International Workshop on AI Applications in
Telemedicine and Digital Health
July 9th, 2024 – Salt Lake City, UT, USA
TeleHealth-AI
Welcome to TeleHealth-AI, a pioneering event dedicated to exploring the innovative intersection of artificial intelligence, telemedicine, and digital health.
The workshop will provide insights into the latest advancements in AI applications in telemedicine and digital health.
Overview
This workshop aims to bring together researchers, practitioners, and healthcare professionals to discuss the transformative power of AI in telehealth delivery.
Our goals are to share the latest advancements, foster collaboration, and identify new research directions in AI applications within telemedicine and digital health.
Format
The workshop will comprise two sessions divided by a 15-minute networking coffee break. At the end of the second session, a 15-minute discussion will be led by the workshop co-chairs to summarize the workshop findings, to have presenters to answer any additional questions, and to plan future directions.
Call for submissions
Publication
A subset of articles will be considered for submission to Journal of Healthcare Informatics (impact factor 5.9) and Diagnostics Journal (impact factor 3.6). Based on the Workshop Proceedings, a special issue will be published by MDPI.
An expanded commentary based on the workshop proceedings has been commissioned by the Lancet eBioMedicine (impact factor 10.2).
Key dates
(*) All times are Mountain Time (MT). The Mountain Time Zone refers to time zone which observes time where seven hours are subtracted from GMT (UTC/GMT -7).
Review process
The submissions will be reviewed on a rolling basis. Review results are expected to be communicated within 10 days after submission.
Topics
- ML models utilizing serial patient-reported data to predict health risks or to forecast disease trajectory.
- Natural Language Processing to analyze patient feedback on telehealth services to improve patient experience.
- Use of Convolutional Neural Networks (CNNs), transfer learning or other deep learning techniques in tele-radiology, tele-dermatology, and other fields for diagnosing diseases from remotely transmitted medical images such as X-rays, MRIs, and CT scans.
- AI techniques to improve visualization and analytics of low-resolution images obtained via remote
cameras. - Deep learning techniques such as Recurrent Neural Networks (RNNs), including LSTM (Long Short-Term Memory) networks for analyzing time-series data from wearable devices, monitoring patient
vitals, and predicting health events. - Use of various ML techniques to identify unusual patterns in remote patient monitoring data that may indicate a medical condition or the need for intervention (anomaly detection).
- Optimizing personalized treatment recommendations based on continuous feedback from wearable health devices (reinforcement learning).
- Telehealth demand forecasting using time-series forecasting methods like ARIMA or Prophet to predict patient demand for telehealth services, aiding in resource allocation.
- Operations research and optimization algorithms for scheduling and managing virtual appointments and healthcare delivery logistics.
- Deep learning for analyzing complex multimodal datasets, including genomics, electronic health records, combined with patient-generated data to tailor treatments to individual patients.
- Harnessing Large language models (LLMs) to serve as virtual health assistants.
- ML algorithms for analyzing speech and language patterns to detect signs of mental health issues, such as depression or anxiety, in teletherapy sessions.
- Federated learning supporting the development of predictive models across multiple institutions without sharing patient data, preserving privacy while leveraging EHRs for insights of large scale telehealth delivery networks
Submission guidelines
TeleHealth-AI features 3 types of submissions (full papers, short papers and posters), each one should be submitted as a PDF file using our Easychair page and following the further instructions:
1. Full papers:
- Main paper up to 10 pages, including references; appendix up to 10 pages, maximun of 20 pages in the final file.
- Papers should be formatted according to Springer’s LNCS format. Authors can include an appendix that does not count towards the page limit, however there is no guarantee that reviewers will read it.
- Authors should consult Springer’s authors’ guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers.
2. Short papers:
- Main paper up to 5 pages, including references.
- Should include abstract, introduction, methods, results, discussion, conclusions and references.
- Arial or Times New Roman size 11 are recommended fonts for submissions.
- Figure and/or tables can be included, the number and position of these are on the discretion of authors.
3. Posters:
- 1 to 2 pages consisting of a structured abstract
- The structured abstract comprises background, methods, results, discussion and references.
- Arial or Times New Roman size 11 are recommended fonts for submissions.
- Figure and/or tables can be included in the structured abstract, the number and position of these are on the discretion of authors.
Accepted structured abstracts will be presented at the poster sessions, and must follows the further:
- Posters should not exceed 48 inches wide by 48 inches tall.
- Presenters must provide their own poster printed on paper or cloth.
- Should you need assistance with directions for printing a poster in Salt Lake City, please email cmcfadden@conferences.utah.edu
- Boards on easels with push pins or binder clips will be provided.
Organization
Workshop co-chairs
Professor and Vice-Chair for Clinical Data Science and Telemedicine Informatics at the University of Utah
Professor and Vice Chair of Clinical Informatics at the University of Utah
Director of the Telemedicine Directorate at the Ministry of Health of Peru and Head of the Telehealth Unit at UNMSM
Scientific Paper Program Committee
- Yi Zhou, PhD, Assistant Professor at the Department of Electrical and Computer Engineering at the University of Utah, UT, USA.
- Stefan Escobar-Agreda, MD, Research Associate, Telehealth Unit, Universidad Nacional Mayor de San Marcos (UNMSM), Lima, Peru.
- Fatemeh Shah-Mohammadi, PhD, Assistant Professor at the Department of Biomedical Informatics at the University of Utah, UT, USA.
- Yue Zhang, PhD, Research Associate Professor, Division of Epidemiology, University of Utah School of Medicine, UT, USA.
