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Advances in Artificial Intelligence for Biomedicine
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Advances in Artificial Intelligence for Biomedicine

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1661

Editor

Dr. Anca Udristoiu

E-Mail Website
Guest Editor
Faculty of Automation, Computers and Electronics, University of Craiova, 200440 Craiova, Romania
Interests: artificial intelligence; computer vision; software engineering; algorithm design; big data; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has made significant contributions to the field of biomedicine, revolutionizing biomedical research. The integration of AI in biomedicine has great promises for enhancing diagnostics, drug discovery, personalized medicine, and overall patient care. Despite its numerous benefits, there are challenges and ethical considerations regarding the application of AI in biomedicine, namely ensuring data privacy, addressing ethical concerns, maintaining the transparency and interpretability of AI algorithms, and integrating AI technologies into existing healthcare systems.

This Special Issue will aim to explore the potential of using AI technologies in biomedicine to improve diagnostics, accelerate drug discovery, enable personalized medicine, and optimize healthcare operations. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Medical imaging analysis;
  • AI for diagnosis prognosis;
  • AI for genomics and personalized medicine;
  • AI for drug discovery and development;
  • Natural Language Processing (NLP) in Electronic Health Record (EHR) analysis;
  • AI in medical robotics and surgery;
  • AI for remote patient monitoring.

Dr. Anca Udristoiu
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

  • deep learning
  • machine learning
  • natural language processing
  • diagnosis prognosis
  • drug discovery
  • personalized medicine

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Published Papers (3 papers)

