The Emergence of Artificial Intelligence in Healthcare: A Data-Driven Perspective

  • Unique Paper ID: 207264
  • PageNo: 164-168
  • Abstract:
  • The rapid development of data-centric technology and sophisticated computational techniques has led to a revolutionary change in the healthcare industry with the advent of Artificial Intelligence (AI). This study offers a thorough, data-driven viewpoint on the incorporation of AI into contemporary healthcare systems, highlighting its potential to improve patient outcomes, clinical decision-making, and operational effectiveness. AI techniques, such as machine learning, deep learning, and natural language processing, have become crucial tools for extracting meaningful insights and enabling evidence-based medical practices due to the exponential growth in healthcare data generated from sources like electronic health records (EHRs), medical imaging, wearable devices, and genomic datasets. AI-driven technologies, which enable early disease identification, risk assessment, and customized treatment plans, are being used more and more in diagnostic procedures, predictive analytics, and personalized medicine. These technologies reduce human error and assist well-informed clinical judgments by enabling healthcare workers to evaluate complicated and large-scale datasets more quickly and accurately than with traditional approaches. Additionally, by combining various data sources and spotting trends that support customized treatment strategies, AI advances precision medicine.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{207264,
        author = {Shruti V and Uma and Zahera H and Kuldeep Singh Rana},
        title = {The Emergence of Artificial Intelligence in Healthcare: A Data-Driven Perspective},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {164-168},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207264},
        abstract = {The rapid development of data-centric technology and sophisticated computational techniques has led to a revolutionary change in the healthcare industry with the advent of Artificial Intelligence (AI). This study offers a thorough, data-driven viewpoint on the incorporation of AI into contemporary healthcare systems, highlighting its potential to improve patient outcomes, clinical decision-making, and operational effectiveness. AI techniques, such as machine learning, deep learning, and natural language processing, have become crucial tools for extracting meaningful insights and enabling evidence-based medical practices due to the exponential growth in healthcare data generated from sources like electronic health records (EHRs), medical imaging, wearable devices, and genomic datasets. AI-driven technologies, which enable early disease identification, risk assessment, and customized treatment plans, are being used more and more in diagnostic procedures, predictive analytics, and personalized medicine. These technologies reduce human error and assist well-informed clinical judgments by enabling healthcare workers to evaluate complicated and large-scale datasets more quickly and accurately than with traditional approaches. Additionally, by combining various data sources and spotting trends that support customized treatment strategies, AI advances precision medicine.},
        keywords = {Artificial Intelligence, Data Analytics, Healthcare, Machine Learning, Medical Imaging, Predictive Healthcare, Personalized Medicine},
        month = {July},
        }

Cite This Article

V, S., & Uma, , & H, Z., & Rana, K. S. (2026). The Emergence of Artificial Intelligence in Healthcare: A Data-Driven Perspective. International Journal of Innovative Research in Technology (IJIRT), 164–168.

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