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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63721
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Artificial Intelligence and data science
Amrutwahini College of Engineering,Sangamner
Artificial Intelligence and data science
Amrutwahini College of Engineering,Sangamner
Artificial Intelligence and data science
Amrutwahini College of Engineering,Sangamner
Artificial Intelligence and data science
Amrutwahini College of Engineering,Sangamner
Artificial Intelligence and data science
Amrutwahini College of Engineering,Sangamner
The rapid growth of healthcare data—arising from electronic health records (EHRs), medical imaging, wearable sensors, and patient generated content—offers unprecedented potential for improving patient outcomes, optimizing operations, and informing policy. However, extracting value from such large, heterogeneous data sets requires sophisticated tools, robust analytical frameworks, and careful attention to challenges such as privacy, interoperability, real time processing, and data quality. This paper synthesizes recent research to: (i) characterize major sources of healthcare big data; (ii) overview the tools, frameworks, and analytics types currently in use; (iii) propose a conceptual framework for real time healthcare prediction; and (iv) discuss challenges and opportunities for future work. The survey is motivated by the increasing burden of chronic disease, the push for predictive and preventative medicine, and the demand for healthcare systems that can adapt in near real time.
KEYWORDS
Big data analytics promises significant improvements in healthcare: better outcomes, more efficient operations, earlier detection of disease, and more personalized care. The 2024 review by Badawy et al. provides a useful synthesis of where the field is right now: key data sources, analytical tools, architectures, and the common roadblocks. To move forward, research and implementation need to address both the technical (data quality, latency, integration) and non‑technical (privacy, regulatory, human resources) challenges. Real‑time predictive frameworks, standardization, and ethical AI are especially critical. With coordinated effort among healthcare providers, technologists, policymakers, and patients, many of these opportunities can be realized.
Badawy, Mohammed; Ramadan, Nagy; Hefny, Hesham Ahmed. “Big data analytics in healthcare: data sources, tools, challenges, and opportunities.” Journal of Electrical Systems & Information Technology, Vol. 11, No. 1, Article 63 (2024).“Leveraging Big Data Analytics in Healthcare Enhancement: Trends, Challenges and Opportunities,” Arshia Rehman, Saeeda Naz, Imran Razzak. arXiv preprint.“Big data analytics in the healthcare sector: Opportunities and challenges in developing countries. A literature review” (2024).
Publication Date
5/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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