The DOI matches its Crossref record; 180 of the 180 references it cites were found in a registry or an index.
Significance of machine learning in healthcare: Features, pillars and applications
Open accessAbstract
Machine Learning (ML) applications are making a considerable impact on healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the speed and accuracy of physicians' work. Countries are currently dealing with an overburdened healthcare system with a shortage of skilled physicians, where AI provides a big hope. The healthcare data can be used gainfully to identify the optimal trial sample, collect more data points, assess ongoing data from trial participants, and eliminate data-based errors. ML-based techniques assist in detecting early indicators of an epidemic or pandemic. This algorithm examines satellite data, news and social media reports, and even video sources to determine whether the sickness will become out of control. Using ML for healthcare can open up a world of possibilities in this field. It frees up healthcare providers' time to focus on patient care rather than searching or entering information. This paper studies ML and its need in healthcare, and then it discusses the associated features and appropriate pillars of ML for healthcare structure. Finally, it identified and discussed the significant applications of ML for healthcare. The applications of this technology in healthcare operations can be tremendously advantageous to the organisation. ML-based tools are used to provide various treatment alternatives and individualised treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care. Shortly, ML will impact both physicians and hospitals. It will be crucial in developing clinical decision support, illness detection, and personalised treatment approaches to provide the best potential outcomes.
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Cites
2 retracted
Sujitha, R. A., & Seenivasagam, V. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02071-2 · Cited by 55
RetractedLi, Y., Shan, B., Li, B., Liu, X., & Pu, Y. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/9739219 · Cited by 49
RetractedA guide to deep learning in healthcare
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Deep learning for healthcare: review, opportunities and challenges
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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Mohan, S., Thirumalai, C., & Srivastava, G. (2019) · IEEE Access · doi:10.1109/access.2019.2923707 · Cited by 1,951
Artificial Intelligence (AI) applications for COVID-19 pandemic
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews · doi:10.1016/j.dsx.2020.04.012 · Cited by 1,497
Self-citationDisease Prediction by Machine Learning Over Big Data From Healthcare Communities
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Do no harm: a roadmap for responsible machine learning for health care
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Cited by
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915
Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100148 · Cited by 201
Artificial intelligence in nursing: an integrative review of clinical and operational impacts
Hassanein, S., El Arab, R. A., Abdrbo, A., Abu-Mahfouz, M. S., Gaballah, M. K. F., Seweid, M. M., Almari, M., & Alzghoul, H. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1552372 · Cited by 134
Badawy, W., Zinhom, H., & Shaban, M. (2025) · International Nursing Review · doi:10.1111/inr.13059 · Cited by 80
Saady, M., Eissa, M., Yacoub, A. S., Hamed, A. B., & Azzazy, H. M. E.-S. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103066 · Cited by 15
Metadata
- Authors
- Javaid, M., Haleem, A., Pratap Singh, R., Suman, R., & Rab, S.
- Venue
- International Journal of Intelligent Networks
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 3
- Pages
- 58-73
- ISSN
- 2666-6030 (ISSN-L)
- OpenAlex
- W4281630308
- Cited by
- 641
- References
- 180
- FWCI
- 31.50
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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