2nd Edition: Technological Innovation in Clinical Healthcare and Health Management

Dear Colleagues,

The International Journal of Environmental Research and Public Health (IJERPH) organized a Special Issue, entitled “Technological Innovation in Clinical Healthcare and Health Management: A Global Perspective”, which concluded in March 2021. The Special Issue published ten highly intriguing and influential manuscripts. In view of the new technological innovations in this field, IJERPH has organized a second edition of this Special Issue. In this new edition, we are particularly interested in manuscripts on artificial intelligence (AI), machine learning (ML), and related applications developed to improve environmental health, increase the efficiency of health policy and management, and create new conceptual frameworks and analytic methodologies related to AI/ML, broadly defined. IJERPH is an interdisciplinary, peer-reviewed, open access journal, with focuses in environmental research and public health. More information about the journal can be found on https://www.mdpi.com/journal/ijerph.

The first edition of this Special Issue was a great success. The articles we published covered a broad range of health technologies, and all the research was conducted at great depth. Authors examined a variety of technologies, ranging from the integration of fuzzy rule in smart medical beds (Costello et al. 2021) [1], mobile apps for measuring craniovertebral an (Galleogo-Izquierdo et al. 2020) [2], to craniofacial imaging (Denadai et al. 2019) [3]. The studies covered technological acceptance and perception among both patients and providers, such as Chinese mobile medical treatment patients (Li 2020) [4] and medical doctors who provided online services (Chen et al. 2020) [5]. Other studies applied sophisticated methods to evaluate perceived differences between online vs. in-person health services (Le et al. 2019) [6], skin conductance response (SCR) among smartphone users (Hsieh at al. 2020) [7], methods of utilizing Google Trends to forecast national suicide rates (Barros et al. 2019) [8], and patients’ concerns over electromagnetic fields (EMFs) and radio frequency (RF) (Lopez et al. 2019) [9]. The Special Issue also published a comprehensive review of surgical interventions for inferior turbinate hypertrophy (Abdullah & Singh 2021) [10].   

Building on the first edition, we aim to publish new research on artificial intelligence (AI) and its related concepts, such as machine learning (ML), internet of things (IoT) and big data, in the second edition. AI and related technological developments offer real opportunities to study such questions as: How can AI improve healthcare effectively and efficiently? How can researchers utilize data collected from AI-enabled devices to optimize health management? How is ML different from older analytic techniques and can ML generate new theories and conceptual frameworks? What are the ethical challenges associated with AI-ML, and what solutions can researchers formulate to address these challenges?

More specifically, we welcome manuscripts that address (but are not limited to) the following issues to offer new insights about AI-ML-related innovations:

1. Empirical studies (case studies, experiments, and surveys) applied to any subject related to any AI-ML applications (broadly defined) in high- and/or low-resource environments;
2. Descriptions of development, implementation, validation, or the evaluation of AI-ML- related innovations;
3. Data analytics research on the relationships between AI-ML and the performance of health organizations in the global context;
4. Theory development for AI-ML and related digital health devices;
5. Secondary and tertiary studies such as bibliometric studies, systematic literature reviews, mapping studies, scoping reviews, and meta-analyses in any area related to AI-ML-innovations.

If you have any questions, please feel free to contact me before submitting your manuscript to IJERPH. I look forward to your contributions. Thank you very much for your attention. 

References

1. Costello, F.J.; Kim, M.G.; Kim, C.; Lee, K.C. Exploring a Fuzzy Rule Inferred ConvLSTM for Discovering and Adjusting the Optimal Posture of Patients with a Smart Medical Bed. Int. J. Environ. Res. Public Health 2021, 18, 6341. https://doi.org/10.3390/ijerph18126341.
2. Gallego-Izquierdo, T.; Arroba-Díaz, E.; García-Ascoz, G.; Val-Cano, M.D.A.; Pecos-Martin, D.; Cano-de-la-Cuerda, R. C of A mobile application to measure the craniovertebral angle a validation and reliability study. Int. J. Environ. Res. Public Health 2020, 17, 6521. https://doi.org/10.3390/ijerph17186521.
3. Denadai, R.; Tu, J.C.Y.; Tsai, Y.R.; Tsai, Y.N.; Hsieh, E.Y.J.; Pai, B.C.; Chen, C.-H.; Kane, A.; Lo, L.-J.; Chou, P.Y. Workflow and strategies for recruitment and retention in longitudinal 3D craniofacial imaging study. Int. J. Environ. Res. Public Health 2019, 16, 4438. https://doi.org/10.3390/ijerph16224438.
4. Li, Q. Healthcare at your fingertips: The acceptance and adoption of mobile medical treatment services among Chinese users. Int. J. Environ. Res. Public Health 2020, 17, 6895. https://doi.org/10.3390/ijerph17186895.
5. Chen, J.; Lan, Y.C.; Chang, Y.W.; Chang, P.Y. Exploring doctors’ willingness to provide online counseling services: The roles of motivations and costs. Int. J. Environ. Res. Public Health 2020, 17, 110. https://doi.org/10.3390/ijerph17010110.
6. Le, W.; Chang, P.Y.; Chang, Y.W.; Chen, J. Why do patients move from online health platforms to hospitals? The perspectives of fairness theory and brand extension theory. Int. J. Environ. Res. Public Health 2019, 16, 3755. https://doi.org/10.3390/ijerph16193755.
7. Hsieh, H.F.; Hsu, H.T.; Lin, P.C.; Yang, Y.J.; Huang, Y.T.; Ko, C.H.; Wang, H.H. The effect of age, gender, and job on skin conductance response among smartphone users who are prohibited from using their smartphone. Int. J. Environ. Res. Public Health 2020, 17, 2313. https://doi.org/10.3390/ijerph17072313.
8. Barros, J.M.; Melia, R.; Francis, K.; Bogue, J.; O’Sullivan, M.; Young, K.; Bernert, B.A.; Rebholz-Schuhmann, D.; Duggan, J. The validity of Google Trends search volumes for behavioral forecasting of national suicide rates in Ireland. Int. J. Environ. Res. Public Health 2019, 16, 3201. https://doi.org/10.3390/ijerph16173201.
9. García-Minguillán López, O.; Jiménez Valbuena, A.; Maestú Unturbe, C. Significant Cellular Viability Dependence on Time Exposition at ELF-EMF and RF-EMF In Vitro Studies. Int. J. Environ. Res. Public Health 2019, 16, 2085. https://doi.org/10.3390/ijerph16122085.
10. Abdullah, B.; Singh, S. Surgical Interventions for Inferior Turbinate Hypertrophy: A Comprehensive Review of Current Techniques and Technologies. Int. J. Environ. Res. Public Health 2021, 18, 3441. https://doi.org/10.3390/ijerph18073441.

Dr. Ricky C. Leung
Guest Editor

Manuscript Submission Information

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• Artificial intelligence (AI)
• Machine learning (ML)
• supervised and unsupervised learning
• deep learning
• neural networks
• Internet of Things (IoT)
• big data
• ethical challenges

• Technological Innovation in Clinical Healthcare and Health Management in International Journal of Environmental Research and Public Health (10 articles)