The healthcare sector in India has experienced significant transformations owing to the advancement in technology and infrastructure. Despite these transformations, there are major challenges to address critical issues like insufficient healthcare infrastructure for the country’s huge population, limited accessibility, shortage of skilled professionals, and high-quality care. Artificial intelligence (AI)-driven solutions have the potential to lessen the stress on India’s healthcare system; however, integrating trustworthy AI in the sector remains challenging due to ethical and regulatory constraints. This study aims to critically review the current status of the development of AI systems in Indian healthcare and how well it satisfies the ethical and legal aspects of AI, as well as to identify the challenges and opportunities in adoption of trustworthy AI in the Indian healthcare sector. This study reviewed 15 articles selected from a total of 1136 articles gathered from two electronic databases, PubMed and Google Scholar, as well as project websites. This study makes use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). It finds that the existing studies mostly used conventional machine learning (ML) algorithms and artificial neural networks (ANNs) for a variety of tasks, such as drug discovery, disease surveillance systems, early disease detection and diagnostic accuracy, and management of healthcare resources in India. This study identifies a gap in the adoption of trustworthy AI in Indian healthcare and various challenges associated with it. It explores opportunities for developing trustworthy AI in Indian healthcare settings, prioritizing patient safety, data privacy, and compliance with ethical and legal standards.

Recognizing the impact of mathematical learning difficulties on student achievement, this research focuses on developing adaptive, technology-based solutions for those struggling with learning mathematics, including individuals with dyscalculia. Dyscalculia, a difficulty in understanding numbers and mathematics, can profoundly affect a child’s academic progress and self-confidence. Many interventions aim for broad effectiveness but often struggle to address individual learning differences. This research addresses this gap by employing Dynamic Bayesian Networks (DBNs) within intelligent tutoring systems to develop a personalized, gamified approach for improving mathematical skills in children with dyscalculia. We assessed 158 children aged 6–10 years using the Smartick Dyscalculia Assessment Tool to identify specific numerical cognition deficits. Based on these assessments, we have developed EDSense (Early Detection and Intervention for Insufficient Number Sense), an adaptive web-based learning tool. EDSense provides personalized support and targets skill refinement in mathematics learning. A pre-test and post-test design evaluates EDSense’s effectiveness and demonstrates significant improvements in numerical abilities. The findings highlight the crucial role of adaptive learning platforms in addressing dyscalculia. The EDSense platform demonstrates gamified, self-directed learning environments to enhance both engagement and learning outcomes by accommodating individual cognitive differences. We have proposed a technology-driven framework for personalized dyscalculia interventions, emphasizing early detection to support mathematical skill development.

Cardiovascular illness is becoming more commonplace every day, which makes early identification of the condition worrisome and essential. Making a cardiac diagnosis is a challenging procedure that has to be finished fast and expertly. The identification and prediction of cardiovascular illness are vital medical responsibilities that help cardiologists appropriately diagnose and treat their patients. Deep Learning (DL) algorithms have found increasing usage in the medical industry because of their ability to recognize patterns in data. By applying machine learning to categorize the occurrence of cardiovascular illness, clinicians can reduce the rate of misdiagnosis. The goal of this work is to develop an ML model for cardiovascular disease (CVD) forecast based on correlated problems. This research develops a model that can accurately anticipate CVD illnesses, which will reduce the fatality rate from these ailments. This work employs a variety DL approaches to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. We used a benchmark dataset of UCI Heart disease prediction for this work, which consists of 14 different heart disease-related parameters. As DL models, there are Convolutional Nueral Network (CNN), Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). When compared to DL techniques, the result shows that LSTM offers superior prediction accuracy in less time. 91% accuracy was attained using this LSTM technique. This model performs better on training and testing data. The models were fitted to the test dataset as well as trained on the training dataset to see which fared best. The matrices acquired during this process were accuracy, specificity, sensitivity, Area Under Curve (AUC) and the Receiver Operating Characteristic Curve (ROC).

International Journal of GEOINFORMATICS