Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.6
Journals
Algorithms
Special Issues
Applications of Artificial Intelligence in Healthcare, Biomedicine and Medical Informatics
share announcement

Applications of Artificial Intelligence in Healthcare, Biomedicine and Medical Informatics

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 5942

Editors

Dr. Himanshu Buckchash

E-Mail Website
Guest Editor
Department of Science and Technology, IMC Krems University of Applied Sciences, 3500 Krems, Austria
Interests: self-supervised learning; responsible AI; generative AI; deep learning; AI in healthcare; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Deepak Dhungana

E-Mail Website
Guest Editor
Institute Digitalisation and Informatics, IMC University of Applied Sciences, 3500 Krems, Austria
Interests: factories of the future; Industry 4.0; product line engineering; data-driven business models; customization and personalization; self-adaptation systems; system of systems; digital ecosystems

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is rapidly transforming healthcare and biomedicine, offering unprecedented solutions to long-standing challenges. This includes accelerating drug discovery by identifying potential compounds, enhancing medical imaging for more precise diagnostics, and enabling personalized medicine through tailored treatments based on individual data. AI also improves medical robotics for diagnoses, automates routine tasks for clinicians, facilitates biomarker identification, and optimizes hospital operations. Despite these advances, significant hurdles remain, such as reliance on limited, biased, and unlabeled datasets, critical privacy concerns leading to the creation of data silos, and the “black box” nature of many AI models hindering explainability and trust in clinical decision-making. Furthermore, evolving regulatory frameworks and issues of accountability and liability in AI applications require careful consideration. This Special Issue invites research addressing these complex issues (including AI applications) through theoretical advancements and practical implementations in areas focusing on, but not limited to, medical imaging, video medical analysis, robotics in biomedicine, non-invasive treatment, explainability of AI diagnoses, diverse AI algorithms in healthcare, AI-assisted drug discovery, life science research, data privacy, privacy-preserving data processing, and federated machine learning for healthcare.

Dr. Himanshu Buckchash
Dr. Deepak Dhungana
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. Algorithms 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 1800 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

  • artificial intelligence in healthcare
  • biomedical engineering applications
  • medical informatics
  • deep learning for diagnostics
  • medical image analysis
  • explainable AI
  • drug discovery
  • personalized medicine
  • hospital management and patient engagement
  • telemedicine and wearable technology
  • data privacy in healthcare
  • federated learning in healthcare and biomedicine
  • unlabeled medical data challenges
  • algorithmic bias in medical data
  • clinical decision support
  • medical robotics
  • remote patient monitoring
  • biomedical signal processing

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 (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

20 pages, 613 KB  
Article
Automated Electronic Health Record Phenotyping of Acute and Subacute Subdural Hematoma
by Gregory B. Hooke, Haoqi Sun, Catherine Clive, Spencer Boris, Niels Turley, Lydia Petersen, Jaden Searle, Bram Overmeer, Ali Han Yaramis, Karan Singh, Arjun Singh, Daniel Sumsion, Aditya Gupta, Manohar Ghanta, Valdery F. Moura Junior, Marta Fernandes, Katie L. Stone, Dennis Hwang, Lynn Marie Trotti, Gari D. Clifford, Umakanth Katwa, Shibani S. Mukerji, Sahar F. Zafar, Robert J. Thomas and M. Brandon Westoveradd Show full author list remove Hide full author list
Algorithms 2026, 19(3), 239; https://doi.org/10.3390/a19030239 - 23 Mar 2026
Viewed by 905
Abstract
Accurate identification of acute and subacute subdural hematoma (acute/subacute SDH) is critical for improved patient outcomes. However, large-scale research is hindered by unreliable identification methods in electronic health records (EHRs). Current approaches relying on International Classification of Diseases (ICD) codes lack specificity and [...] Read more.
Accurate identification of acute and subacute subdural hematoma (acute/subacute SDH) is critical for improved patient outcomes. However, large-scale research is hindered by unreliable identification methods in electronic health records (EHRs). Current approaches relying on International Classification of Diseases (ICD) codes lack specificity and cannot distinguish acute, subacute, and chronic cases; manual chart review is too labor-intensive to scale. We developed an automated phenotyping algorithm using structured data and unstructured clinical notes for high-accuracy retrospective identification of acute/subacute SDH. We analyzed 2999 records from two hospitals, including ICD-positive and ICD-negative acute/subacute SDH cases verified by manual chart review. Features for model training included ICD codes, Current Procedural Terminology (CPT) codes, and clinical note keywords. Logistic regression and random forest models were trained using cross-validation and evaluated using AUROC and AUPRC. External validation involved training on one hospital and testing on the other. The random forest keywords-only model performed best, achieving an AUROC of 0.985 (95% CI: 0.980–0.990) and AUPRC of 0.944 (95% CI: 0.923–0.962) on the test set. External validation demonstrated strong AUROCs of 0.965 and 0.971 and AUPRCs of 0.831 and 0.840. The overall error rate was <1%. This model provides a scalable, highly accurate approach to acute/subacute SDH detection in EHR research. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare, Biomedicine and Medical Informatics)
►▼ Show Figures

