TY  - JOUR
AU  - Alam, Md Ashraful
AU  - Sajib, Md Refat Uz Zaman
AU  - Rahman, Fariya
AU  - Ether, Saraban
AU  - Hanson, Molly
AU  - Sayeed, Abu
AU  - Akter, Ema
AU  - Nusrat, Nowrin
AU  - Islam, Tanjeena Tahrin
AU  - Raza, Sahar
AU  - Tanvir, K M
AU  - Chisti, Mohammod Jobayer
AU  - Rahman, Qazi Sadeq-ur
AU  - Hossain, Akm
AU  - Layek, MA
AU  - Zaman, Asaduz
AU  - Rana, Juwel
AU  - Rahman, Syed Moshfiqur
AU  - Arifeen, Shams El
AU  - Rahman, Ahmed Ehsanur
AU  - Ahmed, Anisuddin
PY  - 2024
DA  - 2024/10/28
TI  - Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review
JO  - J Med Internet Res
SP  - e54710
VL  - 26
KW  - machine learning
KW  - deep learning
KW  - artificial intelligence
KW  - big data analytics
KW  - public health
KW  - health care
KW  - mobile phone
KW  - Bangladesh
AB  - Background: The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies. Objective: This scoping review aims to collate (1) the existing research in Bangladesh’s health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research. Methods: MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English. Results: With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%). Conclusions: This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh’s health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e54710
UR  - https://doi.org/10.2196/54710
UR  - http://www.ncbi.nlm.nih.gov/pubmed/39466315
DO  - 10.2196/54710
ID  - info:doi/10.2196/54710
ER  -