Login/Register
ISSN 2583-2913
Home
About Us
Editorial Board
Current Issue
Archives
In Press
Submit Manuscript
Instructions
AWSHR
Contact Us
Latest Updates
Articles are invited for the first issue of Indian Journal of Science and Research.
Join Indian Journal of Science and Research as reviewer, editorial board member.
Congratulations!!! To all the members for Joining editorial board of Indian Journal of Science and Research.
Why Publish With Us
Fast reviewing process: Adopting the fast reviewing process we communicate with authors at all stages till publication. Proactive teams of reviewers from National and International front help us to complete the process within the time.
Article In Press
Title:
LEVERAGING AI TECHNOLOGY IN INTERPRETATION OF X-RAY IMAGES AND ITS INTEGRATION INTO THE PUBLIC HEALTH SYSTEM
Author:
Kartik Jain* and Ranjan Kumar Choudhury
Keyword:
AI diagnostics, Computer-aided detection, X-ray interpretation, Digital health
Page No:
143-154
DOI:
https://doi.org/10.5281/zenodo. 20455473
Abstract:
Artificial Intelligence (AI) is increasingly used in chest X-ray (CXR) interpretation to improve the speed and scalability of screening in resource-constrained settings like India. This study systemat
ically reviewed literature published after 2014, following PRISMA guidelines, to evaluate the diagnostic performance, cost, and efficiency of AI-assisted CXR interpretation compared to radiologists. Radiologists demonstrated an average sensitivity of 71.0% (95% CI: 68.0–76.2%) and specificity of 86.2% (95% CI: 84.0–87.8%), while AI showed higher sensitivity (86.8%; 95% CI: 81.5–90.4%) and comparable specificity (87.0%; 95% CI: 81.3–90.1%). Across prevalence levels relevant to India (0.5–5%), AI consistently yielded higher positive and negative predictive values, although PPV remained low at lower prevalence levels due to screening population characteristics. Sensitivity analyses indicated that AI reduced reporting time and costs under most scenarios, with variability depending on assumptions. AI demonstrates strong potential as a triage tool in large-scale screening programs such as tuberculosis under NTEP. However, its effectiveness depends on prevalence and implementation context. AI should be integrated as a complementary tool within existing workflows rather than a standalone replacement. Key-words: AI diagnostics, Computer-aided detection, X-ray interpretation, Digital health
Download PDF
Title:
SMART DIAGNOSTICS: AI AT THE HEART OF MODERN MEDICINE
Author:
Dr S. Selvam* and J. Jeyavarshana
Keyword:
Artificial Intelligence, Smart Diagnostics, Machine Learning, Healthcare Technology, Medical Imaging, Clinical Decision Support, Digital Health, Data Analysis.
Page No:
155-157
DOI:
DOI: https://doi.org/10.5281/zenodo. 20456009
Abstract:
Artificial Intelligence (AI) is transforming the healthcare industry by enabling faster, more accurate, and highly efficient diagnostic processes. Technologies powered by machine learning, deep learni
ng, and natural language processing are reshaping how diseases are detected, monitored, and treated. This paper explores how AI is integrated into healthcare diagnostics by comparing traditional methods with AI-based approaches. It also highlights the role of AI in medical education and IT-enabled healthcare systems, while addressing challenges such as data privacy, ethical concerns, and implementation barriers. The study includes data collection and analysis to evaluate how AI improves diagnostic accuracy and reduces human error. Findings show that AI-driven systems significantly enhance healthcare delivery by making it more reliable, accessible, and cost-effective. However, issues like technological dependency and limited infrastructure in developing countries still exist. The study concludes that AI should support healthcare professionals rather than replace them. Key-words: Artificial Intelligence, Smart Diagnostics, Machine Learning, Healthcare Technology, Medical Imaging, Clinical Decision Support, Digital Health, Data Analysis.
Download PDF