Figure 1

24 pages, 2924 KB  
Article
Privacy-Preserving Synthetic Histopathological Single-to-Multimodal Data Generation from Brain MRI Using Transfer Learning
by Mahendra Kumar Gourisaria, Abhijit Roy, Amitkumar V. Jha, Bhargav Appasani, Saurabh Bilgaiyan, Alin Gheorghita Mazare and Nicu Bizon
Algorithms 2026, 19(2), 112; https://doi.org/10.3390/a19020112 - 1 Feb 2026
Viewed by 580
Abstract
Brain cancer detection is dependent on multiple diagnostic techniques. Histopathological diagnosis, although the most effective, requires the extraction of cancer cells, which is very risky and painful for a patient. Another popular noninvasive image-based diagnosis technique is magnetic resonance imaging (MRI). Brain diagnosis [...] Read more.
Brain cancer detection is dependent on multiple diagnostic techniques. Histopathological diagnosis, although the most effective, requires the extraction of cancer cells, which is very risky and painful for a patient. Another popular noninvasive image-based diagnosis technique is magnetic resonance imaging (MRI). Brain diagnosis data based on MRI scans are highly sensitive and private. This study proposes a single-to-multimodal transformation technique that generates synthetic histopathological data from expert-labelled brain MRI datasets using transfer learning techniques. Furthermore, to preserve a patient’s privacy, an encryption module is used to encrypt the MRI image data and the respective histopathological notations. The Kruskal–Wallis statistical test is also used to analyze the radiogemomics dataset. The trained module is also encrypted, only to be accessed by authorized medical personnel. The transfer learning modules (CNN-based deep learning model, ViT, Resnet101, and YOLOv8) are used here and achieved 99.60% accuracy. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare, Biomedicine and Medical Informatics)
►▼ Show Figures

Figure 1

Review

Jump to: Research

32 pages, 3198 KB  
Review
Explainability in Deep Learning in Healthcare and Medicine: Panacea or Pandora’s Box? A Systemic View
by Wullianallur Raghupathi
Algorithms 2026, 19(1), 63; https://doi.org/10.3390/a19010063 - 12 Jan 2026
Viewed by 1196
Abstract
Explainability in deep learning (XDL) for healthcare is increasingly portrayed as essential for addressing the “black box” problem in clinical artificial intelligence. However, this universal transparency mandate may create unintended consequences, including cognitive overload, spurious confidence, and workflow disruption. This paper examines a [...] Read more.
Explainability in deep learning (XDL) for healthcare is increasingly portrayed as essential for addressing the “black box” problem in clinical artificial intelligence. However, this universal transparency mandate may create unintended consequences, including cognitive overload, spurious confidence, and workflow disruption. This paper examines a fundamental question: Is explainability a panacea that resolves AI’s trust deficit, or a Pandora’s box that introduces new risks? Drawing on general systems theory we demonstrate that the answer is profoundly context dependent. Through systemic analysis of current XDL methods, Saliency Maps, LIME, SHAP, and attention mechanisms, we reveal systematic disconnects between technical transparency and clinical utility. This paper argues that XDL is a context-dependent systemic property rather than a universal requirement. It functions as a panacea when proportionately applied to high-stakes reasoning tasks (cancer treatment planning, complex diagnosis) within integrated socio-technical architectures. Conversely, it becomes a Pandora’s box when superficially imposed on routine operational functions (scheduling, preprocessing) or time-critical emergencies (e.g., cardiac arrest) where comprehensive explanation delays lifesaving intervention. The paper proposes a risk-stratified framework recognizing that a specific subset of healthcare AI applications—those involving high-stakes clinical reasoning—require comprehensive explainability, while other applications benefit from calibrated transparency appropriate to their clinical context. We conclude that explainability is neither a cure-all nor an inevitable harm, but rather a dynamic equilibrium requiring continuous rebalancing across technical, cognitive, and organizational dimensions. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare, Biomedicine and Medical Informatics)
►▼ Show Figures

Figure 1

34 pages, 831 KB  
Review
Recent Trends in Machine Learning for Healthcare Big Data Applications: Review of Velocity and Volume Challenges
by Doaa Yaseen Khudhur, Abdul Samad Shibghatullah, Khalid Shaker, Aliza Abdul Latif and Zakaria Che Muda
Algorithms 2025, 18(12), 772; https://doi.org/10.3390/a18120772 - 8 Dec 2025
Viewed by 2671
Abstract
The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training [...] Read more.
The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare, Biomedicine and Medical Informatics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop