Integrating Artificial Intelligence, Statistics, and Health: Toward a Translational and Trustworthy Evidence Ecosystem

Authors

  • Ravi Kant Computational Drug and Vaccine Discovery Laboratory, Faculty of Applied Sciences & Biotechnology, Shoolini University, Solan, Himachal Pradesh 173229, India Author https://orcid.org/0009-0007-6348-4638
  • Saurabh Mazumdar Computational Drug and Vaccine Discovery Laboratory, Faculty of Applied Sciences & Biotechnology, Shoolini University, Solan, Himachal Pradesh 173229, India Author

DOI:

https://doi.org/10.61171/pioneerjbiostat.4.1.2026.128

Abstract

Regarding public health, the statistics and artificial intelligence (AI) combination has many advantages. Improvement in monitoring of disease, outbreak prediction, and resource allocation are managed through the analysis of data. In terms of international health crises, such as pandemics, AI-based models along with epidemiological statistics are utilized to make policy decisions, enhance the management strategies, and follow the effectiveness in the population. Even so, these AI uses must be guided by statistical ideals, which preclude confusing inferences as well as unintended results. 

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Published

2026-04-12

Issue

Vol. 4 No. 1 (2026)

Section

Editorial(s)

License

Copyright (c) 2026 Ravi Kant, Saurabh Mazumdar (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

1.
Kant R, Mazumdar S. Integrating Artificial Intelligence, Statistics, and Health: Toward a Translational and Trustworthy Evidence Ecosystem. Pioneer J Biostat Med Res [Internet]. 2026 Apr. 12 [cited 2026 Apr. 12];4(1). Available from: https://www.pjbmr.com/index.php/pjbmr/article/view/128