare solutions. In the future, researchers should focus on making machine learning models better, making sure that AI is used in an ethical way, and coming up with complete rules to support AI driven healthcare that is both long-lasting and important.

Keywords: Machine Learning, Healthcare, Predictive Analytics, Data Privacy, Personalized Medicine

References

Acs, B., & Rimm, D. L. (2018). Computational pathology: Deep learning in histopathology. Clinical Cancer Research, 24(21), 5456–5462. https://doi.org/10.1158/1078-0432.CCR-18-0685

Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1–9. https://doi.org/10.1186/s12911-020-01332-6

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., ... & Corrado, G. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954–961. https://doi.org/10.1038/s41591-019-0447-x

Begum, N. B. N., Chowdhury, M. E. C. M. E., Chowdhury, R. C. R., Begum, K. B. K., Selim, S. K. S. S. K., Hoque, J. H. J., & Sazzad, S. A. S. S. A. (2023a). Globalization and Textile Merchandising: How Global Supply Chains Influence Product Positioning and Market Research. Pathfinder of Research, 1(2), 13-13.

Begum, N. B. N., Mahmud, C. T. M. C. T., Chowdhury, M. E. C. M. E., Chowdhury, R. C. R., Begum, K. B. K., Selim, S. K. S. S. K., ... & Sazzad, S. A. S. S. A. (2023b). Innovative Visual Merchandising Strategies in the Digital Era: Enhancing Retail Consumer Engagement. Pathfinder of Research, 1(2).

Begum, N., Chowdhury, R., Khan, W., & Sazzad, S. A. (2022). Sustainable Merchandising: Integrating Eco-Friendly Practices in Retail Product Presentation. Pathfinder of Research, 3(1), 12-12.

Chowdhury, T. E., Chowdhury, R., Alam, S. M. S., & Sazzad, S. A.. (2020). Empowering Change: The Impact of Microcredit on Social BusinessDevelopment. Pathfinder of Research, 1(1), 13-13.

Chowdhury, T. E., Chowdhury, R., Chaity, N. S., & Sazzad, S. A. (2021). From Shadows to Sunrise: The Impact of Solar Power Plants onEnhancing Bangladesh's Economy. Pathfinder of Research, 2(1), 16-16.

Chowdhury, T. E., Chowdhury, R., Rahman, M. M., & Sunny, A. R. (2022). From Crisis to Opportunity: How Covid-19 Accelerated the Global Shift to Online Business. Pathfinder of Research, 3(1), 18-18.

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

Fogel, A. L., & Kvedar, J. C. (2018). Artificial intelligence powers digital medicine. NPJ Digital Medicine, 1(1), 1–4. https://doi.org/10.1038/s41746-017-0012-2

Goodman, B., Flaxman, S., & Shulman, C. (2020). European regulations on algorithmic decision-making and a "right to explanation". AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-020-00934-7

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402–2410. https://doi.org/10.1001/jama.2016.17216

Hassan, S. M. M., Sazzad, S. A., Selim, S. K., & Al Mozahid, A. (2024). Effect of Covid-19 Pandemic on Specific Crime in Dhaka, Bangladesh. Pathfinder of Research, 2(2), 12-12.

Huang, S., Yang, J., Fong, S., & Zhao, Q. (2022). Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Letters, 529, 99–110. https://doi.org/10.1016/j.canlet.2021.12.010

Ifty, S.M.H, 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

Ifty, S.M.H., Irin, F., Shovon, M.S.S., Amjad, M.H.H., Bhowmik, P.K., Ahmed, R., Ashakin, M.R., Hossain, B., Mushfiq, M., Sattar, A., Chowdhury, R. & Sunny, A.R. (2024). Advancements, Applications, and Future Directions of Artificial Intelligence in Healthcare, Journal of Angiotherapy, 8(8), 1-18, 9843, 10.25163/angiotherapy.889843