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Research

16 pages, 277 KB  
Article
Physicians Retain Moral Responsibility but Endorse Institutional Co-Responsibility in AI-Assisted Decisions: An Exploratory Vignette Study
by Florian Berghea, Alexandra Ligia Dinca, Diana Mihaela Ciuc and Gabi Valeriu Dinca
Appl. Sci. 2026, 16(16), 8338; https://doi.org/10.3390/app16168338 - 21 Aug 2026
Viewed by 139
Abstract
Background: Artificial intelligence (AI) systems, including large language models, are increasingly used in clinical practice, whether consulted informally by clinicians or introduced by employers into decision workflows. It remains unclear how physicians attribute moral responsibility when a decision follows an AI recommendation and [...] Read more.
Background: Artificial intelligence (AI) systems, including large language models, are increasingly used in clinical practice, whether consulted informally by clinicians or introduced by employers into decision workflows. It remains unclear how physicians attribute moral responsibility when a decision follows an AI recommendation and whether that attribution varies with the type of decision at stake. Methods: We conducted a cross-sectional, within-subject vignette survey of physicians in Romania. Each respondent rated the same three scenarios—urgent clinical, elective clinical, and administrative—in which a physician followed an AI recommendation under two extenuating institutional constraints. Five-point Likert items addressed the mitigation of blame by circumstances, physician responsibility despite the AI recommendation, and institutional co-responsibility. Analyses were non-parametric, with corrections for multiple testing. Results: Among 72 physicians from 17 specialties, respondents endorsed full personal responsibility in every scenario, including the administrative one, with no significant difference between scenarios. They rejected extenuating circumstances as mitigating in both clinical scenarios but were divided about them in the administrative scenario, which had the largest effect. Institutional co-responsibility was endorsed alongside personal responsibility rather than in place of it, and the two attributions were largely uncorrelated. No demographic association survived correction, although the study was not powered to detect small-effect sizes. Conclusions: Physicians treated AI as an instrument rather than a bearer of responsibility, which is unsurprising. The substantive findings lie elsewhere: personal responsibility was retained across all decision contexts, while what varied was the admissibility of institutional constraints as excuses and the emphasis placed on the institution’s share. Because respondents did not treat responsibility as a fixed quantity to be divided, the pattern is consistent with distributed-responsibility accounts rather than with a responsibility gap, though attitudinal data cannot adjudicate between normative accounts. The findings are exploratory and require confirmation in larger, more representative samples. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedicine)
18 pages, 2164 KB  
Article
Machine Learning-Based Severity Classification in Decompensated Liver Cirrhosis: Incremental Value of Oxidative Stress Biomarkers for Predicting Ascites and Hepatic Encephalopathy
by Vlad Pădureanu, Florentina Dumitrescu, Rodica Pădureanu, Dragoș Forțofoiu, Dalia Dop, Vlad Dumitru Baleanu, Roni Octavian Damian, Răzvan Radu Mititelu, Lidia Boldeanu and Virginia Maria Rădulescu
Appl. Sci. 2026, 16(12), 6097; https://doi.org/10.3390/app16126097 - 16 Jun 2026
Viewed by 332
Abstract
Oxidative stress biomarkers are elevated in liver cirrhosis, but their clinical utility for severity staging and complication prediction remains uncertain. This retrospective single-centre study enrolled 90 patients with decompensated cirrhosis (Child–Pugh classes B and C) to evaluate serum malondialdehyde (MDA) and 8-isoprostane (8-isoPGF2α) [...] Read more.
Oxidative stress biomarkers are elevated in liver cirrhosis, but their clinical utility for severity staging and complication prediction remains uncertain. This retrospective single-centre study enrolled 90 patients with decompensated cirrhosis (Child–Pugh classes B and C) to evaluate serum malondialdehyde (MDA) and 8-isoprostane (8-isoPGF2α) as predictors of Child–Pugh severity, severe ascites, and severe hepatic encephalopathy, and to quantify their incremental value within supervised machine learning models. Four algorithms—logistic regression, Random Forest, Gradient Boosting, and Support Vector Machine—were evaluated using stratified 10-fold cross-validation; logistic regression models with and without oxidative stress biomarkers were compared for the prediction of ascites and encephalopathy. Routine biochemical parameters effectively discriminated Child–Pugh class B from C, with machine learning models achieving AUC-ROC values of 0.921–0.972. Neither MDA nor 8-isoPGF2α differed between Child–Pugh classes or across ascites categories, and both failed to improve ascites prediction (ΔAUC = −0.015). For severe hepatic encephalopathy, the extended model showed modest but consistent improvements in accuracy (+3.4 percentage points), sensitivity (+6.4%), and model fit, suggesting an outcome-specific complementary role consistent with the established involvement of lipid peroxidation in ammonia neurotoxicity. These findings support the use of machine learning for automated cirrhosis severity classification and indicate that oxidative stress biomarkers hold selective relevance for hepatic encephalopathy rather than global disease staging. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedicine)
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22 pages, 1429 KB  
Article
GenAI-Powered Framework for Reliable Sentiment Labeling in Drug Safety Monitoring
by Eleftherios Vouzis and Ilias Maglogiannis
Appl. Sci. 2026, 16(8), 3942; https://doi.org/10.3390/app16083942 - 18 Apr 2026
Viewed by 578
Abstract
The analysis of medical data presents an opportunity for healthcare systems to support decision-making and improve patient outcomes. In this context, the automated analysis of user-generated drug reviews offers a promising approach for monitoring medication safety, understanding patient experiences, and detecting potential adverse [...] Read more.
The analysis of medical data presents an opportunity for healthcare systems to support decision-making and improve patient outcomes. In this context, the automated analysis of user-generated drug reviews offers a promising approach for monitoring medication safety, understanding patient experiences, and detecting potential adverse effects in real time. This study advances sentiment analyses for pharmacovigilance by introducing a data-centric framework that incorporates a GenAI-powered labeling system for reliable and interpretable data annotation. A corpus of 213,869 user-generated drug reviews was processed through a hybrid labeling pipeline that reconciles user ratings, lexicon-based polarity, zero-shot transformer predictions, and GPT-5.2 as a fallback mechanism. This strategy enables the resolution of sentiment ambiguity, particularly the frequent misalignment between user-assigned ratings and underlying textual sentiment, by leveraging contextual understanding rather than relying solely on numerical scores. Drug review representations are enhanced using the Qwen3-Embedding-0.6B model, allowing improved capture of semantic nuances. Evaluated through 10-fold stratified cross-validation, the proposed labeling framework combined with a Random Forest classifier achieves a classification accuracy of 96.45%, with per-class analysis confirming consistent performance across all sentiment categories. Cross-source validation on an independent drug review dataset of 4091 reviews and a threshold sensitivity analysis further support the robustness and generalizability of the proposed approach. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Biomedicine)
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