Importance of Biostatistics available courses and teaching institutions in India

Article Type:  Editorial

Year: 2025; Volume: 5; Issue: 4; Page No: 6–7

Authors:  Senthilvel Vasudevan

https://doi.org/10.55349/ijmsnr.20255467

Affiliations:  Assistant Professor in Statistics (Biostatistics), Department of Community Medicine, Sri Venkateshwaraa Medical College Hospital & Research Centre, Ariyur, Pondicherry, India.  Email ID: senthilvel99@gmail.com

Article Summary: Submitted: 12-October-2025 Revised: 23-October-2025  Accepted: 19-November-2025  Published: 31-December-2025

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Introduction

Biostatistics is a scientific subject related to the application of statistical techniques and models in the area of science, medical, biomedical sciences, and its related researches.[1] Artificial Intelligence (AI) has introduced in the field of Biostatistics to open for new types of data analysis, interpretations and for presentation of data.  AI related methods can analyze a huge amount of data, find the complicated methods/patterns, and to make decision/predictions with higher degree of accuracy (or) precision. [2]

AI is very much useful and revolutionizing in the field of biomedical research and healthcare development.  This area of development is covering various departments like computer science, and it includes the algorithms and various statistical models which has enable machines has to perform better results in its tasks.  AI needs the human intelligence also like data analysis, planning, and pattern findings, and to take a good decision in the respective field.

Past way of Artificial Intelligence in Biostatistics

In the year of 1980 onwards, AI application had been used in the field of medical data analysis.  It was used in the development of machine learning algorithms and deep learning techniques.  [3]

Uses of Artificial Intelligence in Medical Research and Healthcare [4]

To analyze large datasets and identify patterns that may not be apparent through traditional statistical methods.  Develop predictive models that can forecast patients’ outcomes and identify high-risk patients.  Improve the efficiency and accuracy of clinical trials.

Applications of Artificial Intelligence in Biostatistics

a).  Predictive modelling and Risk Assessment

Predictive modelling is a key application of AI in Biostatistics.  By analysing large datasets.  AI algorithms can identify patterns and develop predictive models that can forecast patient outcomes.  Various ways AI is being used in predictive modelling and risk assessment include as follows:

i).  Developing models that can predict patient outcomes based on clinical and genomics data.

ii).  Identifying high-risk patients and predicting the likelihood of disease progression.

iii).  Improving the accuracy of risk assessment and patient stratification.

b).  Clinical Trials Data Analysis

Clinical trials are a crucial aspect of biomedical research, and AI can play a significant role in analyzing the data generated from these trials.  AI algorithms can identify patterns and trends in the data that may not be apparent through traditional statistical methods.  Help to identify potential safety issues and predict patient outcomes.  Improve the efficiency of clinical trials by identifying the most effective treatments and patient populations.

c).  Genomics and Precision Medicine

The advent of genomics and precision medicine has led to a significant increase in the amount of genomic data being generated.  AI can help to analyse this data and identify patterns that can inform personalized treatment decisions.

In other ways AI is being used in genomics and precision medicine include, identifying genetic variants associated with disease.  Developing predictive models that can forecast patient outcomes based on genomic data.  Improving the accuracy of genomic diagnosis.  [5]

Advantages of AI in Biostatistical Analysis

The use of AI in biostatistics has several advantages, including improved accuracy and efficiency in data analysis.  Ability to handle large and complex datasets and identification of patterns and trends that may not be apparent through traditional statistical methods.

Conclusion

From this article, I concluded that the perfect tool of AI in biostatistics is used to biomedical research and healthcare facilities.  AI algorithms can analyse the huge set of datasets, identify complex patterns, and make predictions with more precision.  But AI have many challenges and limitations to be considered.  IA will be used in biostatistics is likely to be designed by the improvement in machine learning and deep learning, availability of large datasets related to medical, biomedical, and healthcare, and the need for improvement of technology and personalized medicine.

References

1. Biostatistics.  Available from:      https://biostatistics.io/insight/role-of-biostatistics-in-public-health/  [Last Accessed on: 20^th July 2025]
2. Choudhury P, Goel P. Interface of Artificial Intelligence with Conventional Biostatistics in Healthcare Research. Methods Mol Biol. 2025;2952:411-428. doi: 10.1007/978-1-0716-4690-8_22. PMID:
3. Fieschi M. The Devvelopment of AI in Medicine and the Research Environment of the SPHINX Project at the Start of the 1980s.  IMIA Yearbook of Medical Informatics 2012.  Available from: https://www.thieme-connect.com/products/ejournals/pdf/10.1055/s-0038-1639447.pdf
4. Faiyazuddin M, Rahman SJQ, Anand G, Siddiqui RK, Mehta R, Khatib MN, Gaidhane S, Zahiruddin QS, Hussain A, Sah R. The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. Health Sci Rep. 2025 Jan 5;8(1):e70312. doi: 10.1002/hsr2.70312.  PMID: 39763580; PMCID: PMC11702416
5. Available from:      https://biostatistics.io/insight/role-of-biostatistics-in-public-health/  [Last Accessed on: 20^th July 2025]
6. Choudhury P, Goel P. Interface of Artificial Intelligence with Conventional Biostatistics in Healthcare Research. Methods Mol Biol. 2025;2952:411-428. doi: 10.1007/978-1-0716-4690-8_22. PMID: 40553345
7. Fieschi M. The Devvelopment of AI in Medicine and the Research Environment of the SPHINX Project at the Start of the 1980s.  IMIA Yearbook of Medical Informatics 2012.  Available from: https://www.thieme-connect.com/products/ejournals/pdf/10.1055/s-0038-1639447.pdf
8. Faiyazuddin M, Rahman SJQ, Anand G, Siddiqui RK, Mehta R, Khatib MN, Gaidhane S, Zahiruddin QS, Hussain A, Sah R. The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency. Health Sci Rep. 2025 Jan 5;8(1):e70312. doi: 10.1002/hsr2.70312. PMID: 39763580; PMCID: PMC11702416

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