Ifty, S.M.H., S.M., Ashakin, M.R., Hossain, B., Afrin, S., Sattar, A., Chowdhury, R., Tusher, M.I., Bhowmik, P.K., Mia, M.T., Islam, T., Tufael, M. & Sunny, A.R.  (2023a). IOT-Based Smart Agriculture in Bangladesh: An Overview. Applied Agriculture Sciences, 1(1), 1-6. 9563, 10.25163/agriculture.119563

Islam, M. M., Sunny, A. R., Hossain, M. M., & Friess, D. A. (2018). Drivers of mangrove ecosystem service change in the Sundarbans of Bangladesh. Singapore Journal of tropical geography, 39(2), 244-265.

Islam, M. R., Sunny, A. R., Sazzad, S. A., Dutta, A., Hasan, N., Miah, M. F., ... & Prodhan, S. H. (2023). Environmental Jeopardy and Coping Strategies of the Small-Scale Fishers in the Bangladesh Sundarbans: The Precedent of the World's Largest Mangrove. Egyptian Journal of Aquatic Biology & Fisheries, 27(6). Doi:10.21608/ejabf.2023.330198

Jo, T., Nho, K., & Saykin, A. J. (2019). Deep learning in Alzheimer's disease: Diagnostic classification and prognostic prediction using neuroimaging data. Frontiers in Aging Neuroscience, 11, 220. https://doi.org/10.3389/fnagi.2019.00220

Johnson, A. E. W., Pollard, T. J., Shen, L., Li-wei Lehman, L., Feng, M., Ghassemi, M., ... & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. https://doi.org/10.1038/sdata.2016.35

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., ... & Zhao, S. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083

Kantarjian, H. M., & Yu, P. P. (2015). Artificial intelligence, big data, and cancer. Journal of the American Society of Clinical Oncology, 33(29), 3217–3218. https://doi.org/10.1200/JCO.2015.63.8537

Kim, H., Lee, S., & Choi, S. (2021). Challenges and opportunities in deep learning-based medical artificial intelligence. Biomedical Engineering Letters, 11(1), 1–15. https://doi.org/10.1007/s13534-021-00192-2

Kuddus, M. A., Alam, M. J., Datta, G. C., Miah, M. A., Sarker, A. K., & Sunny, M. A. R. (2021). Climate resilience technology for year-round vegetable production in northeastern Bangladesh. International Journal of Agricultural Research, Innovation and Technology (IJARIT), 11(2355-2021-1223), 29-36.

Kuddus, M. A., Datta, G. C., Miah, M. A., Sarker, A. K., Hamid, S. M. A., & Sunny, A. R. (2020). Performance study of selected orange fleshed sweet potato varieties in north eastern bangladesh. Int. J. Environ. Agric. Biotechnol, 5, 673-682.

Kuddus, M. A., Sunny, A. R., Sazzad, S. A., Hossain, M., Rahman, M., Mithun, M. H., ... & Raposo, A. (2022). Sense and Manner of WASH and Their Coalition with Disease and Nutritional Status of Under-five Children in Rural Bangladesh: A Cross-Sectional Study. Frontiers in Public Health, 10, 890293.

Labovitz, D. L., Shafner, L., Reyes Gil, M., Virmani, D., & Hanina, A. (2017). Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke, 48(5), 1416–1419. https://doi.org/10.1161/STROKEAHA.116.016281

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2023). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 39(1), 50–67. https://doi.org/10.1109/MSP.2022.3145436

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Suleiman, M. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://doi.org/10.1038/s41586-019-1799-6

Mithun, M. H., Shaikat, M. F. B., Sazzad, S. A., Billah, M., Al Salehin, S., Foysal, M., ... & Sunny, A. R. (2024). Microplastics in Aquatic Ecosystems: Sources, Impacts, and Challenges for Biodiversity, Food Security, and Human Health-A Meta Analysis. Journal of Angiotherapy, 8(11), 1-12.

