Mathematical and Algorithmic Foundations for Smart Healthcare
A Special Issue of Mathematics (ISSN 2227-7390) belonging to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 May 2027 | Viewed by 48
Editors
Interests: IoT design; artificial intelligence; cloud computing; software design
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; AIoT applications; software engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Traditional medical diagnosis largely relies on physician experience, often resulting in time-consuming processes or requiring extensive trial and error to determine the cause of an illness. In recent years, the integration of Artificial Intelligence (AI) and advanced data analytics with healthcare has significantly reduced diagnostic latency and enhanced clinical precision. Furthermore, AI serves as an invaluable learning tool for junior physicians, accelerating their accumulation of clinical experience.
However, modern healthcare environments generate massive, highly complex, and heterogeneous datasets—ranging from imaging and dynamic videos to real-time IoT biosensors. Effectively processing, analyzing, and fusing these diverse data streams presents significant challenges in algorithmic efficiency and system architecture. This Special Issue aims to showcase cutting-edge research in smart healthcare, medical IoT, and sports medicine, highlighting innovative AI algorithms, edge computing solutions, and multimodal data fusion strategies to address these challenges.
We look forward to receiving your valuable submissions.
Topics include, but are not limited to, the following:
- Medical image and video processing, analysis, and diagnostic algorithms;
- AI applications in sports medicine, motion analytics, kinematics, and dynamic rehabilitation;
- Physiological sensing, wearable biosensors, and Medical Internet of Things (MIoT) architectures;
- Deep learning for pharmacology, drug interaction modeling, and medication data analytics;
- Clinical Decision Support Systems (CDSSs) and Electronic Health Record (EHR) analytics;
- Multimodal data fusion for medical, physiological, and audio–visual health data;
- Edge AI computing and real-time processing for healthcare environments;
- Retrieval-Augmented Generation (RAG) and generative models for medical knowledge management.
Dr. Tse-Chuan Hsu
Dr. Chih-Hung Chang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- mathematical modeling
- optimization algorithms
- multimodal data fusion
- tensor decomposition
- time-series analysis
- predictive modeling
- distributed optimization
- graph neural networks
- topological data analysis
- stochastic modeling
- Bayesian inference
- sparse representation
- compressed sensing
- physiological signal processing
- dynamical systems
- control theory
- retrieval-augmented generation
- data-driven modeling
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