Guest Editors
Kailas Patil
Vishwakarma University
Prof.(Dr.) Kailas Patil is the Dean of the Faculty of Science and Technology and Chief Information Security Officer (CISO) at Vishwakarma University, Pune, India. He completed his PhD in computer science from the National University of Singapore (NUS) and recently finished postdoctoral research at Kasetsart University, Thailand (2023–2024) focusing on data-driven approaches for agriculture and environmental applications. He holds an M.E. in Computer Engineering from the University of Pune and a B.E. in Computer Engineering from North Maharashtra University. With over 20 years of teaching and research experience, his work spans cyber security, web security, network security, Internet of Things (IoT), and machine learning. Prof. Patil has authored numerous SCI/Scopus-indexed publications, books, patents, and research datasets, and is recognized among India’s top cybersecurity researchers by NCIIPC.
Sital Dash
Vishwakarma University
Prof. Sital Dash is an assistant professor in the Department of Computer Engineering, Faculty of Science and Technology, Vishwakarma University, Pune, India. She holds an M.Tech in Information Security from the National Institute of Technology (NIT) Durgapur and a B.Tech in Information Technology from Biju Patnaik University of Technology, Odisha, and has submitted her PhD thesis. With over a decade of teaching and research experience at institutions including KIIT University and MIT World Peace University, her research focuses on artificial intelligence, data privacy, cloud computing, IoT, and mobile ad hoc networks. She has authored several international journal and conference publications, including works on AI in oncology, fake news detection, and IoT-based systems, and actively contributes to interdisciplinary research in privacy-preserving AI and healthcare data analytics.
Collection Overview
Artificial Intelligence (AI) is increasingly transforming healthcare through applications in medical imaging analysis, disease prediction, and clinical decision support systems. However, many high-performance AI models function as “black boxes,” making it difficult for clinicians to interpret how predictions are generated. In healthcare environments where transparency, reliability, and ethical accountability are critical, Explainable Artificial Intelligence (XAI) has emerged as an essential approach for improving trust and interpretability in AI-driven medical systems.
This Topical Collection aims to present experimental protocols and reproducible methodologies for implementing explainable AI techniques in healthcare applications. The collection will highlight step-by-step workflows demonstrating how to design, train, interpret, and evaluate AI models using real-world healthcare datasets. Example experimental protocols may include visualizing deep learning models for medical image diagnosis using Grad-CAM, implementing SHAP-based interpretation for disease risk prediction models, and developing interpretable AI pipelines for clinical decision-support systems.
By presenting practical implementation pipelines and validated experimental procedures, this collection will enable researchers and healthcare practitioners to develop transparent, trustworthy, and clinically reliable AI systems. The collection will help accelerate the adoption of interpretable AI technologies in healthcare research and clinical practice, ultimately supporting more informed and responsible medical decision-making.
Articles
A Reproducible Protocol for Developing and Evaluating Explainable Artificial Intelligence Frameworks in Healthcare Analytics
0 Views
•
2026
•
1Department of Computer Engineering, Vishwakarma Institute of Technology, 2Bharati Vidyapeeth's College of Engineering for Women, 3Faculty of Engineering at Sriracha, Kasetsart University, 4Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Vishwakarma Institute of Technology, 5President Maryam Abacha American University of Nigeria
Abstracts
Adversarially Robust Explainable Artificial Intelligence for Smart Healthcare Diagnostics Using Deep Learning and Explainability Techniques
Aarti Sardhara*1,
Saji George2,
Karthika P Devan3,
Mishel Elizabeth Jacob 4,
Payal Patial5,
Mohammad Israr*6
1Department of Computer Engineering Vishwakarma University, Pune, Maharashtra,India,
2Department of Business Administration, Rajagiri College of Social Sciences and Rajagiri Business School, Kochi, Kerala.,
3Department of Management , Vishwakarma University, Pune, Maharashtra, India.,
4Department of Business Administration , Baselius Autonomous College, Kottayam, Kerala.,
5Department of Electronics and Communication Engineering, University Institute of Engineering, Chandigarh University, Mohali, Punjab, INDIA, 140413,
6Maryam Abacha American University of Nigeria Hotoro GRA, Kano State, Federal Republic of Nigeria.
