applsci-logo

Journal Browser

Journal Browser

Feature Review Papers in Biomedical Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 30 June 2027 | Viewed by 361

Editor


E-Mail Website
Guest Editor
1. Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
2. Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
Interests: machine learning; biomedical signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to gather high-quality review articles highlighting recent advances, emerging technologies, and interdisciplinary developments in biomedical engineering. We welcome contributions across all areas of the Biomedical Engineering Section of Applied Sciences, including, but not limited to, the following: biomedical devices, biophotonics, bioinformatics of diseases, bioinstrumentation, biomedical sensors, biomedical robotics, artificial organs, health monitoring and wearable systems, biomedical signal and image processing, brain research, artificial intelligence in healthcare, and 3D bioprinting.

Dr. Ana María Torres Aranda
Guest Editor

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. Applied Sciences 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 2400 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

  • biomedical engineering
  • medical devices
  • biophotonics
  • bioinformatics
  • biomedical sensors
  • biomedical signal processing
  • medical imaging
  • artificial intelligence in healthcare
  • wearable health systems
  • 3D bioprinting
  • neuroengineering
  • biomedical robotics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Review

26 pages, 1558 KB  
Review
From Nano-Enabled Multimodal Biosensing to Health Digital Twins: A Scoping Review and Evidence-Gated Roadmap
by Leonel Adalberto Vasquez-Cevallos, Paul E. D. Soto-Rodriguez and Pedro A. Salazar-Carballo
Appl. Sci. 2026, 16(17), 8391; https://doi.org/10.3390/app16178391 (registering DOI) - 23 Aug 2026
Abstract
Rapid advances in nanomaterials, wearable biosensors, multimodal acquisition, and artificial intelligence have enabled increasingly integrated health-monitoring systems, yet their progression toward health digital twins remains unclear. We conducted a protocol-driven scoping review of original studies combining nano-enabled multianalyte or multimodal sensing, AI-supported analysis, [...] Read more.
Rapid advances in nanomaterials, wearable biosensors, multimodal acquisition, and artificial intelligence have enabled increasingly integrated health-monitoring systems, yet their progression toward health digital twins remains unclear. We conducted a protocol-driven scoping review of original studies combining nano-enabled multianalyte or multimodal sensing, AI-supported analysis, and health applications. PubMed/MEDLINE, Scopus, Web of Science Core Collection, and IEEE Xplore were searched using a publication cutoff of 10 July 2026; platform execution was completed on 13 July 2026. Two reviewers independently screened 528 unique records and assessed 20 full-text reports. A 79-item charting form was jointly verified for 12 included studies. Nine studies reported reference-method or matrix-relevant analytical validation, nine included human-sample or on-body evidence, and six acquired longitudinal or continuous data. Under the author-proposed, corpus-specific functional classification, six systems were L0, five L1, and one L2; none of the 12 met the L3 or L4 functional criteria. No included study combined dynamic individual-state assimilation with prospective prediction or simulation, and none reported external-site validation or formal predictive uncertainty quantification. Because eligibility required nano-enablement, multiple analytes or channels, AI integration, and selected clinical domains, these findings do not estimate the prevalence or maturity of health digital twins in the wider literature. Progress requires longitudinal multimodal data, validated state updating, external generalization, confidence-aware AI, and prospective evaluation of governed feedback. Full article
(This article belongs to the Special Issue Feature Review Papers in Biomedical Engineering)
Show Figures

Figure 1

Back to TopTop