Machine Learning Technology in Predictive Healthcare, 2nd Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 410

Special Issue Editors

Department of Public Health Science, Penn State College of Medicine, Hershey, PA, USA
Interests: machine learning; clinical informatics; healthcare innovation; EHR/EMR mining; natural language processing; complex diseases; outcome prediction; health disparity; machine learning-enabled decision support system; stroke; transient ischemic attack; cerebrovascular medicine
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Public Health Science, Penn State College of Medicine, Hershey, PA, USA
Interests: big data; data science; machine learning; artificial intelligence; deep learning; data visualization; anomaly detection; computer vision; federated machine learning

Special Issue Information

Dear Colleagues,

Machine learning, an artificial intelligence technique enabling computers to learn and adapt from experience without explicit programming, has considerably impacted the realms of medicine, health, and healthcare. Additionally, precision medicine, an approach that considers individual genetic, environmental, and lifestyle variations, has also gained prominence.

This Special Issue aims to focus on the convergence between machine learning approaches and precision medicine by providing a platform for researchers to share their knowledge and insights. We seek to feature papers that underscore how the use of machine learning can reduce disparity and improve outcomes for mainstream/minority patient populations in healthcare, addressing areas such as the development and application of machine learning algorithms, and methodologies for innovative healthcare and disease management, including drug discovery, disease diagnosis, patient stratification, clinical decision support, etc.

We look forward to your submissions, which we believe will be valuable in revolutionizing medical care and improving patient outcomes.

Dr. Vida Abedi
Dr. Alireza Vafaei Sadr
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly 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 2700 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

  • machine learning
  • artificial intelligence
  • precision medicine
  • precision health
  • predictive modeling
  • medical diagnosis
  • medical prognosis
  • healthcare disparity
  • healthcare innovation
  • EHR/EMR mining
  • smart healthcare systems
  • patient stratification

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.

Related Special Issue

Published Papers (1 paper)

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

Other

15 pages, 1364 KB  
Systematic Review
Translating AI to the Bedside with Physician Buy-In: Recommendations from a Meta-Analysis and Systematic Review of the Literature
by Grace Hwang, Seyyed Sina Hejazian, Alireza Vafaei Sadr, Jennifer K. Wagner, Pingxu Hao, Ajith Vemuri, Yuki Kawamura, Khalid Nawab, Shadi Hijjawi, Ramin Zand and Vida Abedi
Bioengineering 2025, 12(12), 1363; https://doi.org/10.3390/bioengineering12121363 - 16 Dec 2025
Viewed by 297
Abstract
Background: Artificial intelligence (AI) is increasingly being used in healthcare. Despite its promise, physicians and trainees remain cautiously optimistic. This systematic review and meta-analysis aimed to assess knowledge and attitudes toward AI and to provide recommendations for AI buy-in by physicians. Methods: Searches [...] Read more.
Background: Artificial intelligence (AI) is increasingly being used in healthcare. Despite its promise, physicians and trainees remain cautiously optimistic. This systematic review and meta-analysis aimed to assess knowledge and attitudes toward AI and to provide recommendations for AI buy-in by physicians. Methods: Searches of PubMed-OVID-IEEE-Scopus, and Web-of-Science for studies in 2013–2024 identified 11,437 records. One-hundred-and-fifteen met inclusion criteria. Fifty-three studies reported quantitative data on physicians’/trainees’ knowledge and were included in the meta-analysis. Results: Our meta-analysis estimated that only 19.6% of physicians and trainees have high overall AI knowledge, while 36.3% have low knowledge. Fifty-five studies evaluated the depth of AI knowledge. These studies consistently concluded that most physicians or trainees possess only moderate conceptual knowledge of AI, and their technical knowledge is usually limited. Qualitative evaluations also highlighted that a high level of conceptual AI knowledge is associated with greater receptiveness to AI implementation in medicine. We identified five major barriers to translating AI to the bedside with physician buy-in. Conclusion: Although physicians and trainees are generally receptive to AI, many barriers hinder adoption. To address them, we recommend establishing standardized AI education and workforce training, involving clinicians early in AI design, clarifying legal and regulatory issues, leveraging insights from clinical decision support system implementation to reduce workflow challenges, and integrating patient-centered communication principles to enhance trust and transparency. Full article
(This article belongs to the Special Issue Machine Learning Technology in Predictive Healthcare, 2nd Edition)
Show Figures

Figure 1

Back to TopTop