AI in Healthcare: Drug Delivery in Tuberculosis

Year : 2025 | Volume : 13 | Issue : 01 | Page :
    By

    Amol T. Ubale,

  • Vivek S. Tarate,

  • Amar S. Kulkarni,

  • Prachi B. Kale,

  • Janhavi D. Joshi,

  • Vinod V. Kamble,

  • Prem S. Khadpe,

  1. Vice Principal, Department of Pharmacy, Vijayrao Naik College of Pharmacy, Shirval Kankavli Sindhudurg, Maharashtra, India
  2. Associate Professor, Department of Pharmacy, Late Narayansas Bhawandas Chhabda Institute of Pharmacy (B. Pharm), Raigaon, Satara, Maharashtra, India
  3. Associate Professor, Department of Pharmacy, Late Narayansas Bhawandas Chhabda Institute of Pharmacy (B. Pharm), Raigaon, Satara, Maharashtra, India
  4. Student, Department of Pharmacy, Vijayrao Naik College of Pharmacy, Shirval Kankavli Sindhudurg, Maharashtra, India
  5. Student, Department of Pharmacy, Vijayrao Naik College of Pharmacy, Shirval Kankavli Sindhudurg, Maharashtra, India
  6. Student, Department of Pharmacy, Vijayrao Naik College of Pharmacy, Shirval Kankavli Sindhudurg, Maharashtra, India
  7. Student, Department of Pharmacy, Vijayrao Naik College of Pharmacy, Shirval Kankavli Sindhudurg, Maharashtra, India

Abstract

Tuberculosis (TB) , mainly caused by Mycobacterium tuberculosis, remains a major global health burden, accounting for millions of new infections and deaths each year. Although progress has been made in diagnosis and treatment, the growing threat of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB makes disease control increasingly difficult. Conventional diagnostic approaches such as chest X-rays, sputum smear microscopy, and culture methods continue to play an important role, but they face limitations in sensitivity, turnaround time, and accessibility, particularly in low-resource settings. In this context, artificial intelligence (AI) is emerging as a powerful tool for TB management by improving screening precision, accelerating diagnosis, strengthening public health surveillance, and assisting in treatment monitoring. AI-based advances are also shaping drug delivery systems, where machine learning supports nanocarrier design, targeted release, and personalized dosing, leading to safer and more effective therapies. Still, barriers such as limited datasets, high implementation costs, technical challenges, and unresolved regulatory and ethical issues persist. Looking ahead, AI applications in TB are expected to expand further, with integration into clinical workflows, smartphone-based adherence monitoring, real time tracking of treatment response, and predictive models for patient outcomes. Ultimately, incorporating AI into TB prevention and care offers great promise for advancing global control strategies and improving health outcomes provided there is sustained investment, equitable access, and strong validation frameworks.

Keywords: Artificial Intelligence, Healthcare, Drug delivery, Tuberculosis, Mycobacterium tuberculosis

[This article belongs to Trends in Drug Delivery ]

How to cite this article:
Amol T. Ubale, Vivek S. Tarate, Amar S. Kulkarni, Prachi B. Kale, Janhavi D. Joshi, Vinod V. Kamble, Prem S. Khadpe. AI in Healthcare: Drug Delivery in Tuberculosis. Trends in Drug Delivery. 2025; 13(01):-.
How to cite this URL:
Amol T. Ubale, Vivek S. Tarate, Amar S. Kulkarni, Prachi B. Kale, Janhavi D. Joshi, Vinod V. Kamble, Prem S. Khadpe. AI in Healthcare: Drug Delivery in Tuberculosis. Trends in Drug Delivery. 2025; 13(01):-. Available from: https://journals.stmjournals.com/tdd/article=2025/view=232634


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 27/09/2025
Accepted 18/11/2025
Published 19/11/2025
Publication Time 53 Days
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