The Role of Trustworthy Artificial Intelligence in Health Care
JMIR Medical Informatics (Editor-in-Chief: Christian Lovis, MD, MPH, FACMI) and guest editors Dr Dilbag Singh (Gwangju Institute of Science and Technology), Dr Vijay Kumar (NIT Hamirpur), and Dr Robertas Damaševičius (Silesian University of Technology) invite submissions that review the current state of artificial intelligence (AI) use in health care and predict future trends and developments in producing explainable platforms for the upcoming themed issue titled “The Role of Trustworthy Artificial Intelligence in Health Care.”
AI allows us to replicate certain human capabilities. It is now causing a fundamental change in health care, which can be attributed to the growing accessibility of health information and, thus, the rapid advances in its analysis. Just as the industrial revolution transformed manufacturing, health care systems may be approaching a paradigm shift.
The “intelligence” of any platform is derived from the data. Data may be limited by bias, errors, or insufficient attributes if it were not collected with the study purpose in mind. To be effective, any application in health care must be based on trustworthy data. As we move into a new AI-driven age, understanding how a data set was collected has become as important as the accuracy of the data itself.
In addition to the logistic constraints of collecting data and maintaining privacy, data comparability has become increasingly important. Researchers must often combine multiple databases in varied formats. The task of unifying different data sets such that they are comparable and communicable remains challenging.
A large volume of data has already been gathered within health care; however, AI and machine learning models commonly focus on large datasets and conventional techniques of analysis such that many topics remain unexplored. In the typical “black box” approach, the resulting AI platforms may not represent which data is most relevant to understanding a disease, thus necessitating human interpretation. Explainable AI produces solutions that can be immediately understood by humans. When constructing reliable AI in health care, explainable modeling may prove preferable to post hoc explanations.
While AI regulation remains a topic for discussion, existing regulatory frameworks (i.e., frameworks for drug safety) may provide an example of how trust is built over time. There are several potential ways to regulate AI; for example, we can define a comprehensive list of verified criteria designed to ensure that an AI model is constitutional, ethical, and resilient.
In this theme issue, researchers and practitioners are invited to advise on how to develop trustworthy AI systems for the health sector and contribute to the field of trustworthy AI. We will combine various perspectives on design, implementation, effectiveness, and regulation.
The topics of interest include:
Ethics and AI in health care
Explainable AI at the service of health: a potential revolution
High-performance medicine: the convergence of humans and AI
AI in electronic health records data: opportunities and challenges
AI and medical imaging analysis
Deontological aspects of the use of big data and AI in biomedical research
Edge-based frameworks to support efficient applications to health care
AI models using real-world electronic health care
AI in medicine: making the interpretability of neural networks a toolbox for the practitioner
Article submission deadline: October 15, 2022 (Closed for submissions)
Author’s notification: December 15, 2022
Revised papers due: January 15, 2023
Publication: April 15, 2023
Submitted papers should report new and original results that are unpublished elsewhere. Manuscripts should be prepared according to the guidelines outlined here.
To submit an article to this JMI theme issue, please visit https://medinform.jmir.org/, click on “Submit Article” on the banner, and select the “Journal Section” entitled “Theme Issue: The Role of Trustworthy Artificial Intelligence in Health Care”
All submitted manuscripts will undergo a full peer-review process, consistent with the standard editorial criteria of JMIR. Accepted papers will be published in JMIR Medical Informatics. All papers will appear together in an e-collection (theme issue) guest-edited by the academics listed below. Papers rejected for this theme issue may still be considered for regular issues. Submissions not reviewed or accepted for publication in this JMIR theme issue may be offered cascading peer review or transfer to other JMIR journals, according to standard JMIR Publications policies.
For this theme issue, the article processing fee (APF) is discounted by 20%. (APF for JMI will be US $2300.00. This is a discount of US $460.)
Dr Dilbag Singh
Research Professor, School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, South Korea, Email: Dilbagsingh@gist.ac.kr;firstname.lastname@example.org, Profile, Google Scholar
Dr Dilbag Singh completed a PhD in Computer Science and Engineering, which inspired him to work in a challenging environment, wherein his skills and academic experience could be applied in practice. In turn, this allowed him to enrich his knowledge and to gain experience in the fields of data science, machine learning, and computer vision. He has published many papers in highly reputed journals. He has been conferred with internal and external grants from different international sponsors and has led several units and committees at both national and international levels.
Dr Robertas Damaševičius
Dr Robertas Damaševičius received his PhD in Informatics Engineering from Kaunas University of Technology (Lithuania) in 2005. His research interests include sustainable software engineering, human computer interfaces, assisted living, data mining, and machine learning. He has authored over 300 research papers, as well as a monograph published by Springer. He is also the Editor-in-Chief of the academic journal Information Technology and Control and has been the Guest Editor of several invited issues of international journals (Biomed Research International, Computational Intelligence and Neuroscience, Journal of Healthcare Engineering, IEEE Access, and Electronics).
Dr Vijay Kumar
Dr Vijay Kumar is Assistant Professor in Computer Science and Engineering Department, NIT Hamirpur. He received his PhD from NIT Kurukshetra. Previously, he received MTech and BTech degrees from GJUS&T, Hisar, and M.M. Engineering College, Mullana, respectively. He has 4 years of teaching and research experience at Thapar Institute of Engineering & Technology, Patiala. Prior to that, he has 8 years of teaching experience in various reputed institutes. He has completed two DST SERB–sponsored research projects. Presently, he is working on a CSIR-sponsored research project. He has published more than 100 research papers in international journals and conferences, and he has authored book chapters for international reputable publishers. He has also supervised many PhD and MTech theses on metaheuristics, image mining, and data clustering. He is the reviewer of several reputed SCI journals. Dr Kumar is also a member of ACM, CSI, International Association of Engineers, International Association of Computer Science, and Information Technology (Singapore). His current research interests include soft computing, data mining, deep learning, steganography, and pattern recognition.