A Reproducible Machine Learning-Based Method for Secure Cloud Database Migration in Distributed Healthcare and Enterprise Environments
Priyanka Nalawade*1,
Prashant Kumbharkar2,
Pankaj Agarkar3,
Avanish Kumbharkar4,
Sudan Jha*5
11: School of Engineering, Ajeenkya D Y Patil University, Pune, MH, India 2: Department of Computer Engineering(Software Engineering), MIT Academy of Engineering, Alandi(D) Pune, MH, India,
2 School of Engineering, Ajeenkya D Y Patil University, Pune, MH, India,
3Ajeenkya D Y Patil SOE ,Lohegaon ,Pune, SPPU ,
4University of Sydney, Sydney, Australia.,
5Department of Computer Engineering, Kathmandu University, Kathmandu, Nepal
Multimodal Deep Learning Approach for Cardiovascular Risk Prediction Using Retinal Imaging and Clinical Data
Gokapay Dilip Kumar1,
Nagulmeera Sayyed2,
Shaik Shabana3,
Mohammed Altaf Ahmed*4,
Qutubuddin Mohammed5,
Sultan Alqahtani6
1Department of AI&ML, Sri Chaitanya Institute of Technology and Research (SCIT), Ponnekal, Khammam, Telangana, India,
2Computer Science and Engineering ,R.V.R. &.J.C. College of Engineering, Chowdavaram, Guntur, India,
3Computer Science and Engineering , R.V.R. &.J.C. College of Engineering, Chowdavaram, Guntur, India,
4Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University– Al-Kharj, Saudi Arabia,
5Department of Electrical Engineering, College of Engineering Wadi Addawasir, Prince Sattam bin Abdulaziz University, Wadi Addawasir, Saudi Arabia,
6Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
Design and Experimental Validation of Explainable AI Pipelines for Transparent Healthcare Analytics
Yashwant Dongre*1,
Deepali Godse2,
Prawit Chumchu*3,
Trupti Ghongade1
1Vishwakarma Institute of Technology, Pune, Maharashtra, India.,
2Bharati Vidyapeeth’s College of Engineering for Women, Pune, Maharashtra, India.,
3Kasetsart University, Thailand.
Artificial Intelligence in Healthcare: Applications, Benefits, and Challenges in the Indian Context
Bhakti Unhale*1
1MIT ADT University, Pune, India
Experimental Protocols for Developing and Validating Explainable AI Models in Secure Healthcare Diagnostics
Sonali Patil1,
Meghana Lokhande*2,
Shweta Koparde3,
Sonali Kothari4,
Sudan Jha*5
1Department of Computer Science and Engineering, Faculty of Science and Technology, Vishwakarma University, Pune,
2Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune,
3School of Engineering & Technology, DY Patil University, Ambi, Pune,
4Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune,
5Department of Computer Engineering, Kathmandu University, Kathmandu, Nepal
A Federated Multimodal Transformer Framework with Diffusion Augmentation and Graph-Based Personalized Drug Recommendation for Diabetes Intelligence
Netra S Patil*1,
Naveenkumar Jayakumar2,
Sushil Nepal*3
1Department of Computer Engineering,Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, Maharashtra, 411 043, India,
2 School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India,
3Department of Computer Science and Engineering, Kathmandu University, Kathmandu, Nepal.
SecureHealthKey+: Integrating Explainable TinyML Anomaly Detection into Lightweight Authentication Protocols in IoT-Enabled Healthcare Systems
Shital Pawar1,
Sonali Patil2,
Ashwini Sengar3,
Khushal Khairnar 4,
Sandip Thite4,
Mohammad Israr*5
1Bharati Vidyapeeth's College of Engineering for Women Pune,
2International Institute of Information Technology (I2IT) ,Pune,
3Vishwakarma University, Pune,
4MIT Academy of Engineering, Alandi ,
5Maryam Abacha American University of Nigeria Hotoro GRA, Kano State,Federal Republic of Nigeria
Experimental Protocol for Explainable Secure Federated Learning with CNN-Based Aggregation for Medical Image Classification
Neha Kudu1,
Vipul Dalal 2,
Rasika Ransing3,
Anant Nagesh Kaulage4,
Mohammad Israr*5,
Amol A Bhosle*6
1Assistant Professor Vidyalankar Institute of Technology, Mumbai,
2Dean & Professor, School of Engineering and Technology, Atharva University, Mumbai,
3Assistant Professor, Vidyalankar Institute of Technology, , Mumbai,
4Associate Professor, G H Raisoni College of Engineering and Management, Pune,
5President,Maryam Abacha American University of Nigeria Hotoro GRA, Kano State, Federal Republic of Nigeria,
6Associate Professor Department of Computer Science and Engineering School of Computing MIT Art, Design and Technology University, Pune
DCSC: An XAI 2.0 based Framework for Personalized Diabetes Complications Surveillance and Ethical Care Management
Rupali Ahuja*1
1Maitreyi College, University of Delhi
An Explainable Artificial Intelligence Protocol for Wearable Doppler Ultrasound Signal Analysis and Early Detection of Vascular Abnormalities
Sital Dash*1,
Kailas Patil*1,
Mohammad Israr*2
1Vishwakarma University, Pune, Maharashtra,
2President Maryam Abacha American University of Nigeria Hotoro GRA, Kano State, Federal Republic of Nigeria
An Adaptive Hybrid Feature Fusion Framework for Interpretable Brain Tumor Classification in MRI
Jayendra Jadhav*1
1Vishwakarma University, Pune
Experimental Protocol for Explainable Multimodal AI-Based Breast Abnormality Screening Using Thermal and Ultrasound Imaging
Sital Dash*1,
Kailas Patil*1
1Vishwakarma University, Pune, Maharashtra