Leveraging Big Data Analytics for Personalized Cancer Treatment: An Overview of Current Approaches and Future Directions
Open accessAbstract
Big data analytics’ incorporation into customized cancer care is a revolutionary development in medical science. Using knowledge from genomic sequencing, electronic health records (EHRs), clinical trial databases, and social determinants of health, this systematic review investigates the relationship between artificial intelligence (AI), machine learning, and big data analysis in customizing cancer treatments for each patient. The study highlights how AI‐driven prediction models might improve treatment outcomes, reduce side effects, and increase patient safety by synthesizing insights from the last 10 years of academic research. A systematic literature review that follows PRISMA guidelines and includes both quantitative and qualitative evaluations of academic publications is among the key approaches. The findings show that multiomics approaches, which combine transcriptomics, proteomics, metabolomics, and genomics, are becoming increasingly important for customized medicine. Real‐time data analytics and wearable technology are also noted as promising resources for prompt responses. Notwithstanding obstacles, including data heterogeneity, moral dilemmas, and validation problems, the results highlight how crucial AI is to treating tumor complexity and developing precision medicine. In order to improve regulatory frameworks, promote interdisciplinary collaboration, and optimize AI applications in cancer treatment planning, the study concludes by suggesting future research areas. For scientists, physicians, and legislators hoping to transform cancer treatment using big data analytics–driven tailored medicine, this review provides insightful information.
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Integrating big data analytics with genomic sequencing and SDOH data improves personalized cancer care and patient safety.
“Integrating big data analytics with genomic sequencing and SDOH data improves personalized cancer care and patient safety [7].”
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Self-citation
- Authors
- Ahmed, M. K., Rozario, E., Mohonta, S. C., Ferdousmou, J., Saimon, A. S. M., Moniruzzaman, M., Manik, M. M. T. G., & Hasan, R.
- Affiliations
- International American University (US)
- Baruch College (US)
- Maharishi International University (US)
- Westcliff University (US)
- Corresponding author
- Hasan, Rakibul
- ORCID
- Venue
- Journal of Engineering
- Publisher
- Wiley
- Published
- 2025-01
- Volume
- 2025
- Issue
- 1
- ISSN
- 2314-4904 (ISSN-L)
- 2314-4912
- OpenAlex
- W4414323358
- Cited by
- 10
- References
- 53
- Citations by year
- 2026: 9 · 2025: 1
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Engineering (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- big data analytics, precision oncology, artificial intelligence in oncology, machine learning prediction models, multi-omics, electronic health records, genome sequencing, wearable devices, real-time analytics, social determinants of health, clinical trial data, data heterogeneity, AI ethics, regulatory framework
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