Responsible AI
Exploring fairness, ethics, transparency, accountability and human-centered approaches to artificial intelligence.
Research & Scholarship
Advancing responsible AI through interdisciplinary and applied research
Dr. Loveleen Gaur’s research connects artificial intelligence, healthcare, explainability, governance and data-driven innovation.
Her work explores how AI systems can become more transparent, responsible and useful across healthcare, education, organizations and society.
Research Vision
Research should create knowledge that is rigorous, responsible and useful
Artificial intelligence research creates greater value when technical advancement is considered alongside human impact, ethical responsibility and practical application.
Dr. Loveleen Gaur’s work brings together interdisciplinary research, applied analytics and responsible AI thinking to address complex institutional and societal challenges.
Her scholarship spans healthcare AI, medical imaging, explainable systems, generative AI, digital transformation, sustainability and algorithmic accountability.
Research Themes
Interdisciplinary areas of inquiry
Research themes connect artificial intelligence with healthcare, governance, organizational innovation, sustainability and society.
Explainable AI
Developing and evaluating interpretable AI systems that strengthen confidence, transparency and decision quality.
Healthcare AI
Applying artificial intelligence to clinical decision support, healthcare analytics, medical imaging and equitable care.
Medical Imaging
Using deep learning and explainable AI to support analysis of neurological disease, cancer and infectious conditions.
Generative AI
Studying responsible adoption, ethical implications, organizational use and governance of generative systems.
AI Governance & Policy
Connecting innovation with institutional accountability, policy, risk management and responsible deployment.
Data Science & Analytics
Using predictive, prescriptive and interpretive methods to support research, strategy and organizational decisions.
Sustainability & Society
Examining how technology, consumer behavior and intelligent systems influence sustainable development and social outcomes.
Selected Scholarship
Featured Research
A curated selection of research reflecting Dr. Loveleen Gaur’s work across healthcare AI, explainability, responsible systems and applied innovation.
Healthcare AI
Advancements of deep learning in medical imaging for neurodegenerative diseases
Frontiers in Neuroscience
Examines the role of deep learning in improving medical-image analysis and supporting research into neurodegenerative disease.
View researchExplainable AI
Explanation-driven deep learning model for prediction of brain tumour status using MRI image data
Frontiers in Genetics
Demonstrates how explainability can improve confidence and interpretability in AI-supported brain-tumour prediction.
View researchClinical Decision Support
Explanation-driven HCI model to examine the mini-mental state for Alzheimer’s disease
ACM Transactions on Multimedia Computing, Communications, and Applications
Connects human-computer interaction, explainability and AI-assisted cognitive assessment for Alzheimer’s disease.
View researchMedical Imaging
Medical image-based detection of COVID-19 using deep convolutional neural networks
Multimedia Systems
Investigates deep-learning approaches for identifying COVID-19 through medical-image analysis.
View researchResponsible AI
An integrated model to evaluate transparency in predicting employee churn using explainable artificial intelligence
Journal of Innovation & Knowledge
Explores transparency and interpretability in predictive organizational decision systems.
View researchSustainability
Artificial intelligence for carbon emissions using system of systems theory
Ecological Informatics
Examines how AI and systems thinking can support understanding and management of carbon emissions.
View researchResearch Impact
Scholarship with global reach
Research impact reflects sustained publication, international citation, peer-review service and recognition across academic communities.
Research Outputs
Journal articles, conference papers, books, edited volumes and scholarly chapters.
Citations
Research cited internationally across artificial intelligence, healthcare, analytics and technology.
Peer Reviews
Verified reviews across leading academic publishers and international journals.
Global Scientist Recognition
Recognized among the Elsevier–Stanford World’s Top 2% Scientists in 2024 and 2025.
Research Ecosystem
Collaboration across disciplines and institutions
Healthcare & Clinical AI
Medical imaging and decision support
Research across neurological disease, brain tumours, infectious disease, telemedicine and explainable clinical AI.
Responsible Technology
Ethics, governance and explainability
Scholarship addressing algorithmic accountability, responsible AI, transparency and institutional governance.
Academic Collaboration
International and interdisciplinary research
Collaboration with scholars and institutions across the United States, India, Malaysia, Peru, Fiji and other regions.
Editorial Leadership
Research quality and scholarly publishing
Editorial and peer-review contributions across Taylor & Francis, Frontiers, Springer, Elsevier, Wiley and other publishers.
Editorial Leadership
Supporting research quality and scholarly exchange
Editorial and peer-review contributions support the development, evaluation and dissemination of international research.
Taylor & Francis
Co-Editor-in-Chief
Communications in Statistics: Case Studies, Data Analysis and Applications
Frontiers
Topic and Review Editor
Neuroscience, Genetics, Artificial Intelligence, Bioinformatics and related research areas
Springer, Elsevier, Wiley & IEEE
Peer Reviewer
Research evaluation across AI, analytics, healthcare, technology and innovation journals
Research Profiles
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