Moniruzzaman, Sazzad, S. A., Hoque, J., & Sunny, A. R. (2023). Influence of Globalization on Youth Perceptions on ChangingMuslim Rituals in Bangladesh. Pathfinder of Research, 1 (1), 11-22.

Nguyen, H. G., Lin, Y., Wang, X., & Yoon, S. W. (2021). Application of deep learning techniques for skin cancer diagnosis. Scientific Reports, 11(1), 1–12. https://doi.org/10.1038/s41598-021-88241-1

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342

Patel, B. N., Rosenberg, L., Willcox, G., Baltaxe, D., Lyons, M., Irvin, J., ... & Lungren, M. P. (2022). Human–machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digital Medicine, 5(1), 1–9. https://doi.org/10.1038/s41746-021-00497-5

Raghu, S., Pradeep, R., & Devi, B. S. (2021). AI-enabled wearable health devices: Innovations and challenges. IEEE Transactions on Biomedical Engineering, 68(6), 1854–1865. https://doi.org/10.1109/TBME.2021.3051487

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259

Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22–28. https://doi.org/10.1177/0141076818815510

Sazzad, S. A. S. S. A., Ana, R. A. R. S. R., Shawon, R., Moniruzzaman, M., Hussain, M. H. M., & Zaman, F. Z. F. (2024a). Climate Change and Socioeconomic Challenges of Fishing Communities in the Coastal District of Shariatpur in Bangladesh. Pathfinder of Research, 2(1).

Sazzad, S. A., Billah, M., Sunny, A. R., Anowar, S., Pavel, J. H., Rakhi, M. S., ... & Al-Mamun, M. A. (2023). Sketching Livelihoods and Coping Strategies of Climate Vulnerable Fishers. Egyptian Journal of Aquatic Biology & Fisheries, 27(4).

Sazzad, S. A., Mithun, M. H., Ahmed, A., Samiullah, M., Hamid, M. A., Shawon, R. A. R., ... & Sunny, A. R. (2024b). Nomad Fishers: A Socially Excluded and Climate Vulnerable Fishing Community in Bangladesh. Egyptian Journal of Aquatic Biology & Fisheries, 28(5).

Sharma, M., Aggarwal, S., & Sharma, S. (2023). Federated learning for healthcare applications: Current trends and future directions. Journal of Biomedical Informatics, 131, 104217. https://doi.org/10.1016/j.jbi.2023.104217

Stein, J. D., Khawaja, A. P., & Weizer, J. S. (2019). Glaucoma in adults—screening, diagnosis, and management: A review. JAMA, 321(19), 1901–1912. https://doi.org/10.1001/jama.2019.3183

Sunny, A. R., Rahman, M. A., Hasan, M. N., Bhuyian, M. S., Miah, M. F., Ashrafuzzaman, M., Pervin, A., Rahman, J. F., & Prodhan, S. H. (2025). Hilsa (Tenualosa ilisha) genetic diversity and conservation strategies for sustainable wetland management in northeastern Bangladesh. Egyptian Journal of Aquatic Biology & Fisheries, 29(1), 1089-1105.

Sunny, A. R., Salam, M. T., Bari, K. F., & Rana, M. S. (2023). Artificial Intelligence in Addressing Cost, Efficiency, and Access Challenges in Healthcare. Journal of Primeasia, 4(1), 1-5.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.

Walters, J., Mishra, S., & Williams, B. (2021). AI in drug discovery: Applications and challenges. Nature Reviews Drug Discovery, 20(8), 564–579. https://doi.org/10.1038/s41573-021-00195-7

Xu, Y., Yang, X., & Zhou, J. (2023). AI-driven cancer detection: A systematic review of recent advances. Cancer Research, 83(2), 123–134. https://doi.org/10.1158/0008-5472.CAN-22-1872

Abstract
Export Citation

View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
10
View
0
Share