A fundamental change is being sparked in the field of healthcare informatics as a result of the confluence of Scalable Computing and advanced Artificial Intelligence (AI) techniques. This special issue dives into the fundamental role that these technologies play in altering the field of medical diagnostics, and it does so in a comprehensive manner. The combination of cutting- edge AI techniques, such as deep learning and machine learning, with Scalable Computing, which encompasses neural networks, genetic algorithms, and fuzzy logic, is giving rise to innovations with far-reaching consequences for the medical field. This special issue investigates several facets of this transition, such as the creation of data-driven decision support tools, intelligent diagnostic systems, and healthcare solutions that center on the patient. This volume comprises papers from some of the most prominent scholars and practitioners in the field. These contributions shed light on innovative approaches, case studies, and practical implementations that are influencing the trajectory of medical diagnosis.

In this special issue, a comprehensive review of the applications of advanced artificial intelligence and scalable computing is used to investigate the vast potential for enhanced precision, effectiveness, and availability in healthcare diagnostics. The investigation is carried out using advanced AI. Our goal is to provide researchers in the healthcare business, such as data scientists and research analyst, with the information they need to properly traverse the ever-changing terrain of modern health informatics and fully comprehend the benefits it offers. The promotion of enhanced awareness of the impact of these technologies will be the means through which this goal will be accomplished.

Not only do the research articles that are presented in this special issue acknowledge previous developments, but they also provide the groundwork for a more intelligent and imaginative approach to healthcare diagnostics. As the field of healthcare informatics develops, this collection of papers offers a glimpse into the revolutionary implications that Scalable Computing and Advanced AI will have in ushering in an era of individualized and accurate healthcare.

Recommended topics (but not limited to)

The main goal of this special issue is to create a forum for researchers and scientists to exchange and analyse the latest advancements, trends, and concerns, as well as the practical difficulties and solutions implemented in the area of Scalable Computing and Advanced AI in Medical Diagnosis.

• Scalable Computing and AI Models in Early Detection of Chronic Diseases
• Medical Image Analysis through Deep Learning and Scalable Computing Approaches
• Natural Language Processing in Transforming Medical Diagnosis:
• Privacy-preserving techniques in health care industry
• Deep learning/Machine Learning for advanced healthcare systems
• Personalized Medical Diagnosis using Scalable Computing and AI
• AI for Real-time Remote Health Monitoring and Diagnosis
• Adaptive AI in Health Informatics:
• Data protection mechanism in scalable computing
• AI and Scalable Computing in Rare Disease Diagnosis:
• AI-Enhanced Telemedicine for Rapid Diagnosis
• Secure data sharing in Health informatics
• HCI with Natural language processing systems
• Human Emotion and sentimental analysis

Important dates

Submission deadline: 30 November, 2024

Authors notification:  29 February, 2025

Revision submission: 30 April, 2025

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

During submission please select a Special Issue that you want to submit to and provide this information in the Comments for the Editor field.

Guest Editors

Lead: Dr. Neelakandan Subramani, Research Professor , HCT Lab, Gyeongsang National University, Republic of Korea, 52828, email: neelakandans@gnu.ac.kr

Neelakandan Subramani is a Research Professor in the HCT Lab at Gyeongsang National University, Republic of Korea. He served as an Associate Professor in the Department of Computer Science and Engineering at R.M.K Engineering College, India. He earned his Bachelor of Engineering in Computer Science & Engineering and M.E in Computer Science and Engineering from Anna University, Chennai. He obtained his Ph.D. in Information and Communication Engineering from Anna University. His research interests include Data Science, Machine Learning, Big Data, and Cloud Computing. With more than 80 research papers published, he has received several awards for his contributions. Neelakandan Subramani serves as an academic editor and reviewer for several international journals. Additionally, he is a Senior IEEE member, a Life member of ISTE, a Member of IAENG, and also a Member of IEI. He has delivered numerous keynote addresses and lectures in various institutions and training programs.

Sumarga Kumar Sah Tyagi, Associate Professor, Department of Computer Science and Engineering,University of South Florida,Tampa, FL, USA, email: sksahtyagi@usf.edu

Sumarga Kumar Sah Tyagi (Member, IEEE) received the M.Sc. degree in computer science from South Asian University (established by SAARC countries), New Delhi, India, in 2014, and the Ph.D. degree in wireless communication from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, in 2019. He is currently an Associate Professor in university of South Florida, USA. He has authored or coauthored two books, more than 15 SCI publications. His research interests include cutting-edge AI enabled communications and computing technology. Dr. Tyagi was the recipient of several prestigious scholarships/awards from different governments, including "CAS-TWAS President’s Fellowship-2014" for the duration of Ph.D. from government of China and Italy, and "SAARC Silver-Jubilee Scholarship" during 2012– 2014 from Indian government for the duration of studying the master’s degree. He is also the Lead Guest Editor of several peer-reviewed journals from the IEEE, ACM, Elsevier, and Springer.

ARI HAPPONEN, Adjunct & Assoc. Prof. and a project manager/principal investigator at LUT School of Engineering Science in Lappeenranta University of Technology, Finland, email: ari.happonen@lut.fi

Assoc. Prof. Ari Happonen is a native-born University-Industry collaborator specializing in Digitalization capabilities, Hackathons & Code Camps, ICT for Sustainability, and modern waste reduction methodologies. Dr. Happonen has a Master’s degree (2005) from Lappeenranta University of Technology from Information Technology. His research includes practical and efficient industrial-related applied collaborative activities in B2B and B2C contexts, both nationally and internationally. He contributes to software engineering, ICT & sustainability, Artificial intelligence and robotization solutions utilization, hackathon & education-related research areas. During his career, he has authored over 150 scientific publications in various topics of software engineering & digitalization and sustainability & education development. Dr. Happonen has given guidance for over 170 theses so far. He also actively participates in education and teaching development efforts with 20+ years of experience, also recently working as head of the bachelor program in Software Engineering at LUT School of Engineering Sciences, in which time he was one of the key persons for overseeing units' successful ASIIN accreditation process. He works as an intermediary in multiple research projects between universities, innovative front-line companies & governmental and municipal offices. Assoc. Prof. Happonen is the current representative for LUT Software Engineering unit in the LUT School of Engineering Science academic council, a Steering group member in Robocamp project, and LUT task group member in European Universities Linking Society and Technology alliance (EULiST). ORCID: 0000-0003-0744- 1776, Scopus Author ID: 35585858000, ResearcherID: H-2697-2018

Dr. Keoy Kay Hooi, Associate Professor, at UCSI Graduate Business School , UCSI University, Malaysia, email: keoykh@ucsiuniversity.edu.my

Keoy Kay Hooi is currently working as Associate Professor, at UCSI Graduate Business School , UCSI University. He completed PhD degree at Sheffield Hallam University, United Kingdom in 2006. He completed his BSc. in Computer Science with Cum Laude Award from University of Campbell, USA. His current research interests include machine Learning, operation management technology & management information systems. He published more than 50 papers in international journals and conferences, and he is an reviewer for several international journals. He has delivered numerous keynote addresses and lectures in various institutions and development programs.