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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.
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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
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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.
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Title:
STUDENT PREFERENCES TOWARD PROJECT-BASED LEARNING IN MATHEMATICS
Author:
Khin Myo Myo Minn and Hlaing Htake Khaung Tin*
Keyword:
Project-Based Learning, Mathematics Education, Student Preferences, Active Learning, Mathematical Projects.
Page No:
158-164
DOI:
https://doi.org/10.5281/zenodo.21320923
Abstract:
Abstract: To foster critical and analytical thinking skills in students, mathematics education is crucial. However, traditional methods emphasize memorization and repetition, making students lose inte
rest in participating in mathematics classes. Project-based learning (PBL) refers to an educational approach in which learners are encouraged to engage in various activities, including creative learning, collaboration, and real-life implementation of theoretical aspects in mathematics. The current research paper examines the choice of students regarding project-based activities in mathematics disciplines. This paper attempts to explore whether mathematical projects encourage students' motivation, learning process, interest, and academic performance. For that reason, a survey methodology was used, and 100 students were carefully chosen from secondary and university-level institutions. The data collection procedure included the use of questionnaires, while the information was evaluated via factual statistics. The results suggest that most students choose project-based activities because such an approach helps them gain conceptual knowledge, teamwork abilities, creativity skills, and apply mathematics in practice. In addition, students indicated that mathematical projects help them avoid fear and anxiety in dealing with complex aspects in mathematics. Keywords: Project-Based Learning, Mathematics Education, Student Preferences, Active Learning, Mathematical Projects.
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Title:
A COMPARATIVE SURVEY ANALYSIS OF USER PREFERENCES BETWEEN AI-GENERATED IMAGES AND REAL PHOTOGRAPHIC IMAGES
Author:
Khin Myo Myo Minn, Ciin Zam Man and Hlaing Htake Khaung Tin*
Keyword:
Artificial Intelligence, AI-Generated Images, Real Images, User Preference, Survey Analysis
Page No:
165-172
DOI:
https://doi.org/10.5281/zenodo.21321143
Abstract:
The emergence of new AI technologies has greatly impacted the process of digital image creation. Currently, AI-generated images can be found in such spheres of life as advertising, entertainment, educ
ation, social media, and graphic design. Nevertheless, along with the foster in the realism of the images, there arise concerns whether users tend to prefer AI-generated visuals to real photographic images. The present research paper is dedicated to finding the answer to this question by studying user preferences for AI-generated images over real images using the method of survey analysis. A survey containing specific questions regarding preferences was selected among 100 contributors from different age categories and levels of education. The survey measured preferences of participants according to realism, creativity, emotional effect, trust, visual quality, and general appeal of images. Percentage analysis and mean score analysis was designed for statistical data processing. It was discovered that although AI-generated images have greater creativity and artistic value, real images remain more trustworthy and emotionally appealing to users